2

Motor Learning and
Performance
From Principles to Application
Fifth Edition
Richard A. Schmidt
Timothy D. Lee
Human Kinetics
3

Library of Congress Cataloging-in-Publication Data
Schmidt, Richard A., 1941- author.
Motor learning and performance : from principles to application / Richard A. Schmidt, Timothy D. Lee. — Fifth
edition.
p. ; cm.
Includes bibliographical references and index.
I. Lee, Timothy Donald, 1955- author. II. Title.
[DNLM: 1. Learning. 2. Motor Activity. 3. Kinesthesis. 4. Psychomotor Performance. BF 295]
BF295
152.3’34–dc23
2013014793
ISBN-10: 1-4504-4361-3 (print)
ISBN-13: 978-1-4504-4361-6 (print)
Copyright © 2014 by Richard A. Schmidt and Timothy D. Lee
Copyright © 2008, 2004, 2000 by Richard A. Schmidt and Craig A. Wrisberg
Copyright © 1991 by Richard A. Schmidt
All rights reserved. Except for use in a review, the reproduction or utilization of this work in any form or by any
electronic, mechanical, or other means, now known or hereafter invented, including xerography, photocopying,
and recording, and in any information storage and retrieval system, is forbidden without the written permission of
the publisher.
Permission notices for material reprinted in this book from other sources can be found on page xvii.
The web addresses cited in this text were current as of June 19, 2013, unless otherwise noted.
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Check Out the Web Study Guide!
You will notice a reference throughout this version of Motor Learning and
Performance, Fifth Edition to a web study guide. This resource is
available to supplement your e-book.
The web study guide offers interactive activities to reinforce key concepts
and principles-to-application exercises that allow you to apply concepts to
real-world scenarios.
Follow these steps to purchase access to the web study guide:
1. Visit http://tinyurl.com/BuySchmidt5EWebStudyGuide.
2. Click the Add to Cart button and complete the purchase process.
3. After you have successfully completed your purchase, visit the book’s
website at www.HumanKinetics.com/MotorLearningAndPerformance.
4. Click the fifth edition link next to the corresponding fifth edition book
cover.
5. Click the Sign In link on the left or top of the page and enter the e-mail
address and password that you used during the purchase process. Once
you sign in, your online product will appear in the Ancillary Items box.
Click on the title of the web study guide to access it.
6. Once purchased, a link to your product will permanently appear in the
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follow the link!
Click the Need Help? button on the textbook’s website if you need
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http://www.HumanKinetics.com/MotorLearningAndPerformance

Dedication
Jack A. Adams (1922-2010) was a giant in motor learning research. His
passing marks a sad personal loss for us as well as a huge professional loss
to motor learning research across the world. This book is dedicated to
Jack’s memory in appreciation for all he taught us.
7

Contents
Dedication
Preface
Student and Instructor Resources
Acknowledgments
Credits
Chapter 1: Introduction to Motor Learning and Performance
Why Study Motor Skills?
The Science of Motor Learning and Performance
Defining Skills
Components of Skills
Classifying Skills
Understanding Performance and Learning
Summary
Part I: Principles of Human Skilled Performance
Chapter 2: Processing Information and Making Decisions
The Information-Processing Approach
Reaction Time and Decision Making
Memory Systems
Summary
Chapter 3: Attention and Performance
What Is Attention?
Limitations in Stimulus Identification
Limitations in Response Selection
Limitations in Movement Programming
Decision Making Under Stress
Summary
Chapter 4: Sensory Contributions to Skilled Performance
8

Sources of Sensory Information
Processing Sensory Information
Principles of Visual Control
Audition and Motor Control
Summary
Chapter 5: Motor Programs
Motor Program Theory
Evidence for Motor Programs
Motor Programs and the Conceptual Model
Problems in Motor Program Theory: The Novelty and Storage Problems
Generalized Motor Program Theory
Summary
Chapter 6: Principles of Speed, Accuracy, and Coordination
Speed–Accuracy Trade-Offs
Sources of Error in Rapid Movements
Exceptions to the Speed–Accuracy Trade-Off
Analyzing a Rapid Movement: Baseball Batting
Accuracy in Coordinated Actions
Summary
Chapter 7: Individual Differences
The Study of Individual Differences
Abilities Versus Skills
Is There a General Motor Ability?
Abilities and the Production of Skills
Prediction and Selection Based on Ability
Summary
Part II: Principles of Skill Learning
Chapter 8: Introduction to Motor Learning
Motor Learning Defined
How Is Motor Learning Measured?
Distinguishing Learning From Performance
Transfer of Learning
Summary
9

Chapter 9: Skill Acquisition, Retention, and Transfer
Skill Acquisition
Skill Retention
Skill Transfer
Summary
Chapter 10: Organizing and Scheduling Practice
Off-Task Practice Considerations
Organizing Practice and Rest
Variable Versus Constant Practice
Blocked Versus Random Practice
Summary
Chapter 11: Augmented Feedback
Feedback Classifications
Functions of Augmented Feedback
How Much Feedback to Give
When to Give Feedback
Summary
Glossary
References
About the Authors
10

Preface
Most of us feel tremendous excitement, pleasure, and perhaps envy when we
watch a close race, match, or performance, focusing on the complex, well-
controlled skills evidenced by the players or musicians. In these situations,
we marvel at those who must succeed in executing their skill “on the
spot”—at how the person with high-level skills is able to excel, sometimes
under extreme “pressure” to do so.
This book was written for people who appreciate high-level skilled activity
and for those who would like to learn more about how such incredible
performances occur. Thus, readers in fields related directly to kinesiology
and physical education (such as teaching and coaching) will benefit from the
knowledge provided here. But the material extends far beyond these fields
and should be relevant for those who study rehabilitation in physical and
occupational therapy, as well as for instructors and facilitators of many
other areas in which motor skills play an important role, such as music,
ergonomics, and the military. The text is intended for beginners in the study
of skill and requires little knowledge of physiology, psychology, or
statistical methodologies.
The level of analysis of the text focuses on motor behavior—the overt,
observable production of skilled movements. Of course, there are many
scientific areas or fields of study involved in the understanding of this overt
skilled behavior. Any skill is the outcome of processes studied in many
different fields, such as neurology, anatomy, biomechanics, biochemistry,
and social and experimental psychology; and this text could have focused on
any number of these fundamental fields. But the focus of the text is broader
than the fundamental fields that support it. The focus is behavioral, with the
major emphasis on humans performing skills of various kinds. To be sure,
we will talk about these other levels of analysis from time to time
throughout the book in an attempt to explain what processes or events occur
to support these high-level skills. Therefore this text should be appropriate
for courses in elementary motor learning and motor performance in a
relatively wide group of scientific areas.
Throughout the text, we construct a conceptual model of human
performance. The term “model” is used in a variety of ways in many
11

branches of science, and models are found frequently. A model typically
consists of a system of parts that are familiar to us; when assembled in a
certain way, these parts mimic certain aspects of the system we are trying to
understand. One example is the pump-and-pipe model of our circulatory
system, in which the heart is represented by a pump and the arteries and
veins are pipes of various diameters and lengths. One could actually
construct the model (although some models are purely conceptual); such a
model could be used in classroom demonstrations or “experiments” on the
effects of blood pressure on capillaries of the “hand.”
Our first goal in writing this text was to build a strong, general, conceptual
understanding (an overview) of skills. We believe that instructors, coaches,
therapists, and trainers, as well as others dealing with the learning or
teaching of skills, will profit greatly from such a high-level conceptual
understanding of skilled behavior. In striving toward this goal, we have
adopted (assumed) the idea that skills can be understood, for the most part,
through the use of concepts concerning information and its processing. We
set about to build a conceptual model that would capture (or explain) many
of the intricacies of skilled motor performance. We begin this process by
considering the human as a very simple input–output system; then gradually,
as we introduce new topics in the text, we expand the model by adding these
new concepts. Gradually, by building on knowledge and concepts presented
in earlier parts of the text, we add increasing complexity to the conceptual
model. Simply presenting the finished conceptual model would make it very
difficult for students to understand, and we hope that the systematic process
of constructing the model, assembled with parts as they are presented in the
text, forms a logical basis for increasing the model’s complexity. This
construction process should make the final version of the model maximally
understandable.
Our second goal was to organize the book in the best way to aid student
understanding based on our many years of teaching experience. The text is
divided into two parts. After the introduction to the study of motor skills in
chapter 1, part I, examines how the motor system works by investigating the
major principles of human performance and progressively developing a
conceptual model of human actions. The focus is mainly on human
performance as based on an information-processing perspective; but motor
learning cannot be ignored, so it is mentioned briefly in various places.
Chapter 2 discusses the nature of information processing, decision making,
and movement planning. Chapter 3 considers the concepts of attention and
12

memory. Chapter 4 concerns the information received from various sensory
sources that is relevant to movement. Chapter 5 examines the processes
underlying the production of movement, with particular attention to the role
of motor programs. Chapter 6 considers the basic principles of performance
that form the “building blocks” of skilled performance—analogous to the
fundamental laws of physics. Finally, in chapter 7, the concern shifts to the
differences in movement abilities among people and how these differences
allow the prediction of success in new situations; differences among
components in the conceptual model help in understanding these differences
among people. On completion of part I, the student should have a reasonably
coherent view of the conceptual and functional properties of the motor
system. These principles seem appropriate for maximizing the performance
of already learned skills. Part II of the text uses the conceptual model to
impart an understanding of human motor learning processes. Much of this
discussion uses the terms and concepts introduced in part I. This method
works well in our own teaching, probably because motor learning is usually
inferred from changes in motor behavior; therefore, it is easy to discuss
these changes in terms of the behavioral principles from part I. In this
second part, chapter 8 treats some methodological problems unique to the
study of learning, such as how and when to measure performance, which
also have application to measuring performance in analogous teaching
situations. Chapter 9 considers broad issues of learning, retention, and
transfer, such as the important role of practice. Chapter 10 concerns the
issue of how and when to practice, dealing with the many factors that
instructors can control directly to make practice more effective. Finally,
chapter 11 deals with the critical topic of feedback, examining what kinds of
movement information students need for effective learning, when it should
be given, and so on. By the end of the text, readers will have a progressive
accumulation of knowledge that, in our experience, provides a consistent
view of how skills are performed and learned.
Many real-world examples of motor performance and learning principles
are discussed in the main body of the text. In addition, we’ve included
Focus on Application sections set off from the main textual materials.
Strategically located directly after pertinent discussions of principles, these
sections indicate applications to real-world teaching, coaching, or therapy.
We wanted to write a text that could be used by performers, teachers,
coaches, physical therapists, and other instructors in various fields to
enhance human performance in real-world settings. To meet this goal, we
have worked to focus the text on the topics most relevant to practical
13

application.
As a third goal, we wanted a presentation style that would be simple,
straightforward, and highly readable for those without extensive
backgrounds in the motor performance area. As a result, the main content
does not stress the research and data that contribute to our knowledge of
motor skill acquisition and performance. Important points are occasionally
illustrated by data from a critical experiment, but the emphasis is on an
integrated conceptual knowledge of how the motor system works and how it
learns. However, for those who desire a tighter link to the basic data, we
have included sections called Focus on Research, which are set off from the
main text and describe the important experiments and concepts in detail.
Finally, we demanded that the principles discussed should be faithful to the
empirical data and thought in the study area. From decades in doing basic
research in motor learning and motor performance, we have developed what
we believe to be defensible, coherent, personal viewpoints (conceptual
models, if you will) about how skills are performed and learned, and our
aim was to present this model to the reader to facilitate understanding. Our
viewpoints are based on a large literature of theoretical ideas and empirical
data, together with much thought about competing ideas and apparently
contradictory research findings. We have tried to write from this
perspective as we would tell a story. Every part of the story can be
defended empirically, or it would not have been included. Our goal has
been to write “the truth,” at least as we currently understand it and as it can
be understood with the current level of knowledge. We have included a
brief section at the end of each chapter describing additional readings that
provide competing viewpoints and additional scientific justifications.
Students will find a range of learning aids within each chapter, including
chapter-opening outlines, objectives, and lists of key terms, as well as an
end-chapter summary of the activities in the accompanying web study guide
and “Check Your Understanding” and “Apply Your Knowledge” questions.
Instructors using this text in their courses will find a wealth of updated
ancillary materials at
www.HumanKinetics.com/MotorLearningAndPerformance, including a
presentation package and image bank, instructor guide, and test package.
This fifth edition of Motor Learning and Performance extends the
approach used in the previous four editions. As with the previous editions,
we have tried to integrate the latest new findings together with the research
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http://http://www.HumanKinetics.com/MotorLearningAndPerformance

and findings that have remained relevant for longer periods of time. But this
edition could also be considered quite different as well. In many ways, this
fifth edition returns to the approach adopted in the first edition, of providing
a theoretical and conceptual basis for motor performance and learning that
could be applied as broadly as possible. Since motor learning and
performance are probably the most widespread activities that humans from
all walks of life experience on a daily basis, our goal was to touch on as
many of these applications as possible. The generality and limitations of
these principles represent a core of human existence, and we hope that our
treatment of them in this book resonates well with each person who reads it.
Richard A. Schmidt
Human Performance Research
Marina del Rey, California
Timothy D. Lee
Department of Kinesiology
McMaster University, Hamilton, Ontario
15

Student and Instructor Resources
Student Resources
Students, visit the free web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance. The web study
guide has been fully revised for the fifth edition to offer a more focused and
interactive set of activities to aid learning. The activities in this study guide
will help you to assess and build your understanding of concepts from each
chapter of the text as you study.
In each chapter of the web study guide, you will be presented with a series
of two to four interactive activities that test your understanding of important
concepts. These include matching, multiple-choice, and diagram-based
activities. For each chapter, you will be also be presented with a
principles-to-application exercise that prompts you to take your knowledge
beyond the classroom by using principles of motor control and learning to
analyze an activity. There is no single right answer for the principles-to-
application problems, but it is important to provide evidence and reasoning
to support your ideas. Each principles-to-application exercise includes
sample student answers and critiques of those answers to guide you as you
develop your analysis. By completing the exercises included in this study
guide, you will build your knowledge of important concepts from the
textbook and learn to apply that knowledge to real-world situations.
Instructor Resources
The instructor guide, test package, chapter quizzes, presentation package,
and image bank are free to course adopters and are accessed at
www.HumanKinetics.com/MotorLearningAndPerformance.
Instructor Guide
The instructor guide includes chapter summary notes for preparing lectures
and ideas for presenting topics and engaging students in class discussions,
as well as practical laboratory activities.
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http://http://www.HumanKinetics.com/MotorLearningAndPerformance

Test Package
The test package includes more than 230 true-or-false, multiple-choice, fill-
in-the-blank, and short-answer questions that can be used to create exams.
The test package is available for download in Respondus and LMS formats
as well as in Rich Text Format (.rtf) for use with word processing software.
Chapter Quizzes
New for the fifth edition, these ready-to-use 10-question quizzes help assess
students’ comprehension of the most important concepts in each chapter.
Chapter quizzes can be imported into learning management systems or be
used in RTF format by instructors who prefer to offer a written quiz.
Presentation Package
The presentation package includes more than 230 PowerPoint text slides
that highlight material from the text for use in lectures and class discussions.
The slides can be used directly in PowerPoint or can be printed to make
transparencies or handouts for distribution to students. Instructors can easily
add, modify, and rearrange the order of the slides as well as search for
images based on key words.
Image Bank
The image bank, included with the presentation package, includes most of
the figures, content photos, and tables from the text, sorted by chapter, which
can be used to develop a customized presentation.
17

Acknowledgments
This edition of Motor Learning and Performance owes a debt of gratitude
to many people. It was Rainer Martens who first conceptualized the idea,
and his encouragement led to the publication of the first edition (Schmidt,
1991). Sincere thanks go to Craig Wrisberg, who coauthored the next three
editions (Schmidt & Wrisberg, 2000, 2004, 2008). Over the years the
authors have worked with many wonderful editors at Human Kinetics, who
made the sometimes tedious process much more enjoyable, for which we
are very grateful. For this edition we would especially like to thank Myles
Schrag and Kate Maurer for their efforts in seeing this project through to
completion. We also thank Liz Sanli for her hard work on the book’s
ancillaries and Jasmine Caveness, Marilyn Lomeli, and Dianne Hopkins for
their contributions to the task of copyediting and several other efforts. And
lastly, we thank our wives, Gwen Gordon and Laurie Wishart, for their
understanding and support of the work that went into not only producing this
book, but all of our various endeavors.
18

Credits
Figures
Figure 2.6 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics). 65; Data from Merkel 1885.
Figure 2.7 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics). 65; Data from Merkel 1885.
Figure 2.8 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics). 70.
Figure 2.9 Reprinted, by permission, from J.A. Adams and S. Dijkstra,
1966, “Short-term memory for motor responses,” Journal of
Experimental Psychology 71: 317.
Figure 3.3 Reprinted from D.J. Simons and C.F. Chabis, 1999, “Gorillas in
our midst: Sustained inattentional blindness for dynamic events,”
Perception 28: 1059-1074. By permission of D.J. Simons and C.F.
Chabis.
Figure 3.5 Part a reprinted, by permission, from R.A. Schmidt and T.D.
Lee, 2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics), 108; part b reprinted, by permission,
from R.A. Schmidt and T.D. Lee, 2011, Motor control and learning: A
behavioral emphasis, 5th ed. (Champaign, IL: Human Kinetics), 110;
Data from Davis 1959.
Figure 3.7 Reprinted from M.I. Posner and S.W. Keele, 1969, Attentional
demands of movement. In Proceedings of the 16th Congress of applied
physiology (Amsterdam, Amsterdam: Swets and Zeitlinger). By
permission of M.I. Posner.
Figure 3.8 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
19

2011, Motor control and learning: A behavioral emphasis, 5th edition.
(Champaign, IL: Human Kinetics). Data from Weinberg and Ragan 1978.
Figure 4.7 Reprinted, by permission, from D.N. Lee and E. Aronson, 1974,
“Visual proprioceptive control of standing in human infants,” Perception
& Psychophysics 15: 529-532.
Figure 4.11 Reprinted, by permission, from T.J. Ayres, R.A. Schmidt et al.,
1995, Visibility and judgment in car-truck night accidents. In Safety
engineering and risk analysis–1995, edited by D.W. Pratt (New York:
The American Society of Mechanical Engineers), 43-50.
Figure 5.3 Reprinted with permission from Research Quarterly for
Exercise and Sport, Vol.24, 22-32, Copyright 1953 by the American
Alliance for Health, Physical Education, Recreation and Dance, 1900
Association Drive, Reston, VA 20191.
Figure 5.4 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th edition.
(Champaign, IL: Human Kinetics), 183.
Figure 5.5 Part a reprinted, by permission, from R.A. Schmidt and T.D.
Lee, 2011, Motor control and learning: A behavioral emphasis, 5th
edition. (Champaign, IL: Human Kinetics), 195; part b reprinted, by
permission, from R.A. Schmidt and T.D. Lee, 2011, Motor control and
learning: A behavioral emphasis, 5th edition. (Champaign, IL: Human
Kinetics), 195; Data from Slater-Hammel 1960.
Figure 5.6 Reprinted from W.J. Wadman, 1979, “Control of fast goal-
directed arm movements,” Journal of Human Movement Studies 5: 10.
By permission of W.J. Wadman.
Figure 5.8 Adapted from T.R. Armstrong, 1970, Training for the
production of memorized movement patterns: Technical report no. 26
(Ann Arbor, MI: University of Michigan, Human Performance Center),
35. By permission of the Department of Psychology, University of
Michigan.
Figure 5.9a and b Reprinted, by permission, from R.A. Schmidt and T.D.
Lee, 2011, Motor control and learning: A behavioral emphasis, 5th
edition. (Champaign, IL: Human Kinetics), 212.
20

Figure 5.10a and b Reprinted, by permission, from D.C. Shapiro et al.,
1981, “Evidence for generalized motor programs using gait-pattern
analysis,” Journal of Motor Behavior 13: 38.
Figure 5.11 Adapted, by permission, from J.M. Hollerbach, 1978, A study
of human motor control through analysis and synthesis of handwriting.
Doctoral dissertation, (Cambridge, MA: Massachusetts Institute of
Technology).
Figure 5.12 Reprinted from M.H. Raibert, 1977, Motor control and
learning by the state-space model: Technical report no. A1-TR-439
(Cambridge, MA: Artificial Intelligence Laboratory, Massachusetts
Institute of Technology), 50. By permission of M.H. Raibert.
Figure 6.1 Adapted from Categories of human learning, A.W. Melton
(Ed.), P.M. Fitts, Perceptual-motor skills learning, categories of human
learning pg. 258.
Figure 6.2 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics), 226. Data from Fitts 1954.
Figure 6.3a and b Adapted from P.M. Fitts, 1954, “The information
capacity of the human motor system in controlling the amplitude of
movement,” Journal of Experimental Psychology 47: 381-391.
Figure 6.4 Reprinted, by permission, from R.A. Schmidt et al., 1979,
“Motor-output variability: A theory for the accuracy of rapid motor acts,”
Psychological Review 86: 425. Copyright © 1979 by the American
Psychological Association.
Figure 6.5 Reprinted, by permission, from R.A. Schmidt et al., 1979,
“Motor-output variability: A theory for the accuracy of rapid motor acts,”
Psychological Review 86: 425. Copyright © 1979 by the American
Psychological Association.
Figure 6.8 Reprinted, by permission, from R.A. Schmidt and D.E.
Sherwood, 1982, “An inverted-U relation between spatial error and force
requirements in rapid limb movements: Further evidence for the impulse-
variability model,” Journal of Experimental Psychology: Human
Perception and Performance 8: 165. Copyright © 1982 by the American
21

Psychological Association.
Figure 6.9 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics), 238.
Figure 6.12 Reprinted, by permission, from P.A. Bender, 1987, Extended
practice and patterns of bimanual interference. Unpublished doctoral
dissertation (Los Angeles, CA: University of Southern California).
Figure 6.13a and b Reprinted, by permission, from T.D. Lee et al., 2008,
“Do expert golfers really keep their heads still while putting?” Annual
Review of Golf Coaching 2: 135-143.
Figure 6.14 Reprinted from Physics Letters A, Vol.118, J.A.S. Kelso, J.P.
Scholz, and G. Schöner, “Nonequilibrium phase transitions in
coordinated biological motion: Critical fluctuations,” pg. 281, copyright
1986, with kind permission of Elsevier.
Table 7.2 by permission, from J.N. Drowatzky and F.C. Zuccato, 1967,
“Interrelationships between selected measures of static and dynamic
balance,” Research Quarterly 38: 509-510.
Figure 7.4 Reprinted, by permission, from E.A. Fleishman and W.E.
Hempel, 1955, “The relation between abilities and improvement with
practice in a visual discrimination task,” Journal of Experimental
Psychology 49: 301-312. Copyright © 1955 by the American
Psychological Association.
Figure 8.2 Reprinted, by permission, from J.A. Adams, 1952, “Warm up
decrement in performance on the pursuit-rotor,” American Journal of
Psychology 65(3): 404-414.
Figure 8.3 Reprinted, by permission, from C.J. Winstein and R.A. Schmidt,
1990, “Reduced frequency of knowledge of results enhances motor skill
learning,” Journal of Experimental Psychology: Learning, Memory and
Cognition 16: 677-691. Copyright © 1990 by the American
Psychological Association.
Figure 8.4 Adapted, by permission, from H.P. Bahrick, P.M. Fitts, and G.E.
Briggs, 1957, “Learning curves—facts or artifacts?” Psychological
22

Bulletin 54: 256- 268. Copyright © 1957 by the American Psychological
Association.
Figure 9.3 Adapted, by permission, from R.A. Schmidt and T.D. Lee, 2011,
Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics), 451; Adapted from MacKay, 1976,
personal communication.
Figure 9.4 Reprinted, by permission, from E. Neumann and R.B. Ammons,
1957, “Acquisition and long term retention of a simple serial perception
motor skill,” Journal of Experimental Psychology 53: 160. Copyright ©
2011 by the American Psychological Association.
Figure 9.5 Reprinted from E.A. Fleishman and J.F. Parker, 1962, “Factors
in the retention and relearning of perceptual motor skill,” Journal of
Experimental Psychology 64: 218. Copyright © 1962 by the American
Psychological Association.
Figure 10.1 Adapted, by permission, from B.A. Boyce, 1992, “Effects of
assigned versus participant-set goals on skill acquisition and retention of
a selected shooting task,” Journal of Teaching in Physical Education
11(2): 227.
Figure 10.2 Reprinted, by permission, from R. Lewthwaite and G. Wulf,
2010, “Social-comparative feedback affects motor skill learning,”
Quarterly Journal of Experimental Psychology 63: 738-749.
Figure 10.3 Reprinted, by permission, from G. Wulf, 2003, “Attentional
focus on supra-postural tasks affects balance learning,”Quarterly
Journal of Experimental Psychology 56A: 1191-1211.
Figure 10.4 Reprinted, by permission, from D.M. Ste-Marie et al., 2012,
“Observation interventions for motor skill learning and performance: An
applied model for the use of observation,” International Review of Sport
and Exercise Psychology 5(2): 145-176.
Figure 10.6 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics), 365. Data from Baddeley and
Longman 1978.
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Figure 10.7 Reprinted from L.E. Bourne and E.J. Archer, 1956, “Time
continuously on target as a function of distribution of practice,” Journal
of Experimental Psychology 51: 27.
Figure 10.10 Reprinted, by permission, from K.M. Keetch, R.A. Schmidt,
T.D. Lee, and D.E. Young, 2005, “Especial skills: Their emergence with
massive amounts of practice,” Journal of Experimental Psychology:
Human Perception and Performance 31: 970-978. Copyright © 2005 by
the American Psychological Association.
Figure 10.11 Adapted, by permission, from J.B. Shea and R.L. Morgan,
1979, “Contextual interference effects on the acquisition, retention, and
transfer of a motor skill,” Journal of Experimental Psychology: Human
Learning and Memory 5: 179-187. Copyright © 1979 by the American
Psychological Association.
Figure 10.12 Reprinted with permission from Research Quarterly for
Exercise and Sport, vol. 68, pg. 103. Copyright 1997 by the American
Alliance for Health, Physical Education, Recreation and Dance, 1900
Association Drive, Reston, VA 20191.
Figure 11.3 Reprinted with permission from Research Quarterly for
Exercise and Sport, Vol. 78, pg. 43, Copyright 2007 by the American
Alliance for Health, Physical Education, Recreation and Dance, 1900
Association Drive, Reston, VA 20191.
Figure 11.4 Adapted by permission. ©Bob Scavetta. Any adaptation or
reproduction of the “1.5 Seconds of Thought” is forbidden without the
written permission of the copyright holder.
Figure 11.5 Reprinted, by permission, from R.A. Schmidt and T.D. Lee,
2011, Motor control and learning: A behavioral emphasis, 5th ed.
(Champaign, IL: Human Kinetics), 401; Data from Kernodle and Carlton
1992.
Figure 11.6 Reprinted, by permission, from C.J. Winstein and R.A.
Schmidt, 1990, “Reduced frequency of knowledge of results enhances
motor skill learning,” Journal of Experimental Psychology: Learning,
Memory, and Cognition 16: 910. Copyright © 1990 by the American
Psychological Association.
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Figure 11.8 Reprinted from Human Movement Science, Vol 9, R.A.
Schmidt, C. Lange, and D.E. Young, “Optimizing summary knowledge of
results for skill learning,” p. 334, copyright 1990, with permission of
Elsevier.
Figure 11.9 Reprinted, by permission, from W. Yao, M.G. Fischman, and
Y.T. Wang, 1994, “Motor skill acquisition and retention as a function of
average feedback, summary feedback, and performance variability,”
Journal of Motor Behavior 26: 273-282.
Figure 11.12 Adapted, by permission, from Schmidt and Lee, 2011, Motor
control and learning: A behavioral emphasis, 5th ed. (Champaign, IL:
Human Kinetics), 387. Adapted from Armstrong 1970.
Figure 11.13 Reprinted, by permission, from S.P. Swinnen et al., 1990,
“Information feedback for skill acquisition: instantaneous knowledge of
results degrades learning,” Journal of Experimental Psychology:
Learning, Memory, and Cognition 16: 712. Copyright © 2011 by the
American Psychological Association.
Figure 11.14 Reprinted from M.A. Guadagnoli and R.M. Kohl, 2001,
“Knowledge of results for motor learning: Relationship between error
estimation and knowledge of results frequency,” Journal of Motor
Behavior, 33: 217-224.
Photos
Dedication photo Courtesy of Jack Adams
Chapter 1 opening page © Zumapress/Icon SMI
Chapter 1 dec. photo 1 © Jerome Brunet/ZUMA Press
Focus on Research 1.1 Reprinted, by permission, from Weinberg, R.S., and
Gould, D. 12003, Foundations of Sport and Exercise Psychology,
Champaign, IL: Human Kinetics, 10.
Chapter 1 dec. photo 2 © Lee Mills/Action Images/Icon SMI
Chapter 1 dec. photo 3 © Human Kinetics/J. Wiseman, reefpix.org
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Chapter 2 opening page © Cliff Welch/Icon SMI
Chapter 2 dec. photo 3 © Chris Ison/PA Archive/Press Association Images
Figure 3.3 Reprinted from D.J. Simons and C.F. Chabis, 1999, “Gorillas in
our midst: Sustained inattentional blindness for dynamic events,”
_Perception_ 28: 1059-1074. By permission of D.J. Simons and C.F.
Chabis.
Figure 3.4 © Zuma Press/Icon SMI
Chapter 3 dec. photo 2 © Zuma Press/Icon SMI
Chapter 3 dec. photo 3 © Jim West/Age Fotostock
Chapter 4 dec. photo 2 © Photo courtesy of Philip de Vries
Figure 4.7 Reprinted from D.N. Lee and E. Aronson, 1974, “Visual
proprioceptive control of standing in human infants,” _Perception &
Psychophysics_ 15: 529-532. By permission of D.N. Lee.
Chapter 4 dec. photo 3 © Ed Bock/Lithium/age fotostock
Chapter 5 opening page © Tonyi Mateos/Age Fotostock
Chapter 5 dec. photo 2 © Zumapress/Icon SMI
Chapter 6 opening page © STOCK4B/Age Fotostock
Chapter 6 dec. photo 4 © Quim Roser/Age Fotostock
Chapter 7 dec. photo 1 © Zumapress/Icon SMI
Focus on Application 7.1 © Everett Collection Inc./age footstock (BABE)
Chapter 7 dec. photo 2 © Javier Larrea/Age Fotostock
Chapter 8 opening page © A. Farnsworth/Age Fotostock
Chapter 8 dec. photo 3 © Martin Rickett/PA Archive/Press Association
Images
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Chapter 9 opening page Courtesy Timothy D. Lee.
Chapter 9 dec. photo 3 © George Shelley/Age Fotostock
Chapter 9 dec. photo 4 Courtesy Richard A. Schmidt
Figure 9.6 © Norbert Michalke/ imagebroker/age fotostock
Focus on Research 9.2 © Zuma Press/Icon SMI
Chapter 10 opening page © GEPA/Imago/Icon SMI
Chapter 10 dec. photo 2 © Tony Ding/Icon SMI
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Chapter 11 dec. photo 2 © Laura Leonard Fitch
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Images
All other photos © Human Kinetics
27

Chapter 1
Introduction to Motor Learning
and Performance
How Skills Are Studied
Chapter Outline
Why Study Motor Skills?
28

The Science of Motor Learning and Performance
Defining Skills
Components of Skills
Classifying Skills
Understanding Performance and Learning
Summary
Chapter Objectives
Chapter 1 provides an overview of research in human motor skills
with particular reference to their study in motor learning and
performance. This chapter will help you to understand
the scientific method in skills research,
different taxonomies used to classify skills,
common variables used to measure motor performance, and
the rationale for developing a conceptual model of motor
performance.
Key Terms
absolute constant error (|CE|)
absolute error (AE)
closed skill
constant error (CE)
continuous skill
discrete skill
open skill
root-mean-square error (RMSE)
serial skill
skill
tracking
variable error (VE)
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In 2012, a petite American girl named Gabrielle (“Gabby”) Douglas
captivated the sport world with her feats in women’s gymnastics at the
London Olympic Games, and, almost instantly, she became a role model for
many young girls who suddenly wanted to learn gymnastics. The “flying
squirrel,” as she is fondly called by her teammates, won the gold medal in
the women’s all-around competition—arguably the pinnacle in women’s
gymnastics. In the 1980s Jim Knaub, who had lost the use of his legs in an
accident, won several important marathons in the wheelchair division,
earning our heartfelt respect. From the 1970s into the ’90s, David Kiley
earned the title “King of Wheelchair Sports” by (a) winning five gold
medals in the 1976 Paralympic Games in Canada; (b) climbing the highest
mountain in Texas; (c) playing on the United States men’s wheelchair
basketball team five times; and (d) competing at the highest level in tennis,
racquetball, and skiing. And, since the Woodstock music festival in 1969,
Johnny Winter, who is arguably (at least according to the second author) the
world’s best guitarist, continues to amaze audiences with his skills and
versatility. These and countless other examples indicate that skills are a
critical part of human existence. How people can perform at such high
levels, how such skills are developed, and how you can develop some
approximation of these skills in yourself, your children, or your students—
all of these questions generate fascination, encouraging further learning
about human movement.
A description of the study of motor performance and learning starts here.
The overview presented in this chapter introduces the concept of skill and
discusses various features of its definition. The chapter then gives examples
of skill classification schemes important for later applications. Finally, to
help you understand skills effectively using this book, the logic behind the
book’s organization is described: first, the principles and processes
underlying skilled performance, followed by how such capabilities can be
developed with practice.
The remarkable human capability to perform skills is a critical feature of
our very existence. It is almost uniquely human, although various animals
relatively high on the evolutionary scale can be trained to produce what you
might call skilled behaviors (e.g., circus dogs doing somersaults, bears
riding bicycles). Without the capacity for skilled performance, we could not
type the page we are preparing now and you could not read it. And for
students involved in physical education and kinesiology, coaching, physical
(or speech or occupational) therapy, chiropractic, medicine, or human
30

factors and ergonomics, here is the opportunity to learn about the
fundamentals of a wide variety of sports and athletic endeavors, music, and
simply ordinary everyday actions that are so strongly fascinating and
exciting. Human skills take many forms, of course—from those that
emphasize the control and coordination of our largest muscle groups in
relatively forceful activities like soccer or tumbling, to those in which the
smallest muscle groups must be tuned precisely, as in typing or repairing a
watch. This text generally focuses on the full range of skilled behavior
because it is useful to understand that many common features underlie the
performance of skills associated with industrial and military settings, sport,
the reacquisition of movement capabilities lost through injuries or stroke, or
simply the everyday activities of most people.
Most humans are born with the capability to produce many skills, and only
some maturation and experience is necessary in order to produce them in
nearly complete form. Walking and running, chewing, balancing, and
avoiding painful stimuli are some examples of these relatively innate
behaviors. But imagine what simple and uninteresting creatures we would
be if these inherited actions were all that we could ever do. All biological
organisms have the remarkable facility to profit from their experiences, to
learn to detect important environmental features (and to ignore others), and
to produce behaviors that were not a part of their original capabilities.
Humans seem to have the most flexibility of all, which allows gains in
proficiency for occupations as chemists or computer programmers, for
competition in music or athletics, or simply for conducting daily lives more
efficiently. Thus, producing skilled behaviors and the learning that leads to
their development are tightly intertwined in human experience. This book is
about both of these aspects of skills—skilled human performance and human
learning.
This book is not about skills in which the degree of success is determined
by deciding which of many already learned actions the performer is to do.
When the laboratory rat learns to press a bar at the presentation of a sound,
the rat is not learning how to press the bar; rather, the animal is learning
when to make this already learned bar-pressing action. As another example,
in the card game of poker, it does not matter in what fashion the various
cards are played (i.e., moved). What matters is cognitive decision making
about which card to play and when to do it. The study of these kinds of
decision-making processes falls mainly into fields such as experimental
psychology and cognitive neuroscience, and these processes are
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deliberately not included here.
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Johnny Winter, who is (according to this book’s second author) the world’s
best guitarist, demonstrating just one of his nearly unlimited variety of motor
skills.
Why Study Motor Skills?
Because skills make up such a large part of human life, scientists and
educators have been trying for centuries to understand the determinants of
skills and the factors that affect their performance. The knowledge gained is
applicable to numerous aspects of life. Important points apply to the
instruction of skills, where methods for efficient teaching and effective
carryover to life situations are primary concerns. There is also considerable
applicability for improving high-level performances, such as sport, music,
and surgical skills. Of course, much of what coaches and music and physical
education teachers do during their professional activities involves, in one
way or another, skills instruction. The practitioners who understand these
skill-related processes most effectively undoubtedly have an advantage
when their “subjects” begin their trained-for activities.
Other application areas can be emphasized as well. There are many
applications in training skills for industry, where effective job skills can
mean success in the workplace and can be major determinants of
satisfaction both with the job and with life in general. Teaching job skills
most effectively, and determining which of a large number of individuals are
best suited to particular occupations, are common situations in which
knowledge about skills can be useful in industry. Usually these applications
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are considered within human factors (ergonomics).The principles also
apply to physical therapy and occupational therapy settings as well, where
the concern is for the (re)learning and production of movements that have
been lost through head or spinal cord injury, stroke, birth defects, and the
like. Although all these areas may be different and the physical capabilities
of the learners may vary widely, the principles that lead to successful
application are generally the same.
The Science of Motor Learning and
Performance
It is not uncommon that as an area of interest grows, the systematic study of
the principles involved also develops. Motor learning and performance is
no different, in that a science has emerged that allows the formalization of
terms and concepts for others to use. When we use the word “science,” what
do we mean?
The concept of a science implies several things: (a) the active use of theory
and hypothesis testing to further our knowledge; (b) a certain
“infrastructure” that involves books and journals, scientific organizations
that deal with both the fundamental aspects of the science and ways to apply
the knowledge to real-world situations, and granting agencies to provide
funds for research; and, of course, (c) the existence of courses of study of
the area in universities and colleges.
Theories and Hypotheses
Certainly at the heart of every science are theories that purport to explain
how things work. A theory is a human-made structure whose purpose is to
explain how various phenomena occur. The theorist conjures up what are
called hypothetical constructs—imaginary elements, or pieces, that interact
in various ways in the theory. The theorist then describes the ways in which
the hypothetical constructs interact with each other so as to explain some
empirical phenomenon. Then, using logical deduction, scientists determine
certain predictions that the theory makes in its current form. These
predictions form the basis of hypotheses that can be tested, typically in the
laboratory. These hypotheses take the form of statements such as “If I ask
learners to practice under condition x, then learning should be enhanced.”
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Theories are typically tested by doing experiments, which determine
whether or not the hypothesis predicts what happens. In the field of motor
learning and performance, these experiments typically take the form of
having at least two groups of subjects randomly assigned to experimental
treatments, with one group performing a task under condition x (as in the
example just mentioned) and the other group performing under some other
conditions that are reasonably well understood (sometimes called a “control
condition”). If, in this example, the group practicing under condition x
outperforms the group in the control condition, then we say that the
hypothesis is supported. However, a given theory might predict an outcome
that would make sense for several different theories, so an experiment that
supports a hypothesis is not always the strongest type of evidence. What is
usually far stronger is if the results come out contrary to the prediction; this
leads to the logical inference that the theory must be incorrect, allowing us
to reject one of the possible theories. This is so because a theory cannot
“survive” for long if something predicted from it turns out not to be the case.
Because of this difference in the power of the ways in which hypotheses are
tested, scientists tend to search out predictions from a theory that might not
hold if tested in the laboratory.
What Kinds of Skills Have Been Studied?
The science of motor learning and performance has been used to study many
varieties of skills. In the very beginning (research and writing done in the
early 1900s, or perhaps slightly before), two types of investigations can be
identified: (a) investigations of relatively complex, high-level skills such as
telegraphy and typing (e.g., Bryan & Harter, 1897, 1899), and (b) studies by
biologists and physiologists concerning the fundamental mechanisms of
neural control of muscle, muscle force production (Fullerton & Cattell,
1892), and the study of nerves and the nervous system (Fritsch & Hitzig,
1870; Sherrington, 1906).
Focus on Research 1.1
Franklin M. Henry, Father of Motor Behavior
Research
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36

Franklin M. Henry (1904-1993)
Before World War II and during the 1950s and 1960s when much
effort was directed at military skills such as pilotry, most of the
research in movement behavior and learning was done by
experimental psychologists using relatively fine motor skills. Little
effort was devoted to the gross motor skills that would be
applicable to many sports. Franklin M. Henry, trained in
experimental psychology but working in the Department of Physical
Education, at the University of California Berkley, was filling this
gap with a new tradition of laboratory experimentation that started
an important new direction in research on movement skills. He
studied gross motor skills, with performances intentionally
representative of those seen on the playing fields and in
gymnasiums. But he used laboratory tasks——that enabled the
rigorous study of these skills employing methods analogous to those
used in experimental psychology. He examined a number of research
problems, such as the differences among people, practice
scheduling, the mathematical shapes of performance curves, and the
roles of fatigue and rest in performance.
Henry’s influence on the fields of physical education and
kinesiology was widespread by the 1970s and 1980s.
Defining Skills
As widely represented and diverse as skills are, it is difficult to define them
37

in a way that applies to all cases. Guthrie (1952) provided a definition that
captures most of the critical features of skills that we emphasize here. He
defined skill as “the ability to bring about some end result with maximum
certainty and minimum outlay of energy, or of time and energy” (p. 136).
Next, we consider some of the important components (or features) of this
definition.
First, performing skills implies some desired environmental goal, such as
holding a handstand in gymnastics or being able to walk again after a stroke.
Skills are usually thought of as different from movements, which do not
necessarily have any particular environmental goal, such as idly wiggling
one’s little finger. Of course, skills consist of movements because the
performer could not achieve an environmental goal without making at least
one movement.
Second, to be skilled implies meeting this performance goal, this “end
result,” with maximum certainty. For example, while playing darts a player
makes a bull’s-eye. But this by itself does not ensure that he is a skilled
darts player, because this result was achieved without very much certainty.
Such an outcome could have been the result of one lucky throw in the midst
of hundreds of others that were not so lucky. To be considered “skilled”
requires that a person produce the skill reliably, on demand, without luck
playing a very large role. This is one reason why people value so greatly
the champion athlete who, with but one chance and only seconds remaining
at the end of a game, makes the goal that allows the team to win.
Third, a major feature in many skills is the minimization, and thus
conservation, of the energy required for performance. For some skills this is
clearly not the goal, as in the shot put, where the only goal is to throw the
maximum distance. But for many other skills the minimization of energy
expenditure is critical, allowing the marathon runner to hold an efficient
pace or allowing the wrestler to save strength for the last few minutes of the
match. We evolved to walk as we do, in part, because our walking style
minimizes energy expenditure for walking a given distance. This minimum-
energy notion applies not only to the physiological energy costs but also to
the psychological, or mental, energy required for performance. Many skills
have been learned so well that the performers hardly have to pay attention to
them, freeing their cognitive processes for other features of the activity, such
as strategy in basketball or expressiveness in dance. A major contributor to
the efficiency of skilled performance is, of course, practice, with learning
38

and experience leading to the relatively effortless performances so admired
in highly skilled people.
Finally, another feature of many skills is for highly proficient performers to
achieve their goals in minimum time. Many skills have this as the only goal,
such as a swimming race. Minimizing time can interact with the other skill
features mentioned, however. Surgeons who conduct invasive surgery need
to work quickly to minimize the opportunity for infections to enter the body.
Yet surgeons obviously need to work carefully, too. Speeding up
performance often results in imprecise movements that have less certainty in
terms of achieving their environmental goals. Also, increased speed
generates movements for which the energy costs are sometimes higher. Thus,
understanding skills involves optimizing and balancing several skill aspects
that are important to different extents in different settings. In sum, skills
generally involve achieving some well-defined environmental goal by
maximizing the certainty of goal achievement,
minimizing the physical- and mental-energy costs of performance, and
minimizing the time used.
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To be skilled implies that a person can produce an end result with a high
degree of certainty. For example, an expert darts player can consistently
throw the dart close to his target.
Components of Skills
The elegant performance of the skilled dancer and the artistic talents of an
expert sculptor may appear simple, but the performance goals actually were
realized through a complex combination of interacting mental and motor
processes. For example, many skills involve considerable emphasis on
sensory–perceptual factors, such as detecting that a tennis opponent is going
to hit a shot to your left or that you are rapidly approaching a car that has
suddenly stopped on the road ahead of you. Often, sensory factors require
the split-second analysis of patterns of sensory input, such as discerning that
the combined movements of an entire football team indicate that the play
will be a running play to the left side. These perceptual events lead to
decisions about what to do, how to do it, and when to do it. These decisions
are often a major determinant of success. Finally, of course, skills typically
depend on the quality of movement generated as a result of these decisions.
Even if the situation is correctly perceived and the response decisions are
appropriate, the performer will not be effective in meeting the
environmental goal if she executes the actions poorly.
These three elements are critical to almost any skill:
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Perceiving the relevant environmental features
Deciding what to do and where and when to do it to achieve the goal
Producing organized muscular activity to generate movements that
achieve the goal
The movements have several recognizable parts. Postural components
support the actions; for instance, the arms and hands of a surgeon need to be
supported by a stable “platform” in order to perform accurately. Body
transport, or locomotion, components move the body toward the point where
the skill will take place, as in carrying a package of shingles up a ladder to
place on the roof of a house.
It is interesting, but perhaps unfortunate, that each of these skill components
seems to be recognized and studied in isolation from the others. For
example, sensory factors in perception are studied by cognitive
psychologists, scientists interested in (among other things) the complex
information-processing activities involved in seeing, hearing, and feeling.
Sometimes these factors are in the realm of psychophysics, the branch of
psychology that examines the relationship between objective physical
stimuli (e.g., vibration intensity) and the subjective sensations these stimuli
create when perceived (loudness). Factors in the control of the movement
itself are typically studied by scientists in the neurosciences, cognitive
psychology, biomechanics, and physiology. Skill learning is studied by yet
another group of scientists in kinesiology and physical education, in
experimental or educational psychology, or in the field of human factors and
ergonomics. A major problem for the study of skills, therefore, is the fact
that the several components of skill are studied by widely different groups
of scientists, often with little overlap and communication among them.
All of these various processes are present in almost all motor skills. Even
so, we should not get the idea that all skills are fundamentally the same. In
fact, the principles of human performance and learning depend to some
extent on the kind of movement skill to be performed. So, the ways in which
skills have been classified are discussed next.
Classifying Skills
There are several skill classification systems that help organize the research
findings and make application somewhat more straightforward. These are
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presented in the following sections.
Open and Closed Skills
One way to classify movement skills concerns the extent to which the
environment is stable and predictable throughout performance. An open
skill is one for which the environment is variable and unpredictable during
the action. Examples include most team sports and driving a car in traffic
where it is difficult to predict the future moves of other people. A closed
skill, on the other hand, is one for which the environment is stable and
predictable. Examples include swimming in an empty lane in a pool and
drilling a hole in a block of wood. These “open” and “closed” designations
actually mark only the end points of a continuum, with the skills lying in
between having varying degrees of environmental predictability or
variability (see Gentile, 2000, for a fuller discussion).
This classification points out a critical feature for skills, defining the
performer’s need to respond to moment-to-moment variations in the
environment. It thus brings in the subprocesses associated with perception,
pattern recognition, and decision making (usually with the need to perform
these processes quickly) so the action can be tailored to the environment.
These processes are arguably minimized in closed skills, where the
performer can evaluate the environmental demands in advance without time
pressure, organize the movement in advance, and carry it out without
needing to make rapid modifications as the movement unfolds. These
features are summarized in table 1.1.
42

Discrete, Continuous, and Serial Skills
A second scheme for classifying skills concerns the extent to which the
movement is an ongoing stream of behavior, as opposed to a brief, well-
defined action. At one end of this dimension is a discrete skill, which
usually has an easily defined beginning and end, often with a very brief
duration of movement, such as throwing a ball, firing a rifle, or turning on a
light switch. Discrete skills are particularly important in both sport and
daily actions, especially considering the large number of discrete hitting,
kicking, and throwing skills that make up many sport activities, as well as
everyday skills of fastening buttons, writing your signature, and tying your
shoelaces. Discrete skills often result in a measured outcome score, which
can be combined with other scores to result in several types of “error”
scores, discussed in Focus on Research 1.2 and 1.3.
At the other end of this dimension is a continuous skill , which has no
particular beginning or end, the behavior flowing on for many minutes, such
as swimming and knitting. As discussed later, discrete and continuous skills
can be quite different, requiring different processes for performance and
demanding that they be taught somewhat differently as a result.
One particularly important continuous skill is tracking, in which the
performer’s limb movements control a lever, a wheel, a handle, or some
other device to follow the movements of some target-track. Steering a car
involves tracking, with steering wheel movements made so the car follows
the track, defined by the roadway. Tracking movements are very common in
real-world skills situations, and much research has been directed to their
performance and learning. Tracking tasks are sometimes scored using a
particular error score, called root-mean-square error (RMSE) presented in
detail in Focus on Research 1.3, “Error Scores in Continuous Tasks.”
Between the polar ends of the discrete-continuous-skill continuum is the
serial skill, which is often thought of as a group of discrete skills strung
together to make up a new, more complicated skilled action. See table 1.2
43

for a comparison summary. Here the word “serial” implies that the order of
the elements is usually critical for successful performance. Shifting car
gears is a serial skill, with three discrete shift lever action elements (along
with accelerator and clutch elements) connected in sequence to create a
larger action. Other examples include performing a gymnastics routine and
most types of cooking. Serial skills differ from discrete skills in that the
movement durations tend to be somewhat longer, yet each component retains
a discrete beginning and end. One view of learning serial skills suggests that
the individual skill elements present in early learning are somehow
combined to form one larger, single element that the performer controls
almost as if it were truly discrete in nature (e.g., the smooth, rapid way a
gymnast shifts from one maneuver to another on the rings).
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45

Swimming in an empty pool lane is an example of a closed, continuous skill.
Focus on Research 1.2
Error Scores in Discrete Tasks
Quite frequently in research, we are called on to generate a method
for computing an accuracy score for a given subject who was
attempting a series of trials on a test requiring accuracy, often
involving discrete tasks. As we shall see here, there are various
ways to do it.
Assume that you are testing subjects on a throwing task, in which
subjects have to throw a ball exactly 50 ft away from where they are
standing. Two hypothetical subjects perform this task for five trials,
and the following are the results:
46

Which of these two subjects was more skillful in this task? The
problem here is to generate a single number that accurately reflects
their skill in the task (a throwing accuracy “score”), based on those
five throwing trials. Several candidate measures are possible.
Constant Error (CE)
The most obvious way to determine which subject was more
accurate is to compute the error deviation of each throw, relative to
the target, and then calculate the average of these error deviations.
For example, on trial 1, Chester’s error score was -4 (his throw
was 46 ft, and thus, 4 ft short of the target). The error score on the
second trial was +2 (his 52-ft throw as 2 ft too far). After the error
scores for each trial are computed the mean can then be calculated
to determine the average error deviation. This is termed the
subjects’ average constant error. The interpretation is that Chester
tended to overthrow the 50 ft target by 0.4 ft; John-Lee tended to
underthrow the target by 2.6 ft.
The formula for constant error is
CE = [Σ (X i − T) / N]
where: Σ = the “sum of,” i = trial number, X i = score for the ith
trial, T = the target distance, and N = the number of trials.
Absolute Error (AE)
Another relatively obvious way to combine the scores into a single
number is to consider the absolute value (i.e., with the sign ignored
or removed) of the error on each trial, and take the average of those
error scores for the various trials. For example, for Chester the first
trial has an error of −4 ft; when we take the absolute value, the first
trial has an absolute error of 4 ft; the second trial has an error of +2
47

ft, whose absolute value is 2. If we do this procedure for the
remainder of the trials for Chester, and all the trials for John-Lee,
the computed average absolute error for Chester is 6.4; for John-Lee
it is 4.2. Here, with the direction of the errors being disregarded, the
interpretation is that Chester was off-target more than was John-Lee.
The formula for average absolute error (AE) is
AE = [Σ (|X i − T|) / N]
where: Σ, i, X, T, and N are defined as before for constant error, and
the vertical bars (|) mean “absolute value of.”
Variable error (VE)
The third measure of skill is actually a measure of the subject’s
inconsistency—that is, how different each individual score was in
comparison to his average (CE) score. To compute variable error
(VE), we square the difference between each trial’s error score and
the subject’s own constant error [(X i − CE)
2], sum those over all of
the trials, and divide by N. Now, since these are squared values, we
return them to their original state by computing the square root of
this value.
In this example, for Chester’s first trial, the difference between X 1
and Chester’s average CE (which was +0.4) is [(-4 – (+0.4)] = -4.4
ft, which, when squared, is 19.36 ft. For the second trial, the
difference between X 2 and Chester’s average CE is [(+2) – (+0.4)]
= 1.6 ft, which, when squared, is 2.56 ft. Now do the same thing for
trials 3, 4, and 5; add up all five squared differences; divide this
number by N = 5; then take the square root of that number; and you
finally have VE. The score is interpreted as the inconsistency in
responding, that is, how variable the performance is about the
subject’s own CE. Of course when computing the value for John-
Lee, we would use his CE (which was -2.6) in the computations.
The computed VE score for Chester was 7.3 ft, and for John-Lee the
VE was 4.5 ft. The interpretation is that even though Chester’s
average throw was closer to the goal than was John-Lee’s average
throw, Chester was more inconsistent in those throws than was
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John-Lee.
The formula for variable error is
VE = √ [Σ (X i − CE)
2 / N]
where Σ, X, i, CE, and N are defined as before, and √ is the square
root.
AE was employed as a measure of error very frequently until
investigators Schutz and Roy (1973) pointed out some statistical
difficulties with it. Today, investigators tend to use CE (as a
measure of average bias, or directional error) and VE (as a measure
of inconsistency). Sometimes investigators use a statistic called
absolute constant error (abbreviated |CE|) instead of CE for a
subject’s measure of bias. The |CE| is simply the average absolute
value of the computed CE score as defined previously. The
advantage is that |CE| retains the magnitude of average deviation
from the target, but prevents two scores from “canceling out” when
subjects are averaged together to present a group score. Much more
on these error scores is presented in chapter 2 of Schmidt and Lee
(2011).
Focus on Research 1.3
Error Scores in Continuous Tasks
Continuous tasks, like tracking, are capable of producing many error
scores on a single trial. Consider figure 1.1 as an example of a
portion of a single trial of a tracking task for one subject. The blue
track represents the stimulus goal, such as a highway lane along
which a driver might navigate a car. The red line represents the
exact (unmarked) center of the track from edge to edge. The dotted
line represents a subject’s tracking behavior, in this case, how close
to the center of the road the car is maintained. How can skill be
measured in this single trial of a continuous performance?
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A common method used by researchers who study tracking tasks is
to compute a measure called root-mean-square error (RMSE).
One does this by computing the distance of the subject’s tracking
response from the target line at set distance-points along the track
(e.g., every 10 ft of highway traveled) or, more commonly, at a
constant interval of time along the track (e.g., every 100 ms). This
method effectively “slices” the movement into equal intervals of
tracking behavior, from start to finish.
With each “slice” of the track, the researcher then computes how far
the subject’s tracking position is from the target. Since the red line
in figure 1.1 represents the center of the track, it is convenient to
define the red line as the “zero” position. Therefore, if the subject is
to the right of the target, the measure is given a positive error value;
if the tracking position is to the left of the target, the measure gets a
negative value. The root-mean-square error score is computed by
first calculating the squared deviations for each measured position
along the track, then taking the square root of the sum of those
scores.
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Figure 1.1 Measuring RMSE in a tracking task. The blue area
represents the “track” (such as a highway lane); the red (solid) line
represents the unmarked center of the lane, and can be considered
the subject’s goal track. The blue (dotted) line represents the
subject’s performance in attempting to follow the red line.
The RMSE is a more complex measure of performance than any of
the error scores for discrete tasks, because it represents two
components of behavior. The RMSE reflects both the subject’s bias
tendency (e.g., on average, to drive closer to the right edge of the
lane than the left edge) as well as inconsistency in the tracking
behavior (how variable the performance tends to be). It is well
recognized as a very good measure of how effectively the person
tracked.
Understanding Performance and Learning
In some ways, skilled performance and motor learning are interrelated
concepts that cannot be easily separated for analysis. Even so, a temporary
separation of these areas is necessary for presentation and making eventual
understanding much easier. Many of the terms, principles, and processes that
scientists use to describe the improvements with practice and learning (the
subfield of motor learning) actually come from the literature on the
underlying processes in the production of skilled motor performance (the
subfield of human performance; the field of motor behavior often includes
both motor learning and human performance). Therefore, whereas at first
glance it might seem most logical to treat motor learning before motor
performance (a person has to learn before performing), it turns out to be
awkward to present information on learning without first having provided
this background performance information.
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For this reason this book is organized into two parts, the first of which
introduces the terminology, concepts, and principles related to skilled
human performance without very much reference to processes associated
with learning. The principles here probably apply most strongly to the
performance of already skilled actions. Having examined the principles of
how the motor system produces skills, the discussion turns to how these
processes can be altered, facilitated, and trained through practice. This
involves motor learning, whose principles apply most strongly to the
instruction of motor skills.
When studying motor-skill performance and learning, it is helpful to
understand where each concept fits into the complex process of performing
a skill. For this reason, and to help apply skills information to a variety of
settings, this book develops a conceptual model. This model represents the
big picture of motor performance; and as new topics are introduced, they
are added to the model, tying together most of the major processes and
events that occur as performers produce skills. Models of this type are
critical in teaching and in science because they embrace many seemingly
unrelated facts and concepts, linking real-world knowledge with the
concepts being discussed.
Models can be of many types, of course, such as the plumbing-and-pump
model of the human circulatory system and the variety of balls that model
the structure of atoms, the solar system, and molecules in chemistry. For
skills, a useful conceptualization is an information-flow model, which
considers how information of various kinds is used in producing and
learning a skilled action. The first portions of the text build this model, first
considering how the sensory information that enters the system through the
receptors is processed, transformed, and stored. Then, to this is added how
this sensory information leads to other processes associated with decision
making and planning action. To the emerging conceptual model are then
added features of the initiation of action as well as the activities involved
while the action is unfolding, such as controlling muscular contractions and
detecting and correcting errors; this is highly related to the performer’s
analysis of the sensations produced as a result of performing the action—
processes related to feedback. In the second portion of the text, which deals
with learning, the model provides an effective understanding of the
processes that are, and are not, influenced by practice.
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Summary
People regard skills as an important, fascinating aspect of life. Knowledge
about skills has come from a variety of scientific disciplines and can be
applied to many settings, such as sport, teaching, coaching, industry, and
physical therapy.
Skill is usually defined as the capability to bring about some desired end
result with maximum certainty and minimum time and energy. Many different
components are involved; major categories are perceptual or sensory
processes, decision making, and movement output. Skills may be classified
along numerous dimensions, such as open versus closed skills, and discrete,
continuous, and serial skills. These classifications are important because the
principles of skills and their learning often differ for different skill
categories.
The text’s particular organization of materials should facilitate an
understanding of skills. After this introduction, the remainder of part I treats
the principles of human skilled performance and the underlying processes,
focusing on how the various parts of the motor system act to produce skilled
actions. Part II examines how to modify these various processes by practice
and motor learning. Understanding how all of these components can operate
together is facilitated by a conceptual model of human performance that is
developed throughout the text.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 1.1: Classify skills as discrete, serial, or continuous in nature
by selecting the appropriate category for each of five examples.
Activity 1.2: Review the types of error measured in motor learning
research by matching various error measures with their definitions.
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Principles-to-Application Exercise
Activity 1.3: The principles-to-application exercise for this chapter
applies the concepts in this chapter to your own experience by asking
you to analyze a motor skill that you have learned in the past or are
currently learning. You’ll describe the skill as open or closed and as
discrete, continuous, or serial and identify the goals of the skill and the
factors that contribute to your success when performing it.
Check Your Understanding
1. Define a skill and indicate why each of the following terms is
important to that definition.
Environmental goal
Maximum certainty
Minimum energy costs
Minimum time
2. Distinguish between open and closed skills and between discrete,
serial, and continuous skills. Give one example of each.
3. List and describe three elements critical to almost any skill.
4. Define a theory and describe how scientists use theories to design
experiments.
Apply Your Knowledge
1. List three motor skills that you have learned, either recently or when
you were younger (e.g., swinging a baseball bat, tying your shoelaces,
or playing a chord on the piano). Classify each of the skills you have
listed, distinguishing between open and closed and between discrete,
serial, and continuous skills. Are maximum certainty, minimum energy
costs, and minimum time equally important for each of the tasks you
have listed? Why or why not?
Suggestions for Further Reading
Historical reviews of motor skills research were conducted by Irion (1966),
Adams (1987), and Schmidt and Lee (2011). The first edition of Motor
Control and Learning (Schmidt, 1982) contains a chapter devoted to the
scientific study of motor skills. Snyder and Abernethy (1992) devoted a
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chapter to the early stages of the career of Franklin Henry. Skills
classification systems are reviewed in Poulton (1957), Gentile (1972), and
Farrell (1975). See the reference list for these additional resources.
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Part I
Principles of Human Skilled
Performance
Chapter 1 introduced just a few of the types of motor performance that
fascinate us—from the powerful movements of elite athletes to virtuoso
musical performances. We now begin a two-part exploration of such skilled
motor performances. In part I, we emphasize the research-based principles
of how such motor performances can occur. As we introduce the various
concepts concerning motor performance, we “build” a conceptual model of
human skilled performance throughout the first part of the book. This model
contains and summarizes many of the major factors that underlie such
performances and is useful as a guide for understanding how motor skills
are performed. In part I, the focus is mainly on the factors that allow skilled
motor performances to occur, without much reference to practice and
learning of skills. After we explain the terminology and fundamental
concepts of human performance in part I, we turn in part II to some of the
principles governing how certain components outlined in part I are acquired
with practice and experience.
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Chapter 2
Processing Information and
Making Decisions
The Mental Side of Human
Performance
Chapter Outline
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The Information-Processing Approach
Reaction Time and Decision Making
Memory Systems
Summary
Chapter Objectives
Chapter 2 describes a conceptualization of how decisions are made
in the performance of motor skills. This chapter will help you to
understand
the information-processing approach to understanding motor
performance,
the stages that occur during information processing,
various factors that influence the speed of information
processing,
the role of anticipation in hastening the speed of responding,
and
memory systems and their roles in motor performance.
Key Terms
choice reaction time
foreperiod
Hick’s Law
information-processing approach
long-term memory (LTM)
memory
movement time (MT)
population stereotypes
reaction time (RT)
response time
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short-term memory (STM)
short-term sensory store (STSS)
simple RT
spatial anticipation
temporal anticipation
The batter was ready this time. The pitcher had just thrown three slow
curveballs in a row. Although the batter had two strikes against him, he felt
confident because he thought the next pitch would be a fastball, and he was
prepared for it. As the pitch was coming toward him, he strode forward and
began to swing the bat to meet the ball, but he soon realized this was another
curve. He could not modify his swing in time, and the bat crossed the plate
long before the ball arrived. The pitcher had beaten him again.
How did the batter’s faulty anticipation interfere with his performance?
What processes were required to amend the action? To what extent did the
stress of the game interfere? Certainly a major concern for the skilled
performer is the evaluation of information, leading to decision making about
future action. But what information was the batter reflecting on while the
pitch was coming toward the plate—the spin of the ball? its velocity? its
location? the type, speed, and location of previous pitches? how fast to
swing the bat? where to try to hit the ball? Processing all, or even some, of
this information would surely have affected the batter’s success in hitting the
ball.
Without a doubt, one of the most important features of skilled performance
is deciding what to do (and what not to do) in situations in which these
decisions are needed quickly and predictably. After all, the most beautifully
executed baseball throw to first base is ineffective if the throw should have
been directed somewhere else instead. This chapter considers factors
contributing to these decision-making capabilities, including processing
environmental information, and some of the factors that contribute to the
actual decision. We begin with a general approach for understanding how
the motor system uses information, which will form the basis of the
conceptual model of human performance.
The Information-Processing Approach
Researchers have found it useful to think of the human being as a processor
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of information very much like a computer. Information is presented to the
human as input; various processing stages within the human motor system
generate a series of operations on this information; and the eventual output
is skilled movement. This simple information-processing approach is
shown in figure 2.1.
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Figure 2.1 A simplified information-processing approach to thinking about
human performance.
A major goal of researchers interested in the performance of motor skills is
to understand the specific nature of the processes in the box labeled
“Human” in figure 2.1. There are many ways to approach this problem; a
particularly useful one assumes that there are separable information-
processing stages through which the information must pass on the way from
input to output. For our purposes, here are three of these stages:
Stimulus identification
Response selection
Movement programming
This stage analysis of performance generally assumes that peripheral
information enters the system and is processed in the first stage. When, and
only when, this stage has completed its operations, the result is passed on to
the second stage whose processing is completed, and then the Stage-2 result
is passed to the third stage, and so on. A critical assumption is that the
stages are nonoverlapping—meaning that all of the processing in a given
stage is completed before the product of classification is passed to the next
stage; that is, processing in two different stages cannot occur at the same
time. This process finally results in an output—the action. What occurs in
these stages of processing?
Stimulus Identification Stage
During this first stage the system’s problem is to decide whether a stimulus
has been presented and, if so, what it is. Thus, stimulus identification is
primarily a sensory stage, analyzing environmental information from a
variety of sources, such as vision, audition, touch, kinesthesis, and smell.
The components, or separate dimensions of these stimuli, are thought to be
“assembled” in this stage, such as the combination of edges and colors that
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form a representation of a car in traffic. Patterns of movement are also
detected, such as whether other objects are moving, in what direction and
how quickly they are moving, and so on, as would be necessary for driving
a car in heavy traffic. The result of this stage is thought to be some
representation of the stimulus, with this information being passed on to the
next stage—response selection.
Response Selection Stage
The activities of the response selection stage begin after the stimulus
identification stage provides information about the nature of the
environmental stimulus. This stage has the task of deciding what response to
make, given the nature of the situation and environment. In the driving
example, the choice from available responses might be to overtake another
vehicle, to slow the car, or to make an avoidance maneuver. Thus, this stage
requires a kind of transition process between sensory input and movement
output.
Movement Programming Stage
This final stage begins its processing upon receiving the decision about
what movement to make as determined by the response selection stage. The
movement programming stage has the task of organizing the motor system to
make the desired movement. Before producing a movement, the system must
ready the lower-level mechanisms in the brainstem and spinal cord for
action, and it must retrieve and organize a motor program that will
eventually control the movement. In the driving example, if the response-
selection stage determined that a braking response was required, then the
organization of the motor program responsible for executing a braking
action would occur in the movement-programming stage.
Expanding the Conceptual Model
Figure 2.2 adds some detail to the simple notion of information processing
described in figure 2.1 by including the stages of processing just described.
This elaboration is the first revision of our conceptual model, which will be
expanded throughout the text as we introduce more fundamental ideas of
human performance.
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Figure 2.2 An expanded information-processing model, highlighting three
critical processing stages in thinking about human performance.
Clearly, these stages are all included within the human information system
and are not directly observable under usual circumstances. However,
several laboratory methods allow scientists to learn about these stages.
Reaction time (abbreviated RT) is one of the most important tools that
researchers have used for many decades to learn about these stages. We will
examine RT in much more detail to understand how information processing
operates.
Reaction Time and Decision Making
An important performance measure indicating the speed and effectiveness of
decision making is the RT interval—the interval of time that elapses
following a suddenly presented, often unanticipated stimulus until the
beginning of the response. The concept and assessment of RT are important
because it represents a part of some everyday events, such as braking
rapidly in response to an unanticipated traffic event; responding to catch a
glass that has been accidentally tipped over; and, in sport, in such events as
sprint races, where an auditory tone serves as a stimulus to begin a race.
An old saying is that a picture is worth a thousand words. This is certainly
borne out by the redrawn photo of a long-ago sprint race (figure 2.3)
reprinted from Scripture (1905). The starter on the left side of the photo has
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already fired his gun, perhaps a couple of hundred or so milliseconds
earlier, as you can see by the position of the smoke from the pistol rising
above the starter. And yet the runners are all still in their ready positions,
and are only now just beginning to move. The photo illustrates nicely the
substantial delay involved in RT. Being able to minimize RT in such a
situation is critical to getting the movement under way as rapidly as
possible. Because RT is a fundamental component of many skills, it is not
surprising that much research attention has been directed toward it.
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Figure 2.3 Illustration of the RT delay in a sprint start; the starting gun has
been fired, yet the athletes are still on their marks because of the delay in
processing the signal from the starting gun (the delay is contained in the
reaction-time interval).
But RT has important theoretical meaning as well, which is the major reason
it has attracted so much research attention. However, sometimes there is
confusion about what RT is and how it is measured. To review, researchers
define the RT interval very carefully; it is the period of time beginning when
the stimulus is first presented and ending when the movement response
starts. Note that the RT interval does not include the time that is taken to
complete the movement, as illustrated in figure 2.4. That period of time,
from the end of RT until the completion of the movement, is typically called
the “movement time” (or simply MT). Hence, terms like “brake RT” used
to describe the time it takes a person to press the brake pedal in a car are
technically incorrect, as the time taken to press the brake includes the time
for the foot’s movement from the accelerator to depress the brake, which
occurs after the reaction interval. What many refer to as brake RT is
actually the total of RT plus MT—what is called response time.
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Figure 2.4 Measuring reaction time, movement time, and response time for
pressing the brake pedal in a car.
The RT interval is a measure of the accumulated durations of the three
sequential and nonoverlapping stages of processing seen in figure 2.2. Any
factor that increases the duration of one or more of these stages will thus
lengthen RT. For this reason, scientists interested in information processing
have used RT as a measure of the speeds of processing in these stages. Next
is a discussion of how changes in RT can inform us about the stages of
processing.
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Driving a car in traffic presents the driver with a wide range of possible
stimuli and responses.
Factors That Influence Decision Making
There are many important factors that influence RT, ranging from the nature
of the stimulus information presented to the nature of the movement required
of the performer. Some of the more important factors related to human
performance are considered in this section.
Number of Stimulus–Response Alternatives
Consider the following example involving driving a car. In light traffic, the
number of possible emergency situations that require an evasive response is
usually less than in heavy traffic. Unexpected events from multiple vehicles
around you serve to amplify the possible trouble situations that could occur,
compared to extremely light or no traffic. One of the most important factors
influencing the time to start an action is the number of stimuli (each having
its own response) that can possibly occur at any given time. Controlled
laboratory experiments of this type generally involve several possible
stimuli, such as lights, and several different responses from which the
subject is to choose, such as pressing different keys depending on which
stimulus light has been illuminated.
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Figure 2.5 illustrates a typical experimental setup in which the stimulus
array contains eight possible lights that could turn on and a response panel
with eight corresponding response keys. Although all eight stimulus lights
and response keys are seen in each version of the experiment, the researcher
instructs the participant about the number of stimulus–response (S-R)
alternatives from which to expect a stimulus (a light suddenly illuminated)
on any given block of trials. For example, in figure 2.5c, the participant
would know that any one of the middle four lights could be lit, which would
require the response of pressing the assigned one of the corresponding keys.
Note, however, that on any given trial, only one stimulus light will be
illuminated so only one key needs to be pressed.
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Figure 2.5 Different combinations of number of stimulus–response
alternatives. The boxes represent potential visual stimuli; the circles
represent the keys to press in response to these stimuli. Red colors represent
the S-R events that are possible; black colors represent nonstimuli and
nonresponse keys.
This is termed choice reaction time, in which the performer must choose
one response from a subset of possible predetermined movements. Typically
the performer receives a warning signal, followed by a foreperiod of
unpredictable length (e.g., 2, 3, or 4 s, the order being randomly
determined). When the reaction stimulus is suddenly presented, it is only
then that the performer is informed about which button to press; RT is the
time required to detect and recognize the stimulus and select and initiate the
proper response.
Generally, as the number of possible S-R alternatives increases, there is an
increase in the time required to respond to any one of them. The fastest
situation involves only one stimulus and one response, termed simple RT.
Increased RT due to a greater number of S-R alternatives is of critical
importance in understanding skilled performance, forming the basis of
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Hick’s Law (see Focus on Research 2.2). The increase in RT is very large
when the number of alternatives is increased from one to two. As seen in
figure 2.6, RT might increase from about 190 ms with simple RT to more
than 300 ms for a two-choice case—at least a 58% increase in the time
required to process the stimulus information into the response! As the
number of choices becomes larger, adding extra choices still increases RT,
but the increases become smaller and smaller (e.g., the increase from 9 to
10 choices might be only 20 ms, or about 2% or 3%). Even this small
amount of delay can be critical in determining success in many situations.
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Figure 2.6 The relationship between the number of possible stimulus–
response (S-R) alternatives and reaction time.
Reprinted by permission from Schmidt and Lee 2011; Data from Merkel 1885.
Focus on Research 2.1
Donders’ Stages of Processing
Determining the durations of mental activities goes back many years,
and one of the first experiments involving humans was undertaken
by Dutch physician F.C. Donders in 1868. Donders’ “additive-
factors” logic was that insight into the durations of the various
stages could be understood by adding, or subtracting, the time taken
for specific task requirements. Have a look at the illustrations in
figure 2.5. The illustration in figure 2.5a represents the simplest
situation—the participant’s task is to press the response key as
rapidly as possible when the stimulus above it appears. No other
stimulus will appear and no other response will be required—the
task is merely to respond as quickly as possible in this situation.
According to Donders, such a task (which he called an A-type, or
what is now called simple RT) requires only the process of
“stimulus detection,” as the performer knows the response to make
before the stimulus comes on.
Contrast this task with another in which the participant is required to
respond to the signal, as in figure 2.5a, with a rapid key press.
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However, in this task (a C-type, currently called a “go/no-go
reaction”), one of the other stimuli will appear at times. The
participant’s task is to not respond with a button press in these
trials, but to respond only when the specified stimulus appears.
Donders reasoned that this task also required stimulus detection, as
in the A-type task. But, in addition, this task requires that the
participant perform a “stimulus identification” process—identifying
that the stimulus was the specified one before responding. Hence the
difference in RTs between the two tasks supposedly required the
additional stage of stimulus identification.
Lastly, Donders considered a third type of task—a B-type task
(currently called a choice-RT task)—that required subjects to
respond to one of the alternative stimuli with an appropriate key
press as shown in figure 2.5, b through d. This task is similar to the
C-type task in that the stimulus must be detected and identified. But,
in addition, the B-task requires that the subject respond by selecting
the appropriate key to press. Thus, compared to the C-task, the
additional RT required to complete a B-response is caused by the
insertion of a “response selection” stage of processing.
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It may be of interest that when one of us (RAS) was on a sabbatical leave in Utrecht, The
Netherlands, Donders’ laboratory building was located on F.C.-Dondersstraat (in English,
F.C. Donders Street), which was directly on RAS’s bicycle commute to the lab where he
was working.
Exploring Further
1. According to Donders’ logic, if the A-type RT was 150 ms, the
B-type RT was 240 ms, and the C-type RT was 180 ms, what
would be the durations of the stimulus identification and
response selection stages?
2. Donders was one of the first to explore the contents and
workings of the stages of information processing by
rearranging task conditions to be able to add or subtract
specific processing requirements systematically. The approach
is not without problems, however. Critically examine the
following assumptions of this method:
a. That processing stages are serially arranged, with no
overlaps in time
b. That, as compared to the A-reaction, the C-type reaction
task requires only the additional stage of stimulus
recognition, and no others; an analogous assumption
involves the B- and C-tasks and the response selection
stage
To what extent do you think that these assumptions are correct?
Focus on Research 2.2
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Hick’s Law
Over a century ago, Merkel (1885, cited by Woodworth, 1938)
asked subjects in a choice-RT experiment to press a reaction key
when one of up to 10 possible stimuli was presented. The stimuli
were the Arabic numerals 1 through 5 and the Roman numerals I
through V. Each stimulus was paired with one finger or thumb and
response key. For example, the possible stimuli on a set of trials
might be 2, 3, and V (a three-choice case), and the subjects were to
respond with either the right index, right middle, or left thumb if and
when the associated stimulus was presented. Merkel varied the
number of possible stimulus–response alternatives in different sets
of trials. (It is important to remember that only one of the N possible
stimuli is ever presented on a given trial.)
Merkel’s results are shown in figure 2.6, where choice RT is plotted
as a function of the number of S-R alternatives. You can see that as
the number of alternatives was increased there was a sharp rise in
RT (roughly 120 ms; see figure 2.6) from N = 1 to N = 2; this rise
becomes smaller as the number of alternatives is increased toward
10 (from N = 9 to N = 10, where choice RT increases roughly 3 ms;
see figure 2.6).
Much later Hick (1952), and independently Hyman (1953),
discovered that the relationship between choice RT and the
logarithm to the base 2 of the number (N) of S-R alternatives,
abbreviated Log2(N), was linear (see figure 2.7). [Log2(N) is the
power to which the base 2 must be raised to equal N. For example,
the logarithm to the base 2 of 8 is 3, abbreviated Log2(8) = 3,
because the base 2 raised to the third power, (23) = 8.] This
relationship has become known as Hick’s Law, and it holds for a
wide variety of situations using different kinds of subjects, different
movements, and different kinds of stimulus materials. It is one of the
most important laws of human performance.
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Figure 2.7 Hick’s Law: The relation between choice RT and
number of S-R alternatives (N) is replotted using Merkel’s data
from figure 2.6, with choice RT as a function of Log2(N).
Reprinted by permission from Schmidt and Lee 2011; Data from Merkel 1885.
Hick’s Law in equation form is: Choice RT = a + b log2(N), where
a is the RT-intercept and b is the slope. The relationship implies that
choice RT increases a constant amount every time the number of
stimulus–response alternatives (N) is doubled (e.g., from N = 2 to N
= 4, where the Log2(N) is 1 or 2, respectively; or from N = 8 to N =
16, where the Log2(N) = 3 and 4, respectively). This led to an
important interpretation of Hick’s Law: Because the amount of
information needed to resolve the uncertainty among N possible
choices is Log2(N), Hick’s Law says that choice RT is linearly
related to the amount of information that must be processed to
resolve the uncertainty about the various possible stimulus–response
alternatives. Doubling the amount of information to be processed by
doubling N therefore increases choice RT by a constant amount (the
duration required to respond to one of the alternatives); that is, this
operation (doubling N) increases choice RT by a constant amount.
This constant amount is the slope of the log relation known as
Hick’s Law.
Exploring Further
1. How does the concept of “uncertainty” relate to amount of
information in Hick’s law?
2. Name two factors that would be expected to influence the
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magnitude of the slope (b) of Hick’s Law, and describe how
variations in these factors would be predicted to increase or
decrease the slope.
Stimulus–Response Compatibility
An important determinant of choice RT is S-R compatibility, usually defined
as the extent to which the stimulus and the response it evokes are connected
in a “natural” way. Turning the handlebars of a bicycle to the right to move
in that direction is an example of S-R compatibility because the movement
of the handlebars and the change in the intended direction are the same—that
is, they are said to be directionally compatible. Imagine how difficult it
would be to require a movement of the handlebars to the left in order to turn
right. Perhaps that explains some of the reason that steering a sailboat is
trickier than steering a bicycle—the sailor needs to move the tiller to the left
in order to change the heading of the boat to the right.
Figure 2.8 illustrates two types of S-R spatial compatibility “mappings.”
The ensemble of stimuli and responses in illustration in 2.8a is the more
compatible of the two because either of the stimulus lights calls for the
participant to respond in that direction and on the same side of the body as
the light. In the example in 2.8b, however, the right light calls for the left
hand to be moved and the left light calls for the right hand to be moved. The
spatial mapping of the stimuli and required response is not nearly so
spatially direct and unambiguous; this situation is said to be “S-R
incompatible.”
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Figure 2.8 The relationship between the two stimuli and the two responses
is more spatially “S-R compatible” in the array on the left (a) than in the
array on the right (b).
Reprinted by permission from Schmidt and Lee 2011.
It is well established that for a given number of S-R alternatives, increasing
S-R compatibility decreases choice RT. This is thought to be the effect of
the relative “difficulty” of information processing in the response selection
stage, where the more natural linkages between compatible stimuli and
responses lead to faster resolution of uncertainty and thus to shorter choice
RTs. The general rules regarding the number of possible stimuli and choice
RT still apply to incompatible S-R arrangements, however.
Population Stereotypes
There are many types of S-R compatibility other than just spatial mapping.
For example, we tend to turn dials clockwise in order to increase the
loudness of an auditory source or the speed of a fan. In North America we
move a light-switch upward to turn the light on; in Europe, this relationship
is reversed. However, in these cases it is more difficult to argue that the
mapping of the stimulus and response represents a naturally existing
relationship, and not a purely arbitrary one. Instead, the likely association is
a learned one—we sometimes act habitually due to specific cultural
learning, referred to as population stereotypes.
Some colors represent common population stereotypes. Red is often
associated with stop or danger, green with go or safety. Traffic lights exploit
this relationship, as do many other lights in our environment. The small
LEDs (light-emitting diodes) on our coffee machines are red while the
coffee is being brewed, and they turn to green when the coffee is ready to
drink. Once again, however, we tend not to pay attention to these stereotypes
in our day-to-day activities unless the expected relationship is violated.
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Learned associations such as flipping a switch down to turn off a light are
often performed without awareness until our expectations are violated.
Focus on Application 2.1
Light Switches
Stimulus–response (S-R) compatibility and population stereotypes
are a very large part of our daily existence. We usually take notice
of them only in situations in which unexpected issues arise. One
common example occurs when you walk into a room and flip a
switch to turn on a light or a bank of lights. In North American
culture (but not in some other parts of the world), we turn lights on
by flipping the light switch up; moving it down turns the lights off.
We act accordingly when we enter a room and tend not to give the
action a moment’s thought. However, a light switch that has been
installed upside down will bring the issue to our conscious
attention.
A similar issue occurs with respect to the spatial organization of
switches in relation to the spatial locations of the lights in the room
that they control. Suppose you enter a room that has a light near to
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you, one in the middle of the room, and one in the far corner of the
room, controlled by three switches on a switch panel located on the
wall near the door you use to enter the room. Which switch would
you flip to turn the middle light on? A panel that has been
“compatibly mapped” will have the nearest switch control the
nearest light, the middle switch control the light in the middle of the
room, and so on. But how often have you been tricked into turning
on the wrong light because of an incompatible light-to-switch
mapping? Once again, the issue mainly comes to our attention when
the unexpected occurs.
The examples could go on and on. Why is the brake pedal always to
the left of the accelerator pedal in cars? Why does the CD system in
your car have larger numbers assigned to later tracks on a CD? Why
does your car always increase its speed when the accelerator is
pushed down? In all of these examples, the designer who came up
with the very first automobile accelerator pedal, a CD numbering
system, or steering wheel logic could have done it either way. Now,
however, we all have had sufficient benefit of experience with a
particular organization that has become a population stereotype.
Imagine the bother and wasted movements—or, indeed, the
dangerousness—of the system designed with some other stimulus–
response relationship.
Amount of Practice
Highly practiced performers can overcome the disadvantages of low S-R
compatibility, such as the highly experienced racing sailor who doesn’t even
have to think to move the tiller to the right when the boat needs to turn left.
Research shows that two major factors affecting choice RT are (a) the
nature or the amount of practice or both, and (b) stimulus–response
compatibility. For a given number of stimulus–response alternatives, the
higher the level of practice, generally the shorter the RT will be. Overall,
practice reduces the steepness of the increase in RT as the number of
stimulus–response alternatives increases. This means that there is only a
small effect of practice on simple RT but there are very large effects of
practice on choice RT. With extremely large amounts of practice, very high-
level performers can produce reactions that approach automatic processing;
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these reactions are very fast and are slowed little, if at all, as the number of
S-R alternatives is increased further.
This fits well with practical experience. To a beginning driver, the
connection between the presentation of a red light to stop and the response
of pressing the brake pedal is very clumsy. However, after thousands of
hours of driving practice, the link between the red light and the brake pedal
becomes extremely natural, leading almost automatically to the movement.
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Practice can overcome the processing delays caused by low stimulus-
response compatibility. For example, a skilled sailor knows to move the
tiller the opposite direction the boat needs to turn.
Anticipation to Minimize Delays
One fundamental way performers cope with long RT delays is to anticipate.
Typically, a highly skilled performer predicts what is going to happen in the
environment and when it will occur, and then can perform various
information-processing activities in advance of the stimulus. The defensive
lineman in American football predicts that the offensive team will try a
running play, so he anticipates this and moves quickly to stop the play for a
big loss. An experienced long-haul truck driver knows that many car drivers
do not understand the stopping distance of an 18-wheeled vehicle and makes
an evasive lane change to avoid a slow-moving car and a potential accident.
Highly skilled people know what stimuli are likely to be presented, where
they will appear, and when they will occur, so these people can predict the
required actions to take. Armed with this information, a performer can
organize movements in advance, completing some or all of the information-
processing activities usually conducted during the response selection or
movement programming stage. This allows the performer to initiate the
movement much earlier or at a time consonant with the movements of the
environment, as in predicting where and when a pitched ball will arrive at
the plate so that it can be struck effectively with a well-timed bat swing.
Because of these capabilities to anticipate, skilled performers seem to
behave almost as if they had “all the time they need,” without being rushed
to respond to stimuli using the reaction-time processes previously
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discussed.
Types of Anticipation
Anticipation can occur in different ways. Two closely related concepts are
event anticipation and spatial anticipation. For example, two important
shots in badminton are the “clear” and “drop” shots. The clear shot is high
and long and sends the opponent to the back of the court. The drop shot is
intended to land only just on the other side of the net, which brings the
opponent to the front of the court. Anticipation, in badminton, involves
predicting the type of shot your opponent will hit and being in the correct
position to return the shot when it is hit. In other situations, it might be
obvious what is going to occur and where, but there might be uncertainty
about when it will occur, as in anticipating the snap of the ball in American
football. This is usually called temporal anticipation. Although there is a
strong advantage in knowing when some event will occur, not being able to
predict what will occur prevents the performer from organizing the
movement completely in advance.
Focus on Application 2.2
Strategies for Anticipating
The effective gains made when players anticipate correctly, coupled
with the large losses when players anticipate incorrectly, produce
important strategic elements in many rapid sport activities. One
strategy is to do everything to prevent your opponent from
anticipating correctly. A way to do this is to be as unpredictable as
possible in deciding where and when certain actions are made so
that the opponent cannot anticipate effectively. The opponent who
anticipates incorrectly too often will be forced to switch to a
strategy of merely reacting, which is clearly slower and less
effective than anticipating.
Another important strategy is to allow your opponent to anticipate
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but then to make the movement essentially “opposite to” the one
anticipated. A racquetball player moves (and with the proper body
language) as if to make a soft “dink shot” near the front wall,
causing the opponent to move forward quickly. Then, as part of the
plan, the anticipated dink suddenly turns out to be a hard, passing
shot that has the opponent badly out of position. Such a strategy is a
large part of almost every rapid sport. It is dependent on the fact that
if you can lure your opponent into anticipation, you have the
advantage because of the large costs of false anticipation—that is,
taking the opponent out of the optimal position or requiring the
opponent to generate an entirely new action.
Benefits of Anticipation
If the defensive lineman in American football can predict what play will be
run (event anticipation), as well as when the ball snap will occur (temporal
anticipation), he can initiate his movement simultaneously with the snap of
the ball. In many respects, a correct anticipation will result in the
processing lag equivalent to a “RT” equal to 0 ms and he can start the action
simultaneously with the signal, or even before it in certain circumstances,
and it is likely to be very effective. Effective anticipation is not always easy
because it requires the performer to have a great deal of knowledge about
the opponent’s tendencies in various circumstances.
Several factors affect the capability to predict effectively. One is the
regularity of the events. For example, if our racquetball opponent always
serves the ball to our (weak) backhand side, we can predict this event and
counter it in various ways. Clearly, the capability to anticipate would be
minimized if three or four different serves were randomly used instead.
Similarly, if the American football quarterback always has the ball snapped
on the second of two rhythmical verbal signals, the defensive team can
anticipate the critical event and be highly prepared for it. Varying the timing
of the snap signals keeps the defensive team from anticipating temporally,
yet still allows the quarterback’s offensive teammates to anticipate both
temporally and spatially (as they have learned what is to be done and when
in the huddle). The goals here are for the offensive team to respond as a
single unit to the snap count and to allow the defensive team no capability to
anticipate. This provides the offense the greatest relative advantage.
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Focus on Research 2.3
Assessing Anticipation Skills
What skills separate an expert from a novice? Most would agree
that an expert batter in baseball or cricket possesses motor skills
that are more accurate, less variable, and generally more efficient
than those of a lesser-skilled batter. The expert’s motor skills are
simply “better” and more finely tuned. For some skills, however,
and most importantly for open skills, researchers have shown that
experts also have a large advantage in perceptual anticipation
(anticipating the movements of objects in the outside world). How
do the researchers know this?
One of the frequently used experimental techniques is to show
edited video clips of athletes in action and to ask the viewer to
make certain predictions. For example, a video of a baseball pitcher
might be edited such that a certain body part (e.g., the pitching arm)
has been blocked from the viewer’s vision. Presumably, editing out
a body part that is critical to determining what type of pitch is being
thrown (e.g., a fastball or a curveball) would interfere with such an
anticipation only if the athlete were using that perceptual
information. Another method of video editing is to freeze the display
at certain time points in the action. Presumably, a more skilled
viewer would be able to pick up more information in an earlier
frozen frame than a less-skilled viewer. The idea that underlies both
of these “occlusion” edit methods is to discover what types of
information are being used by more highly skilled performers, and
how much earlier in time this information is useful for making
anticipatory judgments.
Exploring Further
1. What is an eye tracker and how is this equipment used to
assess the “quiet-eye” effect?
2. What are point-light displays and how are they used in
research to assess biological motion?
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Costs of Anticipation
There are several strong advantages to anticipation; but, as with most
strategies for trying to gain an advantage, it comes with risks. The primary
disadvantage occurs when the anticipated action is not what actually
happens. In American football again, if the defensive lineman anticipates
that the snap will occur on the second sound but the quarterback takes the
snap on the third sound, the lineman could move too early, incurring a
penalty for his team. In a similar way, when anticipating that an opponent
will hit the ball to the left side of the court, a tennis player will move in that
direction before the shot is hit, but is at risk of losing the point if the shot is
actually hit to the right. Clearly, anticipating correctly can result in many
benefits, but the costs of anticipating incorrectly can be disastrous.
Earlier, we discussed the idea that anticipating allows various information-
processing activities to take place in advance so they do not have to occur
after the reaction stimulus is presented. Suppose that a performer has gone
through these preparatory processes but now the events in the environment
change. The information processing “costs” in this case are actually
magnified. First the performer must inhibit, or unprepare, the already
prepared (falsely anticipated) action, and this process will require time to
complete. Then the correct action must be prepared and initiated, which
extends the processing delay even further. Thus, while a correct anticipation
might reduce the lag period to essentially 0 ms, an incorrect anticipation
will require more processing activities, and longer delay, compared to a
response to a neutral or unanticipated event.
An additional problem occurs if the incorrect anticipation has resulted in a
movement, as you have seen many times in sport events. As before, the
performer still has the problem of inhibiting the incorrect action and
preparing the correct one. But there can be an additional problem: The
inappropriate action might be in the incorrect direction, taking the person
farther from the best location and producing a biomechanical disadvantage
because of her wrong direction. This makes the corrective action even more
difficult, “costly,” and time-consuming to overcome.
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A skilled defensive lineman may be able to anticipate the timing of the snap
and the play about to be run, reducing his effective reaction time to near
zero.
Memory Systems
We change tacks here slightly to discuss an important concept in thinking
about skills, namely memory, which is usually seen simply as the storage of
the results of the various information-processing activities discussed so far.
The various types of memory and their characteristics will be useful later
during the discussion of several aspects of human performance. First we
consider three distinct memory systems involved in movement control:
short-term sensory store, short-term memory, and long-term memory.
Short-Term Sensory Store
The briefest of all memories, the short-term sensory store (STSS), is
thought to retain information for a very short period of time. We rely on this
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memory store during almost all of our daily activities whenever we see,
hear, or feel things. For example, after spotting the milk container in the
refrigerator, we use that brief visual memory to reach for it as we then shift
the eyes to a search for something else. Visual information is taken in by the
eyes and acoustic information by the ears; and the aftereffects of movements
are represented briefly as anticipated information. How long does it last?
Early research by Sperling (1960) suggested that visual information may
last no more than about 1 s in STSS. The basic idea is that STSS is
responsible for storing vast amounts of sensory information only long
enough for some of it to be abstracted and further processed (in short-term
memory, STM). The STSS is not considered to be sustained by attention—it
is simply a brief “holding cell” for sensory information.
Short-Term Memory
Short-term memory (STM), which researchers sometimes call “working
memory,” is a temporary holding place for information, such as a phone
number given to you verbally. Unless we repeat the item we all know that
this phone number will be lost from memory in a short period of time
(probably within a few minutes at most). Information requires rehearsal as
the process by which we keep from losing information from STM.
Repeating the phone number over and over to yourself, either silently or out
loud, is a process of rehearsing the information and thereby keeping it
available in STM.
For example, subjects in a study by Peterson and Peterson (1959) were
presented with verbal information (a three-letter trigram), and were
prevented from saying it over and over again by having them count
backward by 3’s from a two digit specified number. Within about 10 s, the
probability of recalling the trigram successfully was less than .20 and it was
below .10 after about 20 s. Thus, verbal information (eight items) in STM is
retained longer than in STSS but was still in lost quite rapidly when not
given sustained attention.
A study similar to Peterson and Peterson’s, but using motor skills, was
conducted by Adams and Dijkstra (1966) to study the STM of movement
information. Blindfolded subjects moved a slide on a trackway from a start
position to a mechanical stop and tried to repeat this movement (with the
stop removed) after various periods of time. The results of the Adams–
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Dijkstra study are presented in figure 2.9. In this experiment, memory loss is
represented by increases in error over time. As in the Peterson and Peterson
study, rapid forgetting occurred during the first 20 s of the retention period,
with further decreases occurring over the next 60 s. This evidence suggests
that information can be retained for a period of time that is much longer than
STSS but is subject to forgetting if not given sustained attention.
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Figure 2.9 Error in recall of a blind positioning movement increases
rapidly over different retention interval lengths (from Adams & Dijkstra,
1966).
Reprinted by permission from Adams and Dijkstra 1966.
Long-Term Memory
Long-term memory (LTM) contains very well-learned information that has
been collected over a lifetime. Experiments show that LTM must be
essentially limitless in capacity, as indicated by the vast amount of
information that can be stored for very long periods of time. Such
information might never be forgotten: You never seem to forget how to ride
a bicycle or throw a ball, even after many decades of no practice. Even an
apparent loss of information from memory, such as someone’s name or your
old phone number, may just be due to a temporary inaccessibility of the
stored information. For example, you might recognize a person’s name if
someone else mentions it—suggesting that the memory was there but that
retrieval of it posed a problem. The items stored in LTM are thought to be
very abstract, with information coded by elaborate connections to other
stored information and by imagery, sounds, smells, and the like, which
neuroscientists are slowly beginning to understand more completely.
Essentially, a vast amount of information can be stored in LTM by
processing in STM (rehearsal, connecting the information to other
information, and so on), so LTM storage is generally effortful. To say that
someone has learned something means that information was processed in
some way from STM to LTM. This also applies to movement skills, with
motor programs for action (discussed in chapter 5) stored in LTM for later
execution. For many motor skills, particularly continuous ones such as
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riding a bicycle or swimming, evidence and common experience suggest
almost perfect retention after years, even decades, without intervening
practice; this is quite contrary to the forgetting seen with well-learned
verbal and cognitive skills (e.g., foreign language vocabulary). However,
discrete skills, such as throwing or gymnastics stunts, are more easily
forgotten. More on retention is presented in chapter 9.
Summary
The human motor system can be thought of as a processor of information—
for motor skills, information is received from the various sense organs, is
processed through various stages, and is output as movements. The system
has three main stages: a stimulus identification stage, which detects the
nature of environmental information; a response selection stage, which
resolves uncertainty about what action should be made; and a movement
programming stage, which organizes the motor system for action. Reaction
time is an important measure of information-processing speed. Its duration
is strongly affected by stimulus–response alternatives (described by Hick’s
Law), by the “naturalness” of the relationship between stimuli and their
associated movements (stimulus–response compatibility), and by
anticipation of the upcoming events. Three memory systems are described: a
brief store (STSS) holds sensory information for a few seconds at most; a
short-term memory (STM) that is capable of holding about eight items of
information for longer periods of time; but these items last only as long as
can be maintained by attention (about 30 seconds without attention); and a
long-term memory (LTM), which is capable of holding information in
permanent store for years.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 2.1: Identify the stage of information processing in which
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each of a list of actions occurs.
Activity 2.2: Arrange the stages of information processing in the
correct order.
Activity 2.3: Review the memory systems by matching each with its
definition.
Principles-to-Application Exercise
Activity 2.4: The principles-to-application exercise for this chapter
prompts you to choose a skill you can perform or are learning how to
perform. You will describe the skill, including the goal of the movement and
the basic actions involved. You will also select someone else to perform
this skill and describe one processing activity for each stage of information
processing and one factor that might influence making decisions about the
skill.
Check Your Understanding
1. Describe the information-processing activities that might occur in the
stimulus identification, response selection, and movement
programming stages for a hockey goalie in a game and for a kayaker
navigating a set of rapids.
2. Describe and provide an example where spatial anticipation is
important to a sport outcome, and describe and provide an example
where temporal anticipation is important to a sport outcome.
3. Provide an example of stimulus-response compatibility and an example
of stimulus-response incompatibility that you have encountered today.
4. Three memory systems are involved in the learning process. The short-
term sensory store (STSS)’s major role is to store large amounts of
sensory information before processing by short-term memory (STM),
which is a temporary holding place for information, which can remain
in STM through rehearsal. Information in STM can be processed in
order for it to be stored in long-term memory (LTM). This processing
from STM to LTM can be described as learning. Explain how each of
three memory systems (STSS, STM, and LTM) is involved in learning
a new dance routine. Provide examples of specific information that
would be processed.
5. Anticipation can play a role in many contexts. One activity where the
success or failure of anticipation can be particularly clear is racket
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sports, where players must anticipate the next shot of the opponent.
Discuss how anticipation in a game of squash can be both beneficial
and harmful, depending on the situation. What factors can affect the
outcome of anticipation?
Suggestions for Further Reading
Many good references can be found on the information-processing approach
in psychology, including work by Lachman, Lachman, and Butterfield
(1979) and Sternberg (1989). Marteniuk (1976) applied this approach to
thinking about motor-skills research. Welford’s (1980) book on reaction
times provides a good overview of the various applications of this method.
Rasmussen (1986) discusses many applications of information processing.
And Adams’ (1976) book remains an excellent source for discussions of
memory, with particular relevance to motor skills. See the reference list for
these additional sources.
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Chapter 3
Attention and Performance
Limitations on Information Processing
Chapter Outline
What Is Attention?
Limitations in Stimulus Identification
Limitations in Response Selection
Limitations in Movement Programming
Decision Making Under Stress
Summary
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Chapter Objectives
Chapter 3 describes the role of attention as a limiting factor in
human performance. This chapter will help you to understand
attention and its various properties and definitions,
attention as a limitation in the capacity to process information,
attention as a limitation in the capability to perform actions,
and
performance under conditions of increased stress.
Key Terms
arousal
attention
automatic processing
choking
cocktail-party effect
double stimulation paradigm
external focus of attention
hypervigilance
inattention blindness
internal focus of attention
inverted-U principle
“looked-but-failed-to-see” accidents
movement programming
perceptual narrowing
probe-task technique
psychological refractory period (PRP)
response selection
stimulus identification
stimulus-onset asynchrony (SOA)
sustained attention
unintended acceleration
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The task of driving to the store illustrates why the topic of attention is so
complex. Consider the following:
You are driving to the supermarket, but unfortunately, you forgot your
shopping list, which was on the refrigerator door, and you are trying to
reconstruct what might have been on it—eggs, milk, peanut butter,
hmm, no, not milk, I bought some yesterday . . .
You receive a phone call. It’s your roommate, and she asks you to pick
up some pasta for dinner, as well as some green peppers.
There’s an intersection ahead; the light has been green for quite a while
and may turn to amber at any moment, so better be ready . . .
The temperature is pretty warm today; maybe some air conditioning is
needed. Which of these controls do I use?
What was it that my roommate asked me to get? Think hard, I’ll
remember—pasta and . . . green onions, yeah, that’s it.
There’s an amber light; do I have time to make a safe stop, or should I
accelerate through the intersection?
And on it goes. In a very short time this scenario has illustrated the nature of
the concept of attention and factors that influence it. Attention appears to be
limited in that only a certain amount of information-processing capacity
seems to exist. If it is overloaded, much information can be missed. Also,
attention appears to be serial in that it seems to focus first on one thing, then
on another; only with great difficulty (if at all) can we focus attention on two
things at the same time. Sometimes attention is directed to external sensory
events (what other drivers are doing). Sometimes it is focused on internal
mental operations (trying to remember the items on the shopping list). And
sometimes it is focused on internal sensory information (sensations from the
muscles and the joints). In addition, there are the difficulties in doing two
tasks at the same time, such as talking on the cell phone while driving.
Attention is many different things (see also William James’ quote in Focus
on Application 3.1).
Focus on Application 3.1
William James on Attention
Consider the following statement, made over a century ago by the
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famous psychologist William James (1890):
Everyone knows what attention is. It is the taking possession
by the mind, in clear and vivid form, of one out of what seem
several simultaneously possible objects or trains of thought.
Focalization, concentration of consciousness, is of its essence.
It implies withdrawal from some things in order to deal
effectively with others.
Despite his questionable comment that “everyone knows what
attention is,” James goes on to reveal the complexity of the topic. In
this statement James suggests that attention takes on at least three
different roles. First, James says that attention involves “taking
possession by the mind . . . of one out of . . . several . . . trains of
thought.” By this statement, James suggests that attention involves a
process of selection in which the individual is juggling several
ongoing lines of thinking, each competing for current resources (or
consciousness). Second, he states that attention involves
“focalization . . . of consciousness.” This is a subtle, but important,
difference from the first idea. Attention involves an active, directive
process of current thinking, presumably one that is dynamic—that
changes with the changing needs of the performer. And last, James
suggests that attention requires “withdrawal from some things in
order to deal effectively with others.” Here, James is again implying
something subtly different—that there is a limit to the amount of
attention that can be allocated. The performer must be able to shift
the amount of attention allocation as changes occur in the demands
of the task.
As we will discuss in this chapter, James’ intuitions and
descriptions about attention were quite accurate.
What Is Attention?
All of the preceding examples represent different uses of attention. But what
is attention? In our view, attention is a resource (or “pool” of slightly
different resources) that is available and that can be used for various
purposes. In many respects, attention is like a bank account, which contains
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financial resources that allow us to perform activities of daily living. The
ways in which these attentional resources are allocated define how we use
attention.
A way to think of attention is related to the limitations in doing two things at
the same time. Psychologists have approached the limitations in information
processing by first trying to understand the separate requirements of tasks
that interfere with each other. The idea has been that, if two tasks interfere
with each other, then they both demand some access to the limited capacity
to process information; that is, they both require attention. Figure 3.1
illustrates this idea. Both circles represent the total capacity that is
available to be allocated. In the bank account example, this would be the
total of all of the money that we have available. Figure 3.1 illustrates how
the fixed amount of attention (capacity resource) must be divided between a
“main” task and some secondary task. When the main task is relatively
“simple” and does not require very much attention, as depicted in figure
3.1a, then more attentional capacity remains for other tasks.
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Figure 3.1 Attention remaining for a secondary task is reduced when the
primary task is more complex (b) compared to when the primary task is
simple (a).
Based on Posner and Keele 1969.
This notion has strong implications for understanding skilled performance.
In many skills, there is an overwhelming amount of information that could be
processed, some of it relevant to performance (e.g., what other drivers are
doing, as in the earlier example) and some of it irrelevant to performance
(e.g., the song that is playing on the radio). The performer’s problem is how
to cope with this potential overload. The performer must learn what to
attend to and when, and must shift attention skillfully between events in the
environment, monitoring and correcting her own actions, planning future
actions, and doing many other processes that compete for the limited
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resources of attentional capacity.
The following sections turn to the question of when and under what
conditions tasks interfere with each other. One way to understand the kinds
of multiple-task interference involves the stages of information processing
described in chapter 2—stimulus identification, response selection, and
movement programming (see figure 2.2). It is useful to ask whether, within
each stage, there is interference between two processes competing for the
available capacity. There is evidence that some processing can occur “in
parallel” (that is, without attention) in the stimulus identification stage, but
that much less parallel processing occurs in the response selection stage.
Finally, considerable interference often exists among tasks in the movement
programming stage.
Limitations in Stimulus Identification
Some evidence suggests that information processing in the peripheral,
sensory stages of the information-processing model can be done in parallel.
With parallel processing, two or more streams of information can enter the
system at the same time and can be processed together without interfering
with each other. For other tasks, however, the capacity of information
exceeds the limits of attention, requiring that we switch attention between
competing sources. Still other research reveals that sustained attention
tends to wane after extended periods of information processing. These
influences on sensory information processing are discussed in the next
sections.
Parallel Processing
Information from different aspects of the visual display, such as the color
and the shape of objects, can apparently be processed together without
interference. Evidence for parallel processing in stimulus identification
comes from an analysis of the Stroop effect (Stroop, 1935; MacLeod, 1991).
Imagine that you are a research subject, asked to respond as quickly as
possible by naming the color of the ink in which words are printed, as in
figure 3.2. In some cases, the words printed have no semantic relationship
to the colors in which they are printed, as in list a. In other cases, as in list
b, the ink colors compete with the names of the words themselves. The
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Stroop effect is the tendency for the set of stimuli on the right to require
longer completion times to name the colors than those on the left. Evidence
suggests that the color of the ink and the word that the ink spells are initially
processed together and in parallel. The interference is caused later on by the
two stimuli competing for different responses.
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Figure 3.2 The Stroop effect. Time yourself while naming the colors of the
words printed in each list.
There is also considerable parallel processing of the sensory signals from
the muscles and joints associated with posture and locomotion, and people
seem to handle these together and without much awareness. The idea is that,
considering the processes occurring in the stimulus identification stage,
some sensory information can be processed in parallel and without much
interference—that is, without attention.
Cherry (1953) developed the “dichotic listening” task to investigate these
ideas. Subjects wore headphones, such that separate streams of information
were directed to each ear; they were told to pay attention to one channel and
to disregard the other. After a short period of time the subjects removed the
headphones and were asked to repeat the information that had been
presented to the “attended” ear. Unexpectedly, they were also asked to
reveal what had been presented in the “unattended” ear. Subjects were
largely unable to remember the information from the unattended ear,
although they could identify some “surface features” of the message, such as
the speaker’s gender and loudness of the voice.
Cherry (1953) called this the cocktail-party effect, and it represents
another example of parallel processing. The effect is this: Imagine yourself
in a large room at a party, in which many groups of people are engaged in
conversations. There is considerable noise surrounding the conversation in
which you are engaged—loud music, other conversations, and so on—yet
you can still engage in a conversation successfully, effectively shutting out
the background noise. But not all information is blocked. You can be
engaged in an ongoing conversation and suddenly hear your name being
spoken in a conversation in which you are not involved at all. Even though
you have effectively “shut out” that background “noise,” some of it must
have been processed in parallel in the stimulus identification stage in order
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that you could hear your own name. The cocktail-party effect illustrates that
even some “unattended” features of sensory processing are processed in
parallel with other “attended” information in the very early stages of
sensory processing.
Inattention Blindness
The previous section illustrates how stimulus information can be processed
in parallel, even despite efforts to block it out. And yet, sometimes a very
simple, goal-directed visual search, such as looking for a specific entrance
or building number, seems to absorb our attention, making us “blind” to
other things. Some remarkable findings by several research groups have
shown that we can miss seemingly obvious features in our environment
when we are engaged in attentive visual search. For example, research
participants in Simons and Chabris’ (1999) study watched a video of six
people who passed basketballs among themselves while all players were
constantly in motion. Three players on one “team” were dressed in jeans
and black t-shirts, and three in jeans and white t-shirts were on the other
“team.” Each team passed their ball only to players on their own team. The
experimental subjects watched a video of this activity; their only task was to
count the number of passes made by the team dressed in white. You can
view the experimental setup at www.youtube.com/watch?
v=vJG698U2Mvo.
After the 30 s video ended, the experimenter asked the participants for the
answer to the number of passes made, and followed this by asking if they
had seen anything unusual. Only about half of all subjects tested responded
by saying that they had seen someone dressed in a gorilla suit walk through
the group of players and pound his chest (gorilla style) about halfway
through the video. Remarkably, the other half did not report seeing the
“gorilla,” even though it had been in plain view (see figure 3.3). When the
video was replayed to them, these subjects were shocked at having missed
the obvious “gorilla.”
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Figure 3.3 The inattention-blindness effect. Subjects (observers of the
video) counted the number of basketball passes made among the subjects in
white t-shirts. Later, about half of these subjects did not recall seeing the
“gorilla” walk through the middle of the group.
Reprinted by permission from Simons and Chabis 1999.
This phenomenon, which has been given the label “inattention blindness,”
was originally discovered by Neisser and Becklen (1975), who used a
similar task, but with a woman with a parasol instead of a person in a
gorilla suit. The effect has been studied vigorously since the Neisser–
Becklen findings were published.
Missing the rather obvious “gorilla” is likely to occur only under a
restricted set of circumstances, however—when the viewer is engaged in a
specific search task. Watching the video under no specific search
instructions, or being asked to count the passes made by the team in black t-
shirts, produced very few cases of this “inattention blindness” (Simons &
Chabris, 1999). Watching the video a second time, or thinking that
something unexpected might occur, also eliminates the effect. Furley and
colleagues (2010) found that highly skilled basketball players were also
less likely to miss the “gorilla” than were low-skilled players.
Despite these limitations in the inattention-blindness effect, findings reveal
that it is not restricted to watching videos but can be demonstrated in action
events. Other “field” research studies reveal that people who are engaged in
attention-demanding tasks are likely to miss quite obvious things such as a
change in a person to whom they are giving directions (Simons & Levin,
1998) or a person on a unicycle dressed in a clown suit on a university
campus (Hyman et al., 2010). In fact, a number of automobile accidents
seem linked to this phenomenon. These have been given the label “looked-
but-failed-to-see” accidents; here, even though there is evidence that the
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driver looked, he still drove into the path of a pedestrian, bicycle, or
another vehicle, causing an accident (Brown, 2005; Langham et al., 2002);
the driver simply did not “see” the other object coming, even though he
“looked” at it.
Inattention blindness explains why we momentarily fail to recognize a
friend we happen to encounter while looking for someone else in a crowd.
Our search has been directed toward a person with specific visual
characteristics, and we become temporally oblivious to people who don’t
match those search criteria. Magicians and pickpocket thieves use
essentially the same concept. A focus by the “victim” on one specific detail,
especially if attention has been directed there by the magician or thief,
causes the victim to be “blind” to the other person’s intentions (Stephen et
al., 2008).
Sustained Attention
World War II generated a push in research on sustained attention in order to
better understand the limits of radar operators who were on the lookout for
enemy aircraft. Mackworth (1948), who was a leader in this research,
devised a task in which subjects would watch the pointer of a clock-like
apparatus jumping second by second. But, occasionally, after long, irregular
(unpredictable) intervals, the pointer would jump by 2 s. Detection of these
latter jumps was found to be reliable for the first 30 min of work but
declined dramatically thereafter. These were termed “vigilance
decrements,” or decreases in vigilance.
A number of factors are known to affect vigilance, or sustained attention;
these include the operator’s motivation, arousal, and, of course, fatigue
(clearly related to the accumulated amount of time in performing the task).
Environmental factors, such as temperature and noise, are also known to
affect sustained attention (see Davies & Parasuraman, 1982, for more).
After a period of time, the task of concentrating on a single target of our
attention becomes a progressively more difficult chore.
These effects are quite obvious on a daily basis, as they influence many
occupations in which vigilance is a necessity. For example, consider the
task of working as a security agent at a busy airport. Before people can
board their aircraft, they must go through a series of security checks, which
includes an X-ray scan of their carry-on luggage. Each X-rayed piece of
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luggage must undergo a visual search by a human operator, who looks for
the presence of objects that are not permitted on board (figure 3.4). The
obvious ones (guns, knives) should be relatively easy to spot. But the
problems faced by the security agent include trying to decide whether
something that looks like a banned object is actually one or not, which
slows the checkpoint process. Experiments on visual search tasks reveal
that the number and similarity of distracting objects play an important role
in the success of the search. The agent’s task is made even more difficult by
the fact that finding banned objects is (thankfully) a rare occurrence—so
sustained attention to the task is of primary concern (Wolfe et al., 2005).
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Figure 3.4 An airport security guard is required to sustain effort over long
periods of time.
Limitations in Response Selection
Interference between tasks is never more obvious than when the performer
must perform two actions simultaneously, with each task requiring mental
operations, such as answering a telephone call while pouring water into a
coffee maker. Both activities are thought to be done during response
selection because they require that choices be made among several possible
alternative responses—which hands to use to pour the water and pick up the
telephone, which ear to listen with, monitoring the water so as to not spill
any and to not pour too much, and so on. These activities are governed by
controlled processing, which is thought to be (a) slow; (b) attention
demanding, with interference caused by competing processing; (c) serially
organized, with a given processing task coming before or after other
processing tasks; and (d) volitional, easily halted or avoided altogether.
Relatively effortful, controlled processing is a very large part of conscious
information-processing activities, involving mental operations among
relatively poorly learned, or even completely novel, activities. Having to
perform two information-processing tasks together can completely disrupt
both tasks.
A separate, very different kind of information processing seems to occur in
highly practiced people. Some years ago, Peter Vidmar, a 1984 Olympic
silver medalist in gymnastics, claimed that before mounting the apparatus,
he paid attention just to the first move in his routine—the mount; the
remainder of the elements occurred more or less “automatically,” that is,
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without requiring attention (Vidmar, 1984). These later elements required
only minor adjustments while being run off, allowing Vidmar to focus on
such higher-order aspects of his routine as style and form. It is as if much of
the information processing necessary in this complex gymnastics routine
was fundamentally different from controlled processing, not requiring very
much attention. This way of dealing with information, automatic
processing, is (a) fast; (b) not attention demanding, in that such processes
do not generate (very much) interference with other tasks; (c) organized in
parallel, occurring together with other processing tasks; and (d) involuntary,
often unavoidable.
Automatic information processing is thought to be the result of an enormous
amount of practice. Your capability to quickly recognize collections of
letters as the words you are reading now has come from years of practice.
Many years ago, Bryan and Harter (1897, 1899) conducted studies of
telegraph operators, whose job it was to identify (receive) and send Morse
code—which is composed of varying periods of brief noise (called dots
and dashes) that combine to define letters and numbers. Bryan and Harter
found that telegraph operators focused on individual letters at the earliest
stage of learning but that as they gained proficiency, they processed Morse
code as combinations of letters, then later as whole words, and even in
phrases.
The effectiveness of automatic processing has strong implications not only
for many everyday tasks (like reading) but also for high-level performance
skills. If a task is performed automatically, many important information-
processing activities can be produced not only quickly but in parallel with
other, simultaneous tasks and without disrupting performance. It is as if
certain stages (e.g., response selection) are bypassed altogether.
Costs and Benefits of Automaticity
Automatic performances, whose benefits are nearly obvious, are related to
processing information in parallel, quickly, and without interference from
other processing tasks. For example, after much practice, high-level
volleyball players can read their opponents’ movement patterns
automatically to mean that the ball will be spiked from, say, their left side
(e.g., see Allard & Burnett, 1985). But what if, after consistently producing
a pattern leading to a play to the left, the opposing team uses the same
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pattern leading to a play to the right? The defenders’ automatic processing of
the pattern would lead to a quick decision and a movement to counter the
expected play, a response that would be hopeless as far as combating the
actual play is concerned.
Clearly, then, automaticity can have drawbacks, as well as benefits.
Although very fast processing is effective when the environment is stable
and predictable, it can lead to terrible errors when the environment (or an
opponent) changes the action at the last moment. Thus, automaticity seems
most effective in closed skills, where the environment is relatively
predictable. With open skills, so many more patterns are possible that the
performer must develop an automatic response to each of them; this is
generally possible only after many years of experience.
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Automaticity allows highly skilled athletes to process information about
their opponents’ movements quickly and respond.
Developing Automaticity
How do people develop the capability to process information
automatically? Practice, and lots of it, is a very important ingredient, so you
should not expect to see automaticity develop quickly. Practicing for
automaticity is generally most effective under a “consistent-mapping”
condition, where the response generated is related consistently to a
particular stimulus pattern. For example, the response to a red light during
driving is always to bring the vehicle to a stop. This is in contrast to a
“varied-mapping” condition, where a given stimulus sometimes leads to one
response and sometimes to another response (Schneider & Shiffrin, 1977).
An example is the incredible variety of button layouts on different brands of
TV remote-control units, where a given function (changing the channel)
requires pressing different buttons depending on the brand. The diversity of
such “varied-mapping” conditions makes automatic processing almost
impossible to achieve, and such tasks require considerable controlled
processing to avoid making errors.
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Response Selection and Distracted Driving
Distracted driving is an excellent example involving attention’s limited
capacity. But an important question is: Does distracted driving affect the
response selection stage or the movement programming stage? Laws passed
in many U.S. states and other countries have banned the use of handheld cell
phones during driving. Why? The assumption is that the hand operation of a
cell phone interferes with the operation of a motor vehicle; this argument
lays the blame for the cell phone–driving dual-task deficit as a movement
programming limitation. But the research suggests otherwise, and is quite
conclusive that hands-free and handheld phones are about equally
problematic in exceeding the attentional capacity limits of the driver. The
source of the problem lies in the capacity demanded by the phone
conversation, and not whether the driver is holding on to, looking at, or
manipulating the phone (e.g., Strayer & Johnston, 2001). The physical
actions involved in manipulating the cell phone do not add significantly to
the attention demands required in carrying on a conversation while driving
(see Ishigami & Klein, 2009, for an important review of this research). The
discussion in Focus on Research 3.1 provides more information about the
methods used to understand the attention demands of distracted driving.
Remember, though, that using a cell phone at the same time as performing
other activities can be just as dangerous as talking (or texting) and driving.
For example, in an observational study, Thompson and colleagues (2013)
found that people who texted or talked on a cell phone while they crossed a
busy intersection walked about 20% slower than nondistracted pedestrians.
The texting pedestrians were also more likely than nondistracted
pedestrians to fail to look both ways for oncoming traffic before entering the
intersection.
Focus on Research 3.1
Distracted-Driving Research
Researchers have used varying approaches to understand the effects
of various distractions, such as speaking on a cell phone, on the
control of a vehicle. The obvious problem with doing the most
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logical type of research—using drivers in real traffic situations—is
that it brings other drivers, the research participants, and sometimes
the experimenters themselves into potentially dangerous situations,
which is unethical. So, researchers have devised different methods
to assess the attentional cost of performing various tasks while
driving.
A statistical approach to the specific problem of cell phone use
during driving uses call records of individuals who were involved
in an accident. The findings of one study using this method revealed
that the likelihood of being involved in a car accident increased by
400% when a driver was talking on a cell phone (Redelmeier &
Tibshirani, 1997). These authors also found that the increase in
accident rate while talking on a cell phone during driving is about
the same as for driving with a .08% blood alcohol concentration
(BAC)—the legal definition of driving under the influence [DUI] of
alcohol concentration in some states and countries.
The most common type of experiment involves humans performing
in driving simulators in laboratory environments. Some simulators
provide extremely realistic driving environments, enabling
researchers to have control over the “traffic conditions” and varying
distractions without endangering anyone.
A few experimenters have gone further to create an outdoor
environment, using an actual car and a simulated driving
environment in an otherwise safe area such as a large empty parking
lot. In fact, one of the very first studies of its kind was performed
over 40 years ago, and the authors suggested back then that mobile
phones and automobile drivers were a dangerous mix (Brown,
Tickner, & Simmons, 1969).
Of course, each method has advantages and disadvantages. Call
records involve actual data about calls that have occurred. But, the
co-occurrence of an accident and a call as shown in records says
very little about the state of the individual during the call.
Simulators provide the researcher with excellent control of mental
workloads, ongoing distractions, the timing of critical events, and
the measurement of behaviors. But these are all simulations of a
driving environment, not the real thing. Outdoor environments, in
actual cars but with simulated driving conditions, also have the
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advantage of good experimenter control of events, but, again,
without the reality of an actual driving experience in real traffic. In
the end, the information provided by all types of research activities
together provides researchers with the best answers to these critical
questions about distracted driving.
Exploring Further
1. How might the nature of a particular cell phone conversation
affect a person’s capacity to attend to the task of driving?
2. Do you think that a driver having a discussion with an in-car
passenger would have the same effects on driving as the driver
talking on the cell phone? Why or why not?
Limitations in Movement Programming
As you’ll recall from chapter 2, the movement programming stage is the
third in the sequence of information-processing stages. Here, after the
performer has perceived what the environment will allow, and after having
chosen a response that meets those demands, the performer must still
organize the motor system in order to actually execute the action. In this
stage, the performer must make critical adjustments that occur at various
levels (e.g., in the limbs, muscles, and spinal cord). These adjustments take
time, of course. A good example is the action of a fencer, who must
preprogram a movement despite having to execute the movement in the face
of a potentially changing environment. In the following example the
programmed action is somewhat complicated, involving a move toward the
center shoulder and then followed by a sudden change in the action.
A fencer moves the foil toward the opponent’s shoulder but then quickly
alters the direction and contacts the waist instead. Often, responding to the
first move, the fake, seems to have interfered with the opponent’s speed of
responding to the second move, and the point is lost. The delay in
responding suggests strong interference between activities in the later stages
of information processing. Specifically, the delay occurs because of
interference to the movement programming stage, which has the task of
organizing the motor system to make the desired movement.
Some of this view comes from considerable research evidence using the so-
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called “double-stimulation paradigm,” where the subject is required to
respond, with separate responses, to each of two stimuli presented very
closely together in time (see Focus on Research 3.2). This paradigm is in
many ways analogous to the problem facing the fencer’s opponent, who must
respond to one move and then another in rapid succession. The delays in
responding occur because of the interference that arises in programming the
first and second movements as rapidly as possible.
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The fencer on the left has faked one move and is now in the process of
making a second move, allowing her to score a point and revealing
limitations in the movement programming stage.
Focus on Research 3.2
The Double-Stimulation Paradigm
Research on the psychological refractory period (PRP) uses the
“double-stimulation paradigm,” in which the subject is asked, for
example, to respond to a tone (Stimulus1) by lifting the right hand
from a key as quickly as possible. A very short time following the
tone a light (Stimulus2) might appear; the subject is to respond by
lifting the left hand from a key as quickly as possible. The
separation between the onsets of the two stimuli, called the
stimulus-onset asynchrony (SOA), might range from zero to a few
hundred milliseconds. Researchers are usually interested in reaction
time (RT) to the second stimulus (RT2) as a function of the SOA.
(See the paradigm timeline shown in figure 3.5a.)
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Figure 3.5 The double-stimulation paradigm (a) and results from
experiment by Davis (1988) (b), showing that RT2 is lengthened
greatly at the shortest SOAs.
Reprinted by permission from Schmidt and Lee 2011; Data from Davis 1959.
The general findings from one study using this paradigm are graphed
in figure 3.5b, where RT2 is plotted as a function of the SOA. The
horizontal line (labeled RT2) is the value of RT2 when the first
stimulus is not presented at all; it represents the “usual” (without
interference) RT to this stimulus using this response. Depending on
the length of the SOA, there is a marked delay in the RT2 while the
first stimulus is being processed. When the SOA is about 50 ms, the
delay is very large, and it can more than double the value of RT2, as
compared to its control value. As the SOA lengthens, the delay in
RT2 decreases, but there is still some delay in producing RT2 even
with SOAs of 200 ms or more. The single-channel hypothesis
(Welford, 1952), which was originally proposed to account for
effects like these, argues that the processing of the first stimulus and
response completely blocks the processing of the second stimulus
and response until such time that the processing of the first stimulus
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and response has been completed. More recent thinking about data
such as these holds that the major delay in RT2 arises from
interference between the movement programming stages of these
actions.
Exploring Further
1. Why is the most important comparison in this paradigm the RT
to the second of two closely spaced stimuli rather than to the
response to the first stimulus?
2. How would the magnitude of the PRP effect be expected to
change depending on the number of choices involved in
responding to the first stimulus?
Psychological Refractory Period
The delay in responding to the second of two closely spaced stimuli is
termed the psychological refractory period (PRP). An important question
concerns how soon a person can switch from (a) making a goal-directed
response to one stimulus to (b) making a different goal-directed response to
a different stimulus. The motor system processes the first stimulus and
generates the first response. Then, if the experimenter presents the second
stimulus during the time the system is processing the first stimulus and its
response, the onset of the second response can be delayed considerably (the
PRP effect).
One explanation for the PRP is that there is a kind of “bottleneck” in the
movement programming stage, and that this stage can organize and initiate
only one action at a time, as diagrammed in figure 3.6. Any other action
must wait until the stage has finished initiating the first. This delay is largest
when the time between stimuli (SOA) is short, because at this time the
movement programming stage has just begun to generate the first response;
this response must be emitted before the stage can begin to generate the
second response. As the SOA increases, more of the first response will
have been prepared by the time the second stimulus is presented, so there is
less delay before the movement programming stage is cleared.
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Figure 3.6 An information-processing bottleneck in the movement
programming stage occurs when two stimuli (S1 and S2) are presented 100
ms apart. In (a), the first stimulus enters the information-processing system.
In (b), the second stimulus is introduced, but it is delayed at the bottleneck
while the first response is programmed. This is similar to what happens in
response to a fake in rapid sports.
One more finding is of interest here. When the SOA is very short, say less
than 40 ms, the motor system responds to the second stimulus in a very
different way. The system responds to the first and second stimulus as if they
were one, which produces both responses simultaneously. In this
phenomenon, termed grouping, the early processing stages presumably
detect both stimuli as a single event and organize a single, more
complicated action in which both limbs respond simultaneously.
The phenomenon of psychological refractoriness just discussed accounts for
many of the underlying processes in faking. In basketball, for example, the
player taking the shot preprograms a single, relatively complex action that
involves a move to begin a shot, a delay to withhold it, and then the actual
shot—all done in rapid succession. The shooter’s movement is organized as
a single unit and is prepared as any other movement would be in the
movement programming stage. However, the defensive player sees only the
first part of this action; this can be thought of as the first stimulus (S1) in the
double-stimulation paradigm, and it triggers the response to block the shot,
which does not occur until later. The processing of the first stimulus leads to
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large delays in responding to the new information that the shot has been
withheld, that it is a fake (as indicated by the fact that the second stimulus,
S2, the actual shot, is now being made). The result is that the first response
(movement to block) cannot be withheld, and it occurs essentially as
originally planned. This creates a very large delay in initiating a second,
corrective response to block the actual shot, which is made at about the
same time that the defensive player is dropping back to the floor after
“taking the fake.” This all makes the shot very easy for the offensive player
and makes the defensive player look a little foolish at the same time.
Some principles of faking emerge from research on psychological
refractoriness. First, the fake—the first response—should be realistic,
distinct, and clear, so the defensive player treats it as an actual shot.
Second, for the effective fake, the single programmed action that contains
both the fake and the actual shot is planned so as to separate the fake
(stimulus 1) and the actual shot (stimulus 2) sufficiently to generate a
relatively large delay for the response to stimulus 2. From the data in figure
3.5, for the fake to be maximally effective, this separation is somewhere
around 60 to 100 ms. If this separation is too short, the defensive player can
ignore the fake and respond instead to the actual shot (grouping). If the
separation is too long, the defender will respond to the second stimulus with
an essentially normal RT, and the shooter will have lost the advantage of the
fake.
The Probe-Task Technique
Some researchers have used a different approach to studying the attention
demanded during the movement programming stage, called the probe-task
technique. Here, the researcher would have the subject perform one task
(called the primary task; it could be either discrete or continuous in nature).
At some strategic point in the performance of the primary task, the
researcher would probe (or test) the attention demanded in the main task by
presenting a secondary task, usually a discrete stimulus, such as tone or light
(the probe stimulus). Now, the subject’s additional task would be to respond
to the stimulus as rapidly as possible with either a manual (e.g., a key press)
or a vocal (e.g., saying “stop”) response, and RT would measure the delay
in responding to the probe. With this strategy, the researcher would use the
RT to the probe as a measure of the attention demanded by the primary task;
a more attention-demanding primary task would result in slower responses
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to the probe stimulus signal than would a primary task that demanded less
attention.
An example of how this technique is used in research is the work of Posner
and Keele (1969). In their study, subjects were asked to make a rapid,
visually-guided aiming movement using a lever, through 135º of rotation to a
target. Subjects made these movements to either large or small targets—the
idea being that smaller targets might require more of the attentional capacity
due the increased precision requirements of aiming. The probe technique
was used to assess attention demands at various points during the movement
—either before movement initiation, at the point of movement initiation, or
at various later points. Trials that assessed RT were also conducted in a
control condition, in which no aiming movement was made.
The results of the Posner and Keele study are presented in figure 3.7. There
are several things of interest here. First, the control-RT value (represented
as the solid line across the figure near 260 ms), was considerably less than
the probe RTs for any of the other data points in the graph. This was
interpreted as indicating that all parts of the aiming movements required
some attention, since RT was elevated relative to the no-movement (control)
condition. Second, the “bowed” nature of both the small-target and large-
target curves in the graph suggests that attention demands were not evenly
distributed throughout the movement. The elevated probe RTs at positions
representing the beginning (0° position) and end (135° position) of the
movement suggested that these were more attention-demanding parts of the
movement than were the middle positions (i.e., the 15°, 45°, 75°, and 105°
positions). Lastly, the probe RT for the small-target curve was generally
larger than for the large-target curve, indicating that movements with greater
precision requirements are more attention demanding than movements with
less precise requirements.
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Figure 3.7 The attention demands as measured by probe RT at various
points during the visually-guided pointing movement to either a small or
large target. The straight line denotes average RT to the probe stimulus
when no pointing movement is being made.
Reprinted by permission from Posner and Keele 1969.
These secondary-task probe techniques are not without their limitations and
problems, however. As we will see later (in chapter 6), producing two
movements at the same time introduces special coordination challenges to
the processing system. If the movements are compatible (e.g., keeping a
rhythm beat with two hands, as in drumming), then two limbs can be
coordinated in such a way that there is no detriment to performance (e.g.,
Helmuth & Ivry, 1996). A problem arises when two or more separate
actions have distinct and incompatible spatial or temporal requirements.
For example, one limb might be required to produce one spatial action
while another produces a different action—such as rubbing your stomach
and patting your head at the same time. Polyrhythms, in which one limb
produces a faster beat than the other limb (e.g., 4:3 rhythms), are an example
of a challenging task because of the incompatible timing requirements.
The problem for the probe technique is that responding manually to a probe
can produce specific interference effects (over and above those associated
with attention) with a manual primary task (McLeod, 1980). In McLeod’s
study, this occurred because the nature of the manual response to the probe
was incompatible with the movement required for the primary task. Thus,
attention demands assessed by the delay in probe RT were “contaminated”
by the competition between movement requirements. McLeod (1980) found
that much less competition occurred between a primary limb task and a
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vocal probe response, for example, and provided a less contaminated
assessment of attention demand. This sort of idea prompted the notion of
“pools” of attention, where a given pool would be related to vocal
responses, and another pool would be related to motor responses. We will
have much more to say about moving two limbs at the same time when we
discuss coordination in chapter 6.
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Producing two movements at the same time introduces a greater challenge if
the movements are incompatible, as when a drummer plays a faster beat
with one hand than with the other.
Focus of Attention During Action
A relatively recent research interest regarding the movement programming
stage concerns a performer’s recommended focus of attention. Would the
performer be better off directing his focus to an internal source, for
example by monitoring the ongoing movement, or would attention be
directed more effectively at an external target, such as an object to be struck
or the intended effect that the action will have on the environment?
Considerable research conducted by Wulf and her colleagues suggests that,
in almost all situations, an external focus of attention results in more
skilled performance than an internal focus of attention. These studies have
revealed very impressive benefits to performance, seen in a wide variety of
laboratory and sport tasks, and for children, adults, and healthy older adults
(see Wulf, 2007, for a review).
One aspect of this research that remains to be clarified is this: At what skill
level would an external focus would be preferred to an internal focus?
Perhaps these internal-focus instructions benefit individuals who are just
beginners, with an external focus preferred for those who have attained
perhaps even just a minimal proficiency (e.g., Beilock et al., 2002; Perkins-
Ceccato, Passmore, & Lee, 2003). We will return to the issue of focus of
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attention when we consider the process of learning in chapter 9.
Another feature to consider about this research is that these effects occur
when one is focused on internal or external targets during skill execution.
For example, an expert gymnast might go through a number of internal and
external thoughts during a preperformance preparation. But, when it comes
time to perform the movement, the research suggests that an external focus
will produce the most skilled results. And, as we discuss later in this
chapter, the consequences for an expert of reverting to an internal focus
during movement execution can be disastrous, and may explain a large
proportion of the famous cases of athletes and musicians who “choked”
under pressure (Beilock, 2010). Skilled athletes who routinely perform with
an external focus of attention sometimes shift their focus under pressure to
movement production (perhaps thinking about how to perform the
movement, or how the movement will feel when it is performed). This shift
in attentional focus can reduce performance quality, leading to further
internalized focus and a heightened anxiety. This downward spiral is one
cause of choking.
Decision Making Under Stress
Arousal, the level of excitement produced under stress, is a common aspect
of skill performance situations. This is certainly true of many athletic
events, where the pressure to win and the threat of losing, as well as crowd
influences, are important sources of emotional arousal for players. The
level of arousal imposed by a situation is an important determinant of
performance, particularly if the performance is dependent on the speed and
accuracy of decision making.
Inverted-U Principle
One can think of arousal as the level of excitement or activation generated in
the central nervous system. For example, low levels of arousal are
associated with sleep-like states, and high levels are associated with the
agitated and extremely alert states found in life-threatening situations. The
influences of arousal level on performance have been studied for many
years. The inverted-U principle (or, theYerkes-Dodson, [1908] law)
represents an early view of the relationship between arousal and
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performance. The idea is that increasing the arousal level generally
enhances performance, but only to a point. Performance quality peaks at
some intermediate value of arousal, and performance actually deteriorates
as the arousal level rises further—hence the inverted-U function.
Support for the application of the inverted-U principle to human motor
performance was provided in a study by Weinberg and Ragan (1978). The
task involved throwing balls at a target, and levels of stress were induced
by (falsely) comparing subjects’ performances to those of other subjects
like them (junior high school boys). In the “high-stress” condition, 90% of
this “comparison” group supposedly performed more accurately than the test
subjects did. The authors assumed, with good justification we think, that
junior high school boys would find this sort of information feedback to be
arousing or stressful. The moderate- and low-stress groups were told that
60% and 30%, respectively, of the comparison group had scored more
accurately than they had. When the subjects performed the task again,
following these stress-inducing false statements, their performances
conformed well to the predictions of the inverted-U principle, as shown in
figure 3.8.
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Figure 3.8 Stress-inducing instructions have effects on performance that are
consistent with the predictions of the inverted-U principle (from Weinberg
& Ragan, 1978).
Reprinted by permission from Schmidt and Lee 2011; Data from Weinberg and Ragan 1978.
The inverted-U principle might be surprising to many who deal with sport
and coaching, because it is generally believed that the higher the level of
motivation or arousal, the more effective the performance will be. Coaches
often spend a great deal of time before games attempting to raise the team’s
arousal level, and we hear sportscasters argue that a team’s performance
was poor because the players were not “psyched up” enough for the contest.
Yet this general view is contradicted by experimental evidence: A high
level of arousal is effective only to a point, whereas further raising the
arousal level actually damages performance.
Variations of the Inverted-U Principle
Over the years, the general shape of the inverted U has been a good
guideline for thinking about the relationship between arousal and
performance. However, it is probably best not to put too much faith in the
“symmetrical” shape of this U-function. As shown in figure 3.9, more recent
evidence and theorizing have suggested that task differences, as well as
individual differences in the subject’s “excitability,” can result in changes to
the shapes of the curve, with optimal performance occurring at either the
lower or higher ends of the arousal continuum.
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Figure 3.9 Variations to the inverted-U principle.
Consider the three hypothetical curves illustrated in figure 3.9. The red
curve labeled “A” shows a steep rise in performance at relatively low
levels of arousal, with performance peaking and then beginning to decline
even before a moderate level in arousal. Such a curve might represent the
shape of the arousal–performance function for a particularly complex task,
perhaps requiring fine motor control (threading a needle) or cognition
(playing chess), or for an individual who functions best under calm
conditions. In contrast, the green curve labeled “C” could represent the
arousal–performance function for a very simple task, perhaps requiring
great amounts of force with very little cognition (e.g., powerlifting) or for a
person who thrives under pressure. The point here is that the simple
inverted U (the blue curve labeled “B” in figure 3.9) might best represent
many other tasks that have medium levels of complexity, cognitive
involvement, and so on. These principles have been recognized and studied
in the fields of sport and exercise psychology, where the general problem
has been to understand the effects of stress and arousal on performance, and
to examine how arousal-regulation procedures can be used to manage
arousal levels before performance (e.g., Weinberg & Gould, 2011).
Focus on Application 3.2
Automotive Panic
It was a fairly normal morning—the 35-year-old teacher reported
that she had just dropped her son at day care before heading to
school. She stopped for coffee at the local drive-through and moved
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the transmission lever from Drive to Neutral in order to reach for
her purse. After putting the cup in the coffee holder, she intended to
put her foot on the brake while she changed the transmission from
Neutral back to Drive. Suddenly, the car lurched forward and
gathered speed. Pressing harder on the brake pedal seemed only to
make the car go faster. Thinking that the brakes had failed, the
teacher pressed harder still, and this time the pedal went all the way
to the floor. The car sailed across the parking lot, wildly out of
control, with the driver in a complete panic state, before crashing
into a parked vehicle on the other side of the street. The air bags
engaged, and fortunately nobody was seriously hurt. It was only
then, as the motor continued to race with the wheels still squealing,
that the woman realized her mistake—she had pressed the
accelerator instead of the brake.
This story illustrates a number of important issues about motor
control that we will return to later in the book: how we guide
movements in the absence of visual feedback; how movement
selection errors can occur without our detection; how and when we
make error corrections; and lastly, and most important for our
present discussion, the fact that normal modes of information
processing can cease to operate when we are in a heightened state
of panic, sometimes called hypervigilance.
As you reflect on this story, there are a number of easy solutions to
the situation that come to mind. Turning off the ignition, moving the
transmission into Neutral, and removing the foot from the pedal
would have all solved the problem. But in these cases of
unintended acceleration, a phenomenon that occurs far too
commonly, none of these corrective actions are typically made
(Schmidt, 1989). Instead, it is usually some external agent, such as a
tree, wall, or another vehicle, that brings the car to a halt.
Hypervigilance occurs at the very highest levels of arousal. In cases
of unintended acceleration, the driver seems to “freeze” at the
wheel, in terms of both normal movement control and information-
processing activities. In a hypervigilant state, decision making is
severely limited, resulting in an inability to produce “creative”
actions (e.g., switching off the ignition key) and an ineffective
performance generally. Fortunately, hypervigilant states are
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relatively rare. But when they do occur, the fate of the individual is
almost completely turned over to the “fight-or-flight” functioning of
a panicked information-processing system.
Perceptual Narrowing
One important change in information processing that occurs with high
arousal is perceptual narrowing—the tendency for the perceptual field to
shrink under stress. (This phenomenon is sometimes known as “tunnel
vision,” in which the world seems to be viewed as through a pipe such that
the entire focus is on central vision. It has also been referred to as “weapon
focus,” in which the panicked victim of an armed robbery cannot identify the
robber because her attention had been riveted on the gun.) For example,
consider how vision changes for the novice in the relatively stressful sport
of deep sea diving. Weltman and Egstrom (1966), for example, presented
reaction stimuli in various peripheral locations to diving students who had
been asked to perform a simple motor task. On land, where the diver can
operate under a low level of arousal, the range of possible stimulus sources
to which the subject can respond is relatively wide, representing most of the
visual field. However, at the bottom of a swimming pool, and especially on
the ocean floor, where arousal would be thought to increase, the visual field
becomes more narrowly and intensely focused; systematically fewer
peripheral stimuli are detected, with increased focus on the expected or
important aspects of the task and increased focus on those sources of
information most pertinent to or expected in the task (located in central
vision). This is an important mechanism because it allows the person to
devote more attention to those stimulus-sources that are immediately most
likely and relevant. Perceptual narrowing is not limited to vision but
apparently occurs with each of the senses in an analogous way.
Easterbrook’s (1959) cue-utilization hypothesis also helps to explain the
inverted-U principle. When the arousal level is low and the perceptual field
is relatively wide, the performer has access to, and uses, a wide range of
cues, only a few of which are relevant to effective performance, so
performance is suboptimal. As the arousal level rises and the attentional
focus narrows onto the most relevant cues, more and more of the irrelevant
cues are excluded, so proficiency increases because the performer is
responding to mostly relevant cues. When further arousal increases, though,
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the increased perceptual narrowing means missing even some of the
relevant cues, so performance begins to suffer, particularly where the cues
are not highly expected. The optimal level of arousal is presumably one in
which the narrowed attentional focus excludes many irrelevant cues yet
allows most of the relevant cues to be detected.
Choking Under Pressure
One of the most dramatic occasions in high-profile sporting events occurs
when an individual or team, seemingly on the way to certain victory, does
the unimagined thing, plays sloppily, and loses. Relatively recent examples
of team collapses include the 2011 Boston Red Sox and Atlanta Braves
baseball teams, both of which blew large leads in September and failed to
make the playoffs. Rory McIlroy provided an individual example in the
2011 Masters golf tournament when he shot 7 over par on the back nine in a
Sunday collapse. Everyone can probably recall events like this, and there is
no shortage of examples in every sport, it seems.
What defines a “choke”? And, more importantly, why does it occur and how
can it be avoided? By most accounts, choking under pressure is more than
simply a failed performance in an important situation. Not even the very
best basketball players would be expected to make every foul shot or every
jump shot at the final buzzer, with the game on the line. Instead, the term
“choking under pressure” is reserved for situations in which performers
change their normal routine or fail to adapt to a changing situation, resulting
in the failed performance. The reasons for a choke have as much to do with
information-processing errors as they do with errors in motor performance.
Attentional control theory (Eysenck et al., 2007) suggests that increased
levels of anxiety tend to reduce “controlled” selective attention activities of
the performer and increase the attention to certain potentially lifesaving
cues. This shift in attentional control results in an increased competition for
resources, which has a devastating effect on processing efficiency and
ultimately leads to degraded performance and the choke.
In contrast, researchers such as Beilock (2010) suggest that choking under
pressure occurs when there is a change in one’s attentional focus. As the
pressure builds to perform well in a critical situation, athletes who choke
often shift from performing in an overlearned, automatic type of attentional
(external) focus to a more conscious, controlled (internal) focus of attention.
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This shift in attention is tantamount to moving to a type of control typical of
skills in the very early stages of practice.
Summary
A good way to think about attention is to imagine a “pool” of resources such
that, if the information-processing activities from a given task exceed the
resources available, performance of this task and perhaps a second task
attempted at that the same time will suffer. Under a limited set of
circumstances, processing can be done “in parallel”; that is, performance on
two tasks can be done together, without interference. As in common
cocktail-party circumstances in which we can ignore the conversations
around us, focusing on a conversation with a given person can occur until,
for example, your name is spoken in a nearby conversation. Other findings
tend to agree; information about the ink color of a word and the meaning of
the word appear to be processed in parallel; but there is considerable
interference involving selection of the action (the Stroop test). People can
become “blind” to certain stimuli if attention is directed strongly elsewhere,
for example failing to perceive the “gorilla” in plain sight when subjects’
attention was directed strongly to another target. This appears to be related
to a class of motor vehicle crashes in which one driver looked but failed to
see (that is, to perceive) an oncoming vehicle. On the other hand, many
highly practiced tasks tend to be performed without very much attention; this
is difficult to achieve without extensive practice under the proper
conditions. While we might appear to be able to drive a car without any
attention, studies of drivers attempting to drive and converse on a cell phone
at the same time shows that this is a dangerous combination. The biggest
attentional limitation of all appears to be in the movement programming
stage. The so-called psychological refractory period (PRP) provides the
evidence. The overall viewpoint appears to be that, while we may be able
under certain circumstances to respond without attention in the stimulus
identification and response selection stages, only one action can be
programmed at a time during the movement programming stage.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
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http://www.HumanKinetics.com/MotorLearningAndPerformance

chapter.
Interactive Learning
Activity 3.1: Review the concepts associated with limitations in
attention by matching related terms to their definitions.
Activity 3.2: Indicate whether each in a list of characteristics applies
to controlled processing or automatic processing.
Activity 3.3: Using a figure from the text, explore how the inverted-U
relationship between arousal and performance varies by situation and
person.
Principles-to-Application Exercise
Activity 3.4: The principles-to-application exercise for this chapter
prompts you to choose an activity and analyze the attentional demands
of three performance situations within that activity. You will also
indicate whether the demand for attention relates primarily to stimulus
identification, response programming, or movement programming, and
examine how stress might affect decision making during this activity.
Check Your Understanding
1. Both parallel and serial processing can occur during the stimulus
identification stage of information processing. Provide examples of the
types of information that might be processed in parallel and in serial as
a rock climber decides which move to make next.
2. Explain how a fake in wheelchair basketball illustrates a strong
interference between activities in the movement programming stage of
information processing.
3. Regarding attention, explain why a lifeguard at a crowded pool may
find it more difficult to perform his job as he nears the end of his shift.
What factors influence his ability to sustain attention? Suggest two
policies that a pool could put in place to make sustained attention
easier for lifeguards.
Apply Your Knowledge
1. The way information is processed during the response selection stage
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http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30115

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30116

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30117

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of information processing can be very different between a novice and
an expert performing the same task. Describe the differing types of
processing, highlighting the key features of each. How is task
interference related to each type of processing? How might a person
performing a waltz perform differently as a novice and as an expert?
2. When mountain biking, some cyclists who have no trouble riding along
a straight trail have difficulty performing the identical task (riding in a
straight line) on a bridge that is the same width (or wider) than the
trail, resulting in a less smooth performance or even a fall. What role
might focus of attention play in this phenomenon? How could the
inverted-U principle be used to help explain why a bridge over a
ravine may make the task of riding in a straight line more difficult?
Suggestions for Further Reading
Many books and review articles about attention and human performance
have been written. The works by Wickens and McCarley (2008), Chun,
Golomb, and Turk-Browne (2011), and Kahneman (2011) are excellent
recent resources. Wulf’s (2007) book on attentional focus is of special
relevance to motor performance and learning. The Chabris and Simons
(2010) book discusses inattention blindness and other fascinating attention-
related issues. Davies and Parasuraman (1982) discuss sustained attention
in much detail. Weinberg and Gould (2011) discuss arousal, stress, and
other topics of particular importance in sport and exercise psychology.
Beilock’s (2010) book on choking is also a fascinating read. See the
reference list for these additional resources.
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Chapter 4
Sensory Contributions to Skilled
Performance
Feedback Processing in Motor Control
Chapter Outline
Sources of Sensory Information
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Processing Sensory Information
Principles of Visual Control
Audition and Motor Control
Summary
Chapter Objectives
Chapter 4 describes the roles of sensory feedback in human motor
control. This chapter will help you to understand
the types of sensory information available for motor control,
motor control as a closed-loop processing system,
how feedback and feedforward information work in the
conceptual model, and
the roles of vision in motor control.
Key Terms
blindsight
closed-loop control system
comparator
cutaneous receptor
dorsal stream
exteroception
feedforward
Golgi tendon organs
joint receptors
M1
M2
M3
muscle spindle
optical array
optical flow
proprioception
quiet-eye effect
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tau
ventral stream
vestibular apparatus
Success in skilled performance often depends on how effectively the
performer detects, perceives, and uses relevant sensory information.
Frequently, the winner of a contest is the one who has detected a pattern of
action in an opponent most quickly, as in many sports and games. Success
can also be measured by the correct detection of errors in one’s own body
movements and positions, which provides a basis for subsequent movement
(or positional) modifications, as in dance or gymnastics. A surgeon requires
skilled touch (haptic) perception to detect an abnormal growth during a
physical exam of a patient. Consequently, considerable emphasis is directed
toward improving the skill with which performers detect and process
sensory information because these improvements can lead to large gains in
performance.
Sources of Sensory Information
Information for skilled performance arises from a number of sources, and
can be categorized into two major types. One type comes from the
environment and is termed exteroceptive (the prefix “extero-” means that the
information is provided from outside the body). The other type of sensory
information is termed proprioceptive (the prefix “proprio-” means that the
information arises from within the body). Exteroception provides
information to the processing system about the state of the environment in
which one’s body exists, and proprioception provides information about the
state of the body itself. These sources of information are types of inherent
(or intrinsic) feedback. The term “feedback” is used for situations in which,
during a movement, sensations arise because the body is moving, which
produces information that is “fed back” to the performer. For example, when
we move from one place to another, information is available from the
contracting muscles, and there are changes in what we see while moving.
Proprioceptive feedback is sometimes also referred to as “movement-
produced feedback.”
For feedback to be “inherent” means that the information is directly
available to the performer and is available “naturally” through the senses. A
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major distinction is made later in the book when we discuss the provision of
augmented feedback—information about which the performer is not
“normally” aware; this is “extra” information provided by an external
source. An example is a video that shows a person performing a skill.
Exteroception
The most prominent of the exteroceptive information sources, of course, is
vision. Seeing serves the important function of defining the physical
structure of the environment, such as the edge of a stairway or the presence
of an object blocking one’s path, thus providing a basis for anticipating
upcoming events. Vision also provides information about the movement of
objects in the environment in relation to your own movements—such as the
flight path of a ball while you are running to catch it—which can be used to
make certain predictive judgments about the movement direction to take.
Another function of vision is to detect your own movement within the
(stable) environment, such as your path toward an external object and how
much time will elapse before you arrive.
The second major kind of exteroceptive information comes from hearing, or
audition. Although audition is not as obviously involved in motor skills as
vision, there are many activities that depend heavily on well-developed
auditory skills; one obvious example is the sounds of a musical instrument
you are playing. However, audition is important for many other skills too,
such as using the sounds of the sailboat’s hull moving through the water as
cues to boat speed, the sound of a power tool as a skilled carpenter cuts
through different materials, or the sounds of an engine as an auto mechanic
adjusts a carburetor.
Proprioception
The second major type of information is from the body’s movement, usually
termed proprioception. This term refers to the sense of movements of joints,
tensions in muscles, and so on, giving information about the state of the body
parts in relation to each other and relative to the environment. Several
important receptors provide this information.
The vestibular apparatus in the inner ear provides signals related to
movements, one’s orientation (e.g., upside down), or both, in one’s
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environment. These structures are sensitive to acceleration of the head and
are positioned to detect the head’s orientation with respect to gravity. It is
not surprising that these structures are strongly implicated in posture and
balance.
Several other structures provide information about what the limbs are doing.
Receptors in the capsule surrounding each of the joints, called the joint
receptors , give information about extreme positions of the joints.
Embedded within the belly of the skeletal muscle are muscle spindles ,
oriented in parallel with the muscle fibers. Because muscles change lengths
when the joints they span are moved, the muscle spindle lengths are changed
as well; this is thought to provide indirect information about joint position
and other aspects of the movement. Near the junction between the skeletal
muscle and its tendon lie the Golgi tendon organs , which are very
sensitive to the level of force in the various parts of the muscle to which
they are attached. Finally, in most skin areas are cutaneous receptors ,
including several kinds of specialized detectors of pressure, temperature,
touch, and so on. The cutaneous receptors are critical for the haptic sense,
the sense of touch.
None of these receptors respond to just one physical characteristic,
however. The signal from a particular source, for example the muscle
spindles, provides ambiguous information about joint position because the
receptor can be affected by several other physical stimuli (movement
velocity, muscle tension, and the orientation with respect to gravity) at the
same time. For this reason, the central nervous system is thought to use a
complex combination of the inputs from these various receptors as a basis
for body awareness.
Because of the multiple, complex receptors involved, perception of a
movement’s trajectory can be affected by how the movement is produced.
There may be a difference depending on whether the movement was a
normal, active action or a passive, guided action, as when an instructor or
therapist moves a patient through a movement. The perception of the proper
trajectory of an arm movement, for example, can be quite different if the
movement is guided by a therapist, compared to being actively generated by
the patient. Also, many guidance techniques, such as artificially
manipulating the learner’s movements during an action (creating the arm
stroke in swimming, spotting in gymnastics), can affect the proprioceptive
sensations generated markedly. These techniques can be useful at the
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beginning stages of learning, but because they distort the proprioceptive
sensations (and for other reasons that we will discuss later), they could
easily be overused in instructional or therapeutic situations. We will return
to the concept of guidance later on, when we discuss learning.
However, such alterations in proprioception can be used in a positive way
too, as when they are exploited by human factors engineers in the design of
equipment. Adding spring resistance to a car’s steering system adds “feel,”
which can make the car easier to control and more pleasant to drive.
Perhaps for a similar reason, experienced typists prefer keyboards that
provide some haptic feedback (Asundi & Odell, 2011), which provides the
satisfying auditory “click,” indicating to the operator that the key has been
pressed successfully. Supplying aircraft instrument knobs of different shapes
and locations for different functions makes confusion among them less likely
by associating distinct proprioceptive sensations with each knob.
There are many different sources of sensory information for motor control,
varying not only in terms of where the information is detected but also in
terms of how it is processed and used. Even so, the next sections consider
this variety of sensations as a single group, focusing on the common ways
the central nervous system processes this information for skilled
performance.
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A physician or therapist relies on the sense of touch to detect abnormalities
during an examination.
Processing Sensory Information
One important way to think about how sensory information is processed
during movement is by analogy to closed-loop control systems, a class of
mechanisms used in many applications in everyday life. Figure 4.1 provides
an example of a simple closed-loop system.
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Figure 4.1 A basic closed-loop control system.
Closed-Loop Control Systems
The simple closed-loop control system illustrated in figure 4.1 can be
started in a number of ways; but one common way occurs when you input a
desired state or system goal, such as the desired temperature in your house
in the winter. Sensory information about the system’s output (the room’s
actual temperature) is measured by a thermometer and is compared to the
expected temperature. Any difference between the expected and actual
temperature represents an error (e.g., the temperature is too low); this error
information is transmitted to an executive to decide what action to take to
eliminate or reduce the error. The executive sends a command to an effector,
in this case turning on the furnace, which carries out the action. This action
raises the room temperature until the actual state equals the expected state
(where the error in temperature is now zero); this information is sent to the
executive, and the executive sends a new instruction to switch off the heat
production. This process continues indefinitely, maintaining the temperature
near the desired value throughout the day. This kind of system is termed
“closed loop” because the loop from the executive to the effector and back
to the executive again is completely “closed” by sensory information, or
feedback, forming a “loop” that supports the mechanism in regulating the
system to achieve a particular goal.
The same general processes operate in human performance, as in reaching
to pick up a cup. Visual information about the hand’s position relative to the
cup represents the feedback. Differences between the hand’s location and
the desired location are sensed as errors. An executive determines a
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correction, modifying the effector system to bring the hand into the proper
location. Of course, in more complicated skills, feedback consists of a
collection of different kinds of sensory information arising from a variety of
receptors both within and outside the body. Each kind of information is
compared to a corresponding reference level, and the errors are then
processed by the executive level in the system.
All closed-loop control systems have four distinct parts:
1. An executive for decision making about errors
2. An effector system for carrying out the decisions
3. A reference of correctness against which the feedback is compared to
define an error
4. An error signal, which is the information acted on by the executive
Closed-Loop Control in the Conceptual Model
These closed-loop processes fit within the expanded conceptual model for
movement control as shown in figure 4.2. This is simply an expansion of the
conceptual model of human performance presented in chapter 2, which
introduces the stages of information processing (see figure 2.2). However,
now are added the notions of closed-loop control seen in figure 4.1 to
achieve a more complete system that complements our discussion of human
motor performance.
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Figure 4.2 Expanded conceptual model with the addition of closed-loop
components.
This conceptual model is useful for understanding the processes involved
not only in relatively slow movements (e.g., slow positioning of a limb in
physical therapy) where compensations can be made during the action, but
also in relatively fast movements (e.g., swinging an ax) where the correction
of the error must wait until the movements have been completed.
The executive system consists of the decision-making processes discussed
in chapter 2—the stimulus identification, response selection, and movement
programming stages. The executive then sends commands to an effector
system consisting of several parts. One is the motor program, which
produces commands for lower centers in the spinal cord, which finally
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result in the contraction of muscles and the movement of joints. At the same
time, information is specified to define the sensory qualities of the correct
movement, such as the “feel” of an effective ax swing. This information
represents the performer’s anticipated sensory feedback, that is, the
sensations that should be generated if the movement is performed correctly.
An example is the state of the thermostat when it is set to, say, 70°; incoming
feedback from the room’s air temperature (the actual feedback) is then
compared to the anticipated feedback (70°, indicated by the thermostat’s
state) and an error is computed, which is then delivered to the executive. As
such, the information that specifies that the thermostat should be anticipating
a temperature of 70° is often termed feedforward information. The term
feedforward is used to distinguish it from feedback, or the sensory qualities
of the action itself. Feedforward information represents anticipated sensory
consequences of the movement that should be received if the movement is
correct, so that the error would now be zero.
The output (the movement) results in proprioceptive and exteroceptive
feedback information, collectively termed movement-produced feedback.
When muscles contract, the system receives feedback about forces as well
as about the pressures exerted on objects in contact with the skin.
Contracting muscles cause movement, and, as a result, feedback from
moving joints and the changes in body position with respect to gravity.
Finally, movements usually produce alterations in the environment, which
are sensed by the receptors for vision and audition, generating yet more
feedback. These movement-produced stimuli, whose nature is critically
dependent on the production of a particular action by the performer, are
compared against their anticipated states in the comparator. The computed
difference represents error, which is returned to the executive. This process
refines and maintains the performer’s behavior, holding errors at acceptably
low levels.
Notice that the stages of processing are critically important in the closed-
loop model in figure 4.2. Every time an action’s feedback goes to the
executive for correction, it must go through the stages of processing. The
various stages of processing are all subject to the mechanisms of attention,
as discussed in chapter 3. However, this is not the only way in which
feedback can be used, and we discuss various lower-level, reflex-like
loops later in the chapter.
The closed-loop model in figure 4.2 is useful for understanding the
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maintenance of a particular state, as is necessary to perform many long-
duration activities. For example, simply maintaining posture, where the goal
is a natural, upright position, requires feedback. Various learned postures
might be controlled in the same way, such as positioning the body in a
handstand on the still rings in gymnastics. Also, most movement skills
involving the various limbs require an accurate, stable posture as a
“platform.” Without this stable base, the movement, such as throwing darts
or shooting a pistol, would be inconsistent. The comparator is thought to
define and maintain the desired relative positions of the various limbs as
well as the general orientation in space.
Other tasks are far more dynamic. For instance, in a continuous tracking
skill, a performer must follow some constantly varying track by moving a
control. Steering a car is a classic example, where movements of the
steering wheel result from visually detected errors in the car’s position on
the road. There are countless other examples, as this class of activity is one
of the most frequently represented in real-world functioning.
Without a doubt, closed-loop control models such as that shown in figure
4.2 are the most effective for understanding these kinds of behavior. Thus,
understanding how such a model operates provides considerable insight into
human performance and allows many important applications. Understanding
the model’s limitations for movement control is also important, as discussed
next.
Focus on Application 4.1
Error Correction in Batting
In swinging the bat at a pitched baseball, the performer first
evaluates the environmental situation and then selects a movement to
meet the perceived demands. The stages of processing select a
program and ready it for initiation. Once the movement is started,
the response execution processes carry out that movement more or
less as planned. Provided that the environment remains in the same
state as it was when the movement was organized, the movement
should be effective in meeting the environmental demand, so the bat
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should hit the ball.
But what if something in the environment suddenly changes? For
example, if the ball curves unexpectedly, now the batter wants to
swing at a different location or perhaps stop the swing altogether.
The conceptual model enables an estimate of how much time would
be required for this kind of information to influence an ongoing
movement. Such information must pass through the stages of
processing, therefore requiring several hundred milliseconds before
the first modification in the movement can occur.
While these modification processes are occurring, the movement is
still being carried out as originally planned. Therefore, the initial
parts of the first movement occur before the batter can initiate the
correction to stop or change the action. One of two common
occurrences in baseball is observed if the batter detects the curving
ball too late: (a) The original movement is carried out almost
completely, resulting in a hopeless miss, or (b) the batter tries to
stop (“check”) the swing, but is too late to stop its momentum from
carrying the bat across the plate. However, one of two other
common occurrences in baseball is observed if the batter is
successful in modifying the original plan: The batter either adjusts
the swing and makes contact with the ball or stops the swing before
the bat crosses the plate.
Limitations of Closed-Loop Control
The inclusion of the stages of information processing in the system, as
depicted in figure 4.2, illustrates the flexibility in movement control,
allowing various strategies and options and altering the nature of the
movement produced, depending on the particular circumstances. However,
these stages of processing represent a big disadvantage at the same speed—
they are slow, especially when there is high demand for processing time,
resources, or both, as in many complex actions (discussed in chapter 3). The
following sections describe cases in which the closed-loop model is less
effective for guiding movement.
Tracking Tasks
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One important generalization arising from chapters 2 and 3 was that the
stages of information processing require considerable time. Hence the
closed-loop system with these processes embedded in it should be slow as
well. The stages of processing are critical components in reaction-time
(RT) situations, where presenting a stimulus requires various processes
leading to a movement. The closed-loop model can be regarded in the same
way, but the stimulus in this instance is the error that drives the executive,
and the movement is the correction selected by the executive. Numerous
studies of tracking suggest that the system can produce separate responses
(that is, corrections) at a maximum rate of about three per second. This is
about the rate that would be expected if the system were using RT processes
as a critical component, reacting to errors by making corrections.
In the conceptual model presented in figure 4.2, each correction is based on
a collection of information about the errors that have occurred over the past
few hundred milliseconds. This error is processed in the stimulus
identification stage; a movement correction is chosen in the response
selection stage; and the correction is organized and initiated in the
movement programming stage. Therefore, tracking tasks that involve more
than three changes in direction per second are typically performed very
poorly. What makes a fumbled football difficult to retrieve is the frequently
unpredictable nature of the bounces it takes. For this reason, closed-loop
control processes are most relevant to tasks that are relatively slow or have
a long duration in time.
Rapid, Discrete Tasks
A feedback-based view of movement control fails to account adequately for
movement production in skills that are very quick, such as the ballistic
actions in sport skills (e.g., throwing and kicking) and pressing a key during
texting. As a general rule with the most rapid human actions, the performer
initiates a fully planned movement to achieve the goal. If later sensory
information indicates that this movement will be incorrect and thus should
be stopped or radically altered, this information is processed relatively
slowly and sluggishly, so the first few hundred milliseconds of the original
movement occur more or less without modification. As you will see later in
this chapter, though, sensory information plays an increasingly important and
effective role as the movement is made more slowly.
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This sluggishness of feedback processing has implications for controlling
the moment-to-moment adjustments in very rapid movements (say,
movements less than 300 ms or so), such as typing a text message or
plucking the strings of a banjo in a bluesgrass song. According to the
conceptual model, feedback arising from the rapid movement would not
have enough time to be processed before the movement was completed. The
feedback thus would not influence the fine, moment-to-moment control.
More than any other observation, this sluggishness of feedback control has
led scientists to believe that most rapid movements must be organized (or,
as is usually said, programmed) in advance. In this view, the moment-to-
moment control is included as part of the preorganized program and is not
dependent on the relatively slow processes associated with feedback (this
idea is discussed more fully in chapter 5). Feedback can also act reflexively
to modify movements far more quickly than indicated by the basic closed-
loop model presented in Figure 4.2. This aspect of feedback control is
discussed next.
Proprioceptive Closed-Loop Control
To this point, only one kind of closed-loop processes has been considered:
conscious, voluntary control of actions by sensory information. But there are
other ways in which sensory information is involved in movement control,
especially considering the many kinds of corrections, modifications, and
subtle changes in skills that occur automatically (without conscious
awareness).
There are a number of reflexive mechanisms that operate below our level of
consciousness. One of the most well known of these is the so-called knee-
jerk reflex. If one sits on a table with knee bent and lower leg freely hanging
and then a small tap is applied to the patellar tendon (usually with a small
rubber hammer, as done by a neurologist), the response to the tap is a brief
contraction of the quadriceps muscle (on the thigh) and a small extension
(straightening) of the knee. The time from the tap until the quadriceps is
activated is only 30 to 50 ms. This reflexive response occurs without any
active, voluntary control and occurs far too quickly to have come via the
stages of information processing.
Here is what happens. In this seated position, a tap to the patellar tendon,
which attaches the kneecap (patella) to the tibia of the lower leg, applies a
brief downward movement of the kneecap. Then, because the kneecap is
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attached to the quadriceps muscle, which (together with the muscle spindles
in the quadriceps) is stretched a small amount, too, the muscle spindles
respond by sending a signal to the spinal cord via afferent (sensory)
neurons. These neurons synapse (connect) with efferent (motor) neurons that
lead back to the same muscle that was stretched (here, the quadriceps),
causing a brief contraction. This occurs very quickly, and involuntarily, in
part because the afferent and efferent neurons travel a relatively short
distance and are connected by a single synapse. Hence, this reflex has been
termed the monosynaptic stretch reflex. Nearly every skeletal muscle in the
body can display this reflex, operating in the same general way.
Take a look at figure 4.3, which is another expansion of our conceptual
model. Within the “effector” box (motor program–spinal cord–muscles), we
have added a feedback loop (the so-called M1 loop) from the muscle to the
spinal cord and back to the same muscle. This loop is an important
component of the monosynaptic stretch reflex. This feedback loop is at a
relatively low level in the spinal cord, so the responses do not involve
conscious, voluntary control and reflect stereotyped, involuntary, usually
very rapid responses to stimuli.
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Figure 4.3 Conceptual model with the addition of M1 and M2 loops.
Now suppose that you are a subject in a simple experiment. You are
standing, and your task is to hold one of your elbows at a right angle to
support a moderate load on your hand, such as a book. You have a dial in
front of you to indicate the height of the book, and you are instructed to hold
the book at some target position. Suddenly, without your being able to
anticipate it, the experimenter adds another book to the load. Your hand
begins to drop, but after a delay you compensate for the added load and
bring your hand up to the target position again. In all likelihood, your
response was nearly immediate and involuntary, but this time more than one
reflex was involved. The monosynaptic reflex just described was
responsible for an initial, very brief, response to the added load. However,
bringing the hand back to the target position likely involved one or more
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additional reflexes.
The slightly slower (50-80 ms latency) response occurs because the
stretched biceps muscle delivered a signal (via afferent neurons from the
muscle spindles) to the spinal cord. Here, though, this signal is also sent up
the spinal cord, and these neurons synapse with several higher-level
neurons. Then, the signal is sent back down the pathway in the spinal cord
where it synapses with the motor neurons leading to the biceps muscle,
causing a second burst of biceps activity. This second burst of activity
(labeled M2 in figure 4.3) is stronger and more sustained than the first one
(the monosynaptic, M1, response), but it arrives with a slightly greater
delay (50-80 ms) because the signal had to travel farther and because
several synapses were involved. The M2 loop in figure 4.3 goes from the
muscle to higher levels in the CNS. Together, these monosynaptic (M1) and
multisynaptic (M2) reflexes are just two of the many types of reflexive
mechanisms by which actions can be modified quickly (and automatically),
leading toward goal achievement in a closed-loop manner.
Principles of Visual Control
Vision seems to operate somewhat differently than the proprioceptive
reflexes we have just seen. Vision, of course, has a very important role in
everyday activities, and people deprived of vision have a relatively
difficult time functioning in our visually dominated world. But vision also
appears to operate somewhat differently from the other senses in the support
of skills. For these reasons, vision deserves a place of its own in this
chapter.
Two Visual Systems
Over the past 40 years or so, it has become increasingly clear that two
essentially separate visual systems underlie human functioning, rather than
just one. Visual information is delivered from the retina of the eye along two
separate processing streams to different places in the brain, and there is
good evidence that these two different pathways of information are used
differently in the control of behavior.
These two systems, illustrated in figure 4.4, are called the dorsal stream
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and the ventral stream because of their anatomical distinctions
(Ungerleider & Mishkin, 1982; note that they have also been referred to as
the ambient and focal systems, respectively). Visual information in both
streams travels first from the retina of the eye to the primary visual cortex.
However, at that point it is thought that visual information processing
becomes specialized. Information useful for the identification of an object is
sent to the inferotemporal cortex via the ventral stream. Information that is
used specifically for the control of movement within the visual environment
is sent to the posterior parietal cortex via the dorsal stream.
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Figure 4.4 Illustration of dorsal and ventral stream pathways in the brain.
The ventral stream is specialized for conscious identification of objects that
lie primarily in the center of the visual field. Its major function seems to be
providing answers to the general question “What is it?” Hence we use this
system to look at and identify something, such as the words on this page you
are reading now. This system contributes to conscious perception of objects
and is severely degraded by dim lighting conditions, as you know from your
attempts to read or do fine handwork without adequate light.
The dorsal stream is believed to be specialized for movement control.
Distinct from the ventral stream, which is sensitive only to events in central
vision, dorsal vision involves the entire visual field, central and peripheral.
Dorsal vision operates nonconsciously, contributing to the fine control of
movements without our awareness (see Focus on Research 4.1). Clearly,
one reason it is difficult to recognize the existence of dorsal vision is that it
is nonconscious. Its function is to provide answers to the questions “Where
is it?” or perhaps “Where am I relative to it?”
Focus on Research 4.1
“Blindsight” Reveals Dorsal and Ventral Stream
Processing
The term “blindsight” might at first sound self-contradictory, but this
curious phenomenon led many to the discovery of the dorsal
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(ambient) visual system. Blindsight is usually defined a medical
condition in which the person can respond to visual stimuli without
consciously perceiving them. According to Weiskrantz (2007), the
idea originally stemmed from work on the visual cortex of monkeys,
where it was demonstrated that the animal, although technically
“blind,” could still respond to various kinds of visual stimuli. Later
studies demonstrated the phenomenon in humans as well (e.g., by
Humphrey, 1974; Weiskrantz et al., 1974).
Perhaps the most startling, and most convincing, evidence came
from the study of two human neurological patients, TN and DB (the
patients’ initials; de Gelder et al., 2008; Weiskrantz et al., 1974).
TN had had two successive strokes, causing major neurological
damage in his visual cortex, which rendered him “blind” in both
eyes by all traditional measures of vision. After considerable study,
researchers took TN to a hallway, asking him to walk down the
hallway without his usual cane. Unbeknownst to TN, researchers
had placed several objects in the hall around which he would have
to negotiate. To the researchers’ obvious amazement and delight, TN
avoided them all, even pressing himself against the wall to avoid a
trash can. Patient DB, whose occipital cortex had been removed
surgically because of a tumor, was also “blind” according to
traditional measures of vision. Researchers used forced-choice
tests, in which DB was asked to guess where, between two
locations in front of him, an object had been placed. His guesses
were considerably more accurate than chance, even though he could
not “see” the objects. He was also sensitive to long or short
temporal object presentation intervals, to color, to contrast, to
motion, and to the onset and offset of the target’s presence. Very
clearly, both of these subjects were “seeing” objects that they were
not consciously aware of.
These findings eventually were interpreted to mean that we possess
two visual systems: a ventral (also called “focal”) system with
access to consciousness (which DB and TN had lost completely)
and a dorsal system that does not have access to consciousness
(which was intact in TN and DB). (The anatomical pathways for
these systems are illustrated in figure 4.4.) The blindsight
phenomenon demonstrates clearly that we can respond to objects in
our environment unconsciously, guided by visual information of
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which we are completely unaware.
Bridgeman and colleagues (1981) provided some of the strongest
evidence for the role of a dorsal system for movement control.
Subjects sat in a darkened room in front of a screen on which was
projected a rectangle (like a picture frame) with a spot of light
inside it. Without the subject’s awareness, the frame was moved
back and forth slightly a few degrees, with the dot remaining in a
fixed position on the screen inside the moving frame. Under these
conditions, the subject reliably experienced the illusion that the dot
was moving back and forth within the frame, rather than the reverse,
which was actually the case. In terms of the notion of two visual
systems, the ventral system (the one with access to consciousness)
has been “deceived,” judging that the dot was moving when it was
actually stationary.
Next, Bridgeman and collaborators attempted to manipulate the
dorsal system. The subjects were instructed that if the frame and dot
were suddenly turned off, leaving the room in total darkness, they
were to point a lever to the last position of the dot as quickly as
possible. Of course, the subject’s conscious perception was that the
dot was moving back and forth. So, if the ventral system were being
used in controlling the hand, the pointing movements should vary
from right to left as well, in coordination with the (consciously)
perceived “movements” of the dot. To the contrary, when the lights
were turned off, the subjects pointed to where the dot actually was,
not to where they perceived it to be. The interpretation was that the
visual information for the localization of the dot was being used by
the nonconscious dorsal system, and this system was not deceived
by the movements of the frame. This evidence supports the existence
of two separate visual systems: the ventral system for consciousness
biased by the frame movements, and the dorsal system for movement
control, which was not biased by the frame movements.
Exploring Further
1. Patients with optic ataxia and visual agnosia have been the
focus of study by neurophysiologists. With what information
have these patients provided to researchers regard to the
distinction between ventral and dorsal visual streams?
2. What other types of visual illusions have been used in research
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to separate dorsal and ventral stream processing?
Visual Control of Movement Skills
How is visual information used for movement control, and what factors
determine its effectiveness? It is useful to divide this discussion into
separate parts, particularly because it deals with the separate roles of the
dorsal and ventral systems.
Despite the characterization of ventral vision as a system for object
identification, it would be wrong to conclude that it has no role in movement
control. Ventral vision has access to consciousness, so it is processed
through the information-processing stages discussed in chapter 2, leading to
action in much the same way as any other information source. In the
conceptual model in figure 4.3, vision can be seen as just another source of
information arising from action, so its only access to the loop would be
through the stages of information processing. In one sense, this is obvious.
You can look at and consciously identify an oncoming car, which would then
lead to the decision to try to avoid it. Ventral vision is critically involved
here, and failures to identify objects properly can lead to serious errors.
This is particularly important in night driving, when the ventral system’s
accuracy (visual acuity) is degraded considerably.
Before realizing there could be a dorsal system for movement control,
scientists believed that a conscious ventral system was the only way visual
information could influence action. In this outdated view, a baseball batter
watching a pitch come toward the plate relied only on the relatively slow
processes in the information-processing stages to detect the ball’s flight
pattern and to initiate changes in movement control. This idea was
supported by numerous experiments that seemed to show that visual control
of action (ventral stream processing) was particularly slow and
cumbersome. However, recent information about the dorsal visual system,
together with the ideas about optical-flow processes in vision, has markedly
changed our understanding of visual information processing for action.
Dorsal Stream Movement Control
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James J. Gibson (e.g., Gibson, 1966) altered radically the way scientists
theorized about the visual control of movement. A particularly important
concept promoted by Gibson was that of optical-flow patterns, and how this
information was used by the ambient visual system to control body
movement (such as balance) and to provide information about the timing of
events, such as the time to close a gap between the performer and an object.
For example, riding a bicycle along a busy path or street requires rapid
processing of many sources of visual information. The cyclist needs be
aware of traffic signs and signals, pedestrians crossing the street, cars
turning away and into the oncoming path, and, of course, the dreaded
opening of the driver’s door of a parked car. As the cyclist looks into this
textured environment, each visible feature reflects rays of light, which enter
the eyes at specific angles; collectively, this is called the optical array .
Because the cyclist is moving, each object in the environment shifts its
position relative to the cyclist continuously, causing a change in the
information provided by the optical array. This change in information is
termed optical flow and can be thought of as a “flow of light” across the
retina. The important point is that optical flow provides numerous important
kinds of information about the cyclist’s movement through the environment,
such as
time before a collision between the cyclist and an object,
direction of movement relative to objects in the environment,
movement of environmental objects relative to the cyclist,
stability and balance of the cyclist, and
velocity of movement through the environment.
Time-to-Contact Information
Figure 4.5 presents an example of how the optical array picks up
information about an object first seen in the distance (say 25 m [meters]
away—object A), then at a closer distance (15 m away—object B), then at
a very close distance (5 m—object C). The angle of light given from the
edges of the object at distance A is very small (α1); it increases slightly as it
gets 10 m closer (α2), and then it expands at a much more rapid rate over the
next 10 m (α3).
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Figure 4.5 Objects A, B, and C are traveling at the same velocity in the
right-to-left direction toward an eye (gray circles). The sizes of the object’s
optical image on the rear of the eye (the retina) at different distances are α1,
α2, and α3. Notice that when the object travels from A to B, the size of the
retinal image changes at a slower rate than it does for the same distance
covered from B to C. These changes in optical flow are picked up by the
dorsal system, providing information about object distance and time until
the object will contact the plane of the eye.
The pattern of optical flow from an oncoming object, such as parked car,
indicates the time remaining until the object reaches the plane of the eye
(Lee & Young, 1985). The retinal image of an approaching object expands
as the object approaches, and it expands more quickly as the object
approaches more quickly. These changes in optical flow are picked up by
the dorsal system, providing information about object distance and time
until the object will contact the plane of the eye. This time-to-contact is
abbreviated with the Greek letter tau (τ).The optical variable tau (τ), which
is defined as the retinal image size divided by the image’s rate of change in
size, turns out to be proportional to the time remaining until contact. Thus, τ
is derived from optical-flow information and used by the dorsal stream to
specify time-to-contact with the object. This timing information is critical in
interceptive actions involving coincident timing, such as striking or catching
a ball, driving, or preparing the body for entry into the water during a dive.
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As a mountain biker navigates the trail, she processes a continuously
changing stream of visual information about the environment.
Direction of Movement of Objects
One can run through a forest, avoiding trees successfully, by using optical-
flow information about the relative rates of change in the visual angles of
the trees. Assume that the objects shown in figure 4.6 are trees. For tree A,
the angles of light from the left and right edges expand at the same rate from
both sides, indicating that the eye is traveling directly toward tree A and
will collide with it. For tree B, on the other hand, the angles of light from
the right side are expanding systematically more slowly than those from the
left side. This indicates that the eye will pass tree B with B on its left side.
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Figure 4.6 The observer, represented here by an eye, is heading toward
object A, and will pass object B on its left side; in our example, A and B
are trees. The ambient system detects that the rays of light from both sides of
tree A are expanding at about the same rate, whereas the light rays from the
left side of tree B are expanding more quickly than are those from the right
side. This indicates that, if the observer doesn’t change course she will
collide with Tree A and will pass tree B with B on the eye’s left side.
Balance
Maintaining balance has traditionally been the domain of proprioceptive
information in detecting sway and loss of stability. For example, when the
body sways forward, the ankle joint is moved and the associated
musculature is stretched, producing movement signals from the muscle
spindles and other proprioceptors. Also, receptors in the inner ear are
sensitive to movements of the head, providing information about body sway
and balance.
However, vision also plays a key role in balance control. Look straight
ahead at an object on the wall. Without shifting your direction of gaze, move
your head slightly forward and backward and pay attention to the changes in
visual information. You will probably notice that the objects in peripheral
vision seem to sweep rapidly back and forth and that these changes are
dependent on your head movement. Could this peripheral information serve
for balance control?
Lee and Aronson (1974) have shown that balance is strongly affected by
varying the visual information, suggesting that the optical-flow variables in
peripheral vision are critical to balance. In their experiment, the subject
stands in a special, small room surrounded by walls suspended from a very
high ceiling, such that that the walls do not quite touch the floor. The walls
can be moved, with the floor kept still, to influence only the optical-flow
information. Moving the walls slightly away causes the subject to sway
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slightly forward, and moving the walls closer causes the subject to sway
backward. With a little child, an away movement of the walls can cause the
subject to stumble forward, and a toward movement of the walls can cause a
rather ungraceful plop into a sitting position (see figure 4.7).
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Figure 4.7 David N. Lee’s moving room apparatus. Moving the walls
forward (away from the camera) makes the young subject sway forward,
and moving the wall backward causes the subject to “plop” into a sitting
position. This evidence suggests that vision is critical for balance. (Lee is
second from the left in this photo).
Reprinted by permission from Lee and Aronson 1974.
Moving the wall toward the person generates optical-flow information that
ordinarily means the head is moving forward; that is, that the person is out
of balance and falling forward. The automatic postural compensation is to
sway backward. Such visually-based compensations are far faster than can
be explained by conscious processing in the ventral system, with latencies
of about 100 ms (Nashner & Berthoz, 1978). These experiments suggest that
optical-flow information and the dorsal system are critically involved in
controlling normal balancing activities.
This role of vision in balance control has strong implications for learned
postures as well. In performing a handstand on the still rings, where it is
important to remain as motionless as possible, the visual system can signal
very small changes in posture, providing a basis for tiny corrections to hold
the posture steady.
Ventral Stream Movement Control
The ventral stream provides information about the “what” in motor control.
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An expert baseball batter knows that different types of pitches have different
spins—rotations of the ball that help the batter to predict a pitch’s
trajectory. A dental assistant not only must know the difference between the
appearance of a sickle probe and a periodontal probe, but also needs to be
able to identify each with an associated verbal label when called upon.
Thus, the ventral stream usually needs information presented in well-lit
visual conditions in order to identify object information, which then can be
used for conscious, decision-making processes for action.
Vision and Movement Planning
Object identification, via the ventral stream, plays a crucial role in
movement planning before the initiation of an action. For example, have a
look at the objects in figure 4.8 and think about which grip you would use to
pick up each object. For the juice glass (object a), a full-hand grip is
needed. A four-finger grip is needed for the beer mug (object b), but a
thumb-and-finger grip is appropriate for the teacup (object c). The pen
(object d) is usually picked up with a precision grip if the performer intends
to use it to write something. However, if the performer intends to use the
pen as a tool, say, to stab a cardboard box, then a power grip would be
used. Thus, information provided by the ventral stream is combined with the
action goal for further processing in the movement planning stages (see
Rosenbaum et al. 2013 for a fuller treatment).
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Figure 4.8 The ventral stream identifies the visual properties of an object
for the purpose of advance grip planning of objects a through d. However,
the visual properties of objects do not tell the whole story—intentions about
what action will be performed with an object also determine which grip to
use. For example, think about how you would grip object d if you were
planning to write with it, and compare that to how you would grip object d
if you planned to use it to stab a cardboard box (see Rosenbaum et al.
2013).
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Processing Visual Feedback
Earlier, we mentioned that visual information can be used very rapidly
(with latencies less than 100 ms) to make adjustments in the control of
movement. In other situations however, visually based corrections involve
the relatively slow stages of information processing; and one line of the
research has been to identify how much time is needed to conduct this
processing activity.
Following the initial attempts by Woodworth (1899), a unique strategy was
devised by Keele and Posner (1968) to measure the time to process visual
feedback. The subject’s goal was to complete an aimed hand movement to a
target with minimal spatial and temporal error. The target distance was 15
cm, and there were four target goal MTs (150, 250, 350, and 450 ms). Thus,
the movements were completed in times that ranged from very rapid (150
ms goal, but they were actually completed in 185-190 ms) to fairly slow
(450 ms). Subjects were given verbal feedback about their MTs after each
trial to help them to move in the proper MT. A critically important feature of
the research design was that subjects completed some of the trials in the
dark—the ambient lights were suddenly extinguished on a randomly-
selected 50% of the trials just at the initiation of the movement. The
prediction was simple: If visual feedback was used to guide the movement
onto the target, then having the ambient lights on should produce more
accurate aims than when the ambient lights were off. But, if the movement
was made too fast to use visual feedback, then no differences in accuracy
would be expected.
Keele and Posner’s results are presented in figure 4.9. The first thing to note
is that, as the MTs became longer in time, the accuracy in hitting the target
increased, but mainly so for the movements made with the lights on. It is
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important to note that the accuracies with the lights on versus lights off were
identical for the shortest MT, and the curves diverged as movements became
longer in time. These data suggest that, when the MT was approximately
190-250 ms, this was the minimum time to use visual feedback in these
actions. Thus, slower movements benefited from having the ambient lights
on.
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Figure 4.9 Movements completed in times less than 200 ms showed no
improvement in accuracy with the ambient lights on (compared to lights off),
but movements made in times longer than 250 ms did benefit from having the
lights on.
Data from Keele and Posner 1968.
Despite earlier finding suggesting that the time to use visual feedback can be
processed in less than 100 ms, this evidence here suggests that the minimum
time to use visual feedback in aiming is 190 ms and 250 ms. If the vision
offset is unexpected, as in Keele and Posner’s task, the amount of time
needed to process the visual feedback is roughly similar to that for choice
RT. If availability of visual feedback can be predicted, then the processing
time is reduced to essentially that for simple RT (Elliott, Hansen, &
Grierson, 2010).
Focus on Research 4.2
Gaze Control
Eye-movement recording devices provide researchers with very
precise measures of gaze—where a person is looking during an
action or perceptual event. These studies have revealed that we
voluntarily control gaze using two different types of eye movements:
smooth-pursuit eye movements and discrete-saccadic movements.
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The goal of smooth-pursuit movements is to keep the target of our
gaze fixed on the fovea of the retina. The eyes fixate on an object
that is either motionless or moving slowly, allowing the viewer to
pick up precise detail. Faster object movements, on the other hand,
are characterized by brief fixations and rapid shifts to a different
location in the visual environment. Information is picked up during
the fixations, but not during the saccades between fixations.
Saccadic shifts provide us with the capability to pick up information
rapidly from a wide range of sources in our visual environment, for
example as required when driving (looking out the front and side
windows, checking the various mirrors, etc.).
Researchers have discovered something very interesting about the
way highly skilled and less skilled athletes use vision just before the
onset of action. Expert performers keep their eyes fixated for a
longer period of time just before movement onset than do
nonexperts. Moreover, individuals can improve their performance if
they are trained to fixate their gaze for a longer period of time just
before action. These findings have been replicated in many different
types of activities (e.g., basketball free-throw shooting, target
shooting, golf, juggling) and have been termed the “quiet-eye”
effect (see Vickers, 2007, for a good review).
There remains considerable uncertainty regarding the mechanism(s)
responsible for these latter effects. One hypothesis is that a
prolonged gaze period might stabilize the perceptual system,
facilitating movement processes dependent on them. Another view
is that this period of inactivity provides an opportunity to shift
attentional resources to an optimal-control focus. Although many
other possible reasons cannot be excluded at this time, the generality
of the “quiet-eye” effect suggests that these mechanisms probably
arise with skill development. Clearly, there is much more research
to be conducted on this topic.
Exploring Further
1. What are rods and cones of the eye, and what specific
information do they contribute to vision?
2. What is the difference between looking and seeing? How does
the activity known as Parkour reveal that what traceurs and
traceuses see is different from what the rest of us see?
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Vision in the Conceptual Model
The distinction between dorsal- and ventral-stream visual processing has
obvious implications for our conceptual model as presented in figure 4.3.
Although ventral-stream processing would still occur as suggested in the
model (through the slow processing stages, in the “outer loop”), dorsal-
stream processing would be expected to be unconscious—perhaps almost
reflex-like. Because dorsal-stream processing is nonconscious, relatively
fast, and inflexible, it is fed back to relatively low levels in the central
nervous system, considerably “downstream” from the processes that select
and initiate movement but “upstream” from the muscles and the spinal cord.
Thus, dorsal vision can be thought of as operating at intermediate levels of
the system to make minor adjustments in already programmed actions, such
as compensation for head movement in the golf swing and alterations in
posture to maintain balance on the still rings. For this reason, we have
added a feedback loop from the resulting movement to the level of the motor
program in figure 4.10.
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Figure 4.10 Conceptual model with the addition of dorsal stream loop.
Focus on Application 4.2
Visibility in Nighttime Car–Truck Accidents
A not uncommon motor vehicle accident occurs at nighttime—the
driver of a car going at the posted speed limit runs into the back of a
vehicle that is stopped or moving very slowly (perhaps a truck on a
hill). The driver of the car had no trouble seeing the taillights on the
back of the parked or slowly moving vehicle. Rather, the problem
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was that the driver did not know what the lights were “doing.” What
could be going on here?
On a clear night, the driver can probably see the taillights from quite
a long distance away (perhaps a mile or so). Presumably, the rate of
visual expansion (in this case, the lights appearing to move farther
apart) of the optical flow on the retina provides information that the
vehicle ahead is stopped (or going very slowly), instead of going at
the same speed as the car. But the problem is illustrated quite
clearly in figure 4.11 (from Ayres et al., 1995)—the rate of
expansion is so small (smaller than the threshold for detecting any
expansion, the threshold rate for being able to detect any expansion
is 0.2 degrees/second). From figure 4.11 this means that the
following driver cannot perceive the expansion until the car is about
400 ft (about 133 m) from the object, where it is just possible to
detect that the optical array is expanding. The problem is that 400 ft
is very close to the distance over which a car at 60 mph (about 95
kph) can be brought to a complete stop—and this is under ideal (i.e.,
dry, good visibility) conditions. Therefore, the following driver
does not perceive that the truck is moving far more slowly than he is
until it is almost too late. Unless the following driver is extremely
alert, the car can easily strike the slow-moving truck.
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Figure 4.11 The rate of visual expansion of a car at 60mph (about
95 kph) approaching a slow-moving truck (assuming a 6-foot wide
vehicle) remains below threshold until the truck is approximately
400 ft (about 133 m) away.
Reprinted by permission from Ayres, Schmidt et al. 1995.
Another issue is that, at a very far distance away, the visibility of
taillights does not necessarily indicate to the driver that what she is
approaching is actually a truck. It could be lights on two separate
objects, such as road signs (spaced apart by a small distance), or
two motorcycle taillights, or something other than a vehicle. Some
proponents of vehicle safety have advocated placing additional
retro-reflective material on trucks to make them more visible or
conspicuous. But this really does not reduce the visual perception
problem raised here, because the rate of visual expansion of the
retro-reflective material is essentially the same as that of the lights.
Therefore, when you are 500 ft (about 150 m) from the truck, you
have the same problem of not being able to identify what the object
is doing, speed-wise.
Audition and Motor Control
Perhaps one of the more understudied areas of research is the role of
auditory feedback in movement. Yet we know that it can have profound
effects on motor control. For example, a speaker who talks into a
microphone at a large concert hall and hears his voice projected from the
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sound system at the very back of the hall will experience a delay in hearing
the auditory feedback of his voice. Such delays are well known to increase
timing errors and slowed rates of speaking and to disrupt other forms of
movement, such as playing a musical instrument (Pfordresher & Dalla
Bella, 2011). Ironically, delayed auditory feedback is sometimes effective
in the treatment of stuttering, perhaps because it causes individuals to slow
their speech (Lawrence & Barclay, 1998).
Auditory feedback plays other roles in performance as well. An outfielder
in baseball might be fooled into predicting that a line drive seen leaving a
batter’s bat will go over her head. In fact, the sound of the bat–ball contact
correctly indicated that it was a softly hit ball requiring the outfielder to run
in, rather than back. Here the visual information distorted the correct
information provided by sound (see Gray, 2009, for more). The
overshadowing of audition by vision is also illustrated in the McGurk
effect, discussed in Focus on Application 4.3.
The question of how auditory feedback is processed in movement control
remains largely unanswered, although we suspect that information is
processed as outlined in our conceptual model in figure 4.10, with both
anticipatory (feedforward) and actual feedback mechanisms serving
important roles in movement. Clearly, much research remains to be done in
this area.
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A skilled carpenter may use the sound of a saw cutting through wood as a
source of information.
Focus on Application 4.3
When Vision Degrades Performance
In chapter 3 we discussed the various properties of attention.
Directing one’s attention toward a specific external source is
considered an important mechanism, and vision can provide
information critical for effective performance. However, performers
often find that visual control dominates the other senses and that
visual information leads to an unavoidable capture of attention. In
fact, many believe that vision tends to dominate all other sources for
our attention, and this dominance does not always produce positive
outcomes. In many activities, performers have a choice of the modes
of control they use, such as the race car driver or pilot who can
monitor the sounds of the engine or kinesthetic information from the
“seat-of-the-pants” as opposed to the visual information provided
by numerous cockpit gauges or information seen through the
windscreen. And sometimes this nonvisual information is more
reliable than the information provided by vision.
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Visual information is obviously very important in many situations,
but in other situations an overreliance on vision can result in
ineffective performance. A good example comes from sailboat
racing, which is very rich in visual information about the
aerodynamic shapes of the sails and the way the wind is flowing
over them. This visual information can yield relatively good
performance. However, focusing on vision is ignoring other forms
of information, such as the sounds the boat makes as it goes through
the water, the action and position of the hull felt by the “seat of the
pants,” and forces on the tiller, all of which provide additional
useful information about speed—but only if the person at the helm is
attending to them. Some racing sailors have used blindfolded
training methods in an attempt to learn to decrease their reliance on
vision and share the attentional resources among other internal
targets (senses) to optimize performance. The idea is that when
vision is prevented for a long time, the sailors develop sensitivity to
the less dominant sources of information.
Visual illusions provide a powerful demonstration of visual
dominance effects. In the size–weight illusion, for example, two
opaque containers, one much larger than the other, are filled with
amounts of sand so that they are of identical weights. The subject is
asked to lift both and judge which one is heavier. Vision (and
experience) tells us that the larger container is usually heavier than
the smaller one, and when proprioception fails to confirm that, we
naturally conclude the opposite—that the smaller container must
have more weight in it than the larger container. Here, visual
information is overriding proprioception.
The McGurk effect reveals a vision–audition interaction. For
example, a subject is asked to watch and listen to a video of
someone speaking a word over and over again (e.g., “blow”). If you
close your eyes and listen to the audio portion of the video it is
obvious that the person is saying “blow.” But the video is actually
of someone mouthing the word “flow” over and over again, with
“blow” being given on the audio. Most people who watch the video
and also listen to the audio report that the person is saying “flow,”
even though what is actually heard is “blow.” And sometimes
people even report something completely different than either
“flow” or “blow.” Here, vision is distorting the (true) information
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provided by audition.
This McGurk effect and the size–weight illusion are just two
examples of how visual dominance tends to overshadow the
contributions made by the other senses.
Summary
The effectiveness with which a performer processes various forms of
sensory information often determines overall performance level. Sensory
signals from the environment are usually termed exteroceptive information,
whereas those from the body are termed proprioceptive information. For
human performance it is useful to think of these signals as operating in a
closed-loop control system, which contains an executive for decision
making, an effector for carrying out the actions, feedback about the state of
the environment, and a comparator to contrast the environmental state with
the system’s goal.
In the conceptual model of human performance, closed-loop control is
added to the stages of information processing discussed in previous
chapters. This model is particularly effective for understanding how slower
actions as well as tracking tasks are performed. To the conceptual model are
then added several reflex-like processes that account for corrections
without involving the information-processing stages. In moving from the
M1, M2, and M3 (or voluntary reaction time), these responses show
systematically increased flexibility and increased latency. Finally, vision is
considered as a special case of closed-loop control. Two visual streams are
introduced, a dorsal stream for motor control and a ventral stream for object
identification, and the role of the dorsal stream in balance and in producing
and correcting actions is considered. These sensory systems are then
integrated into the conceptual model, which helps to show how these
various sensory events can support or modify skilled actions and under what
conditions they operate.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
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http://www.HumanKinetics.com/MotorLearningAndPerformance

activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 4.1: Identify the roles of the sensory organs involved in
proprioception by matching each receptor with its location and
function.
Activity 4.2: Identify which component of a closed-loop control
system relates to each of a series of actions.
Activity 4.3: Indicate whether each in a list of characteristics applies
to the dorsal or ventral visual processing stream.
Activity 4.4: Choose the labels for the conceptual model of motor
control to review its stages, including closed-loop control pathways
and visual stream information.
Principles-to-Application Exercise
Activity 4.5: The principles-to-application exercise for this chapter
prompts you to choose an activity, identify sources of proprioceptive
and exteroceptive information during the activity, and evaluate which
sources of information are useful to the performer. You will also apply
the concept of closed-loop control to the activity.
Check Your Understanding
1. Name the four distinct parts of a closed-loop control system. Describe
how each of these parts might function for a child stacking toy blocks.
2. Explain how the pattern of optical flow can inform an outfielder
attempting to catch a fly ball about when and where the ball will reach
the height of the fielder’s glove.
3. How does ventral-stream movement control play a role in movement
planning when opening various doors throughout your day?
Apply Your Knowledge
1. Several sources of sensory information are available to a skier as she
makes her way down an alpine ski run. Describe and provide examples
of exteroceptive and proprioceptive information that she might receive
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http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30121

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30122

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30123

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30124

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30127

during her run, and indicate why this information is important for
movement.
2. How closed-loop control processes are used (if at all) is dependent on
the task that is performed. Contrast the role played by closed-loop
control processes for casting a fishing line and tracing a clothing
pattern onto fabric. Would this role change if either of the tasks were
sped up?
Suggestions for Further Reading
An overview of closed-loop control of movement is provided by Ghez and
Krakauer (2000), with specific roles assigned to spinal reflexes presented
by Pearson and Gordon (2000). Elliott and Khan (2010) edited an excellent
book that provides many contributions regarding the various roles of vision
in motor control. The contributions of ventral- and dorsal-stream processing
are debated in Norman (2002). And a more comprehensive discussion of the
various topics presented in this chapter can be found in chapter 5 of Schmidt
and Lee (2011). See the reference list for these additional resources.
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Chapter 5
Motor Programs
Motor Control of Brief Actions
Chapter Outline
Motor Program Theory
Evidence for Motor Programs
Motor Programs and the Conceptual Model
Problems in Motor Program Theory: The Novelty and Storage
Problems
Generalized Motor Program Theory
Summary
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Chapter Objectives
Chapter 5 describes how motor programs are used in the control of
movement. This chapter will help you to understand
motor control as an open-loop system and the role of motor
programs,
experimental evidence for motor programs,
limitations and problems in the simple motor program concept,
and
generalized motor programs and evidence for this expanded
concept.
Key Terms
central pattern generator (CPG)
deafferentation
generalized motor program (GMP)
invariant features
motor program
novelty problem
open-loop control
parameterized
parameters
reflex-reversal phenomenon
relative timing
sensory neuropathy
startle RT
storage problem
surface feature
Watching a guitarist pluck a series of notes with blazing speed, or a pianist
run trills up and down the keyboard, reminds us that in many skills, a
number of separate actions can appear in very quick sequence. Yet these
separate actions are produced while maintaining a specific rhythm to the
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sequence that leaves the impression of a single, fluid, coordinated motion.
How does the skilled musician produce so many movements so quickly?
What controls them, and how are they combined to form a whole? The
skilled musician gives us the impression that these quick movements might
be organized in advance and run off without much feedback control.
This chapter investigates the idea of open-loop control, introducing the
concept of the motor program as responsible for this kind of movement
control. Then the various feedback pathways discussed in the previous
chapter are examined as to their interaction with motor programs, giving a
more complete picture of the interplay of central and peripheral
contributions to movements. The chapter also focuses on the concept of a
generalized motor program (GMP), a theory that can account for the
common observation that movements can be varied along certain
dimensions—for example, playing the guitar or piano sequence slower or
faster (or louder or softer) without sacrificing their underlying structure
(i.e., the rhythm).
In many actions, particularly quick ones produced in stable and predictable
environments (e.g., springboard diving, hammering), most people would
assume that a performer somehow plans the movement in advance and then
triggers it, allowing the action to run its course without much modification
or awareness of the individual elements. Also, the performer does not seem
to have much conscious control over the movement once it’s triggered into
action; the movement just seems to “take care of itself.” Perhaps this is
obvious. Certainly you cannot have direct, conscious control of the
thousands of individual muscle contractions and joint movements—all the
degrees of freedom that must be coordinated as the skilled action is
unfolding. There is simply too much going on for the limited-capacity
attentional mechanisms (which we have discussed in chapters 3 and 4) to
control any one of them individually.
If these individual contractions are not controlled directly by processes of
which you are aware, how then are they controlled and regulated? In many
ways, this question is one of the most fundamental to the field of motor
behavior because it goes to the heart of how biological systems of all kinds
control their actions. This chapter focuses on the ways the central nervous
system is organized functionally before and during an action and how this
organization contributes to the control of the unfolding movement. As such,
this chapter is a close companion to chapter 4, which considered the ways
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sensory information contributes to movement production. This chapter adds
the idea of centrally organized commands that sensory information may
modify somewhat. First, though, comes the important concept of a motor
program, which is the prestructured set of movement commands that
defines and shapes the movement.
Motor Program Theory
The concept of the motor program, which is central to this entire chapter, is
based on a kind of control mechanism that is in some ways the opposite of
the closed-loop system discussed throughout chapter 4. This type of
functional organization is called open-loop control.
Open-Loop Control
The basic open-loop system is illustrated in figure 5.1, and consists
essentially of two parts: an executive and an effector. This open-loop
structure has two of the main features used in closed-loop control (figure
4.1), but missing are feedback and comparator mechanisms for determining
system errors. Open-loop control begins with input about the desired state
being given to the executive (or decision-making) level, whose task it is to
define what action needs to be taken. The executive then passes instructions
to the effector level, which is responsible for carrying out these instructions.
Once the actions are completed, the system’s job is over until the executive
is activated again. Of course, without feedback, the open-loop system is not
sensitive to whether or not the actions generated in the environment were
effective in meeting the goal; and since feedback is not present,
modifications to the action cannot be made while the action is in progress.
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Figure 5.1 A basic open-loop system.
This kind of control system can be observed in many different real-world
mechanisms. For example, an open-loop system is used in most traffic
signals, where it sequences the timing of the red, yellow, and green lights
that control the traffic flow. If an accident should happen at that intersection,
the open-loop system continues to sequence the lights as if nothing were
wrong, even though the standard pattern would be ineffective in handling
this new, unexpected traffic flow problem. Thus, the open-loop system is
effective as long as things go as expected, but it is inflexible in the face of
unpredicted changes.
A microwave oven is another example of an open-loop system. The user
places a frozen entree in the oven and programs it to defrost for 5 min, and
then cooks on high power for another 2 min. Here, the program tells the
machine what operations to do at each step and specifies the timing of each
operation. Although some microwave ovens are sensitive to the temperature
of the item being cooked, many are not, and these latter machines follow the
instructions without any regard for whether they will result in the desired
state (an entree that is ready to eat).
Generally, the characteristics of a purely open-loop control system can be
summarized as follows:
Advance instructions specify the operations to be done, their
sequencing, and their timing.
Once the program has been initiated, the system executes the
instructions, essentially without modification.
There is no capability to detect or to correct errors because feedback
is not involved.
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Open-loop systems are most effective in stable, predictable
environments in which the need for modification of commands is low.
Motor Programs as Open-Loop Systems
Many movements—especially ones that are rapid, brief, and forceful, such
as kicking and key pressing—seem to be controlled in an open-loop fashion,
without much conscious control once the movement is under way. The
performer in these tasks does not have time to process information about
movement errors and must plan the movement in its entirety before
movement initiation. This is quite different from the style of control
discussed in the previous chapter, where the movements were slower (or
longer in time) and were largely based on feedback processes of various
kinds.
Open-loop control seems especially important when the environmental
situation is predictable and stable. Under these circumstances, human
movements appear to be carried out without much possibility of, or need
for, modification. This general idea was popularized more than a century
ago by the psychologist William James (1891) and has remained as one of
the most important ways to understand movement control.
Consider a goal such as hitting a pitched baseball. The executive level,
which consists of the decision-making stages of the system defined in
chapter 2, evaluates the environment in the stimulus identification stage,
processing such information as the speed and direction of the ball. The
decision about whether or not to swing is made in the response selection
stage. Then, the movement is programmed and initiated in the movement
programming stage, in which details about the swing’s speed, trajectory, and
timing are determined.
Control is then passed to the effector level for movement execution. The
selected motor program now carries out the swing by delivering commands
to the spinal cord, which eventually directs the operations of the skeletal
system involved in the swing. This movement then influences the outcome—
resulting in the desired movement (hitting the ball squarely) or not (e.g.,
missing the ball, popping the ball up).
Although the decision-making stages determine what program to initiate and
have some role in the eventual form of the movement (e.g., its speed and
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trajectory), movement execution is not actually controlled by the conscious
decision-making stages. Therefore, the movement is carried out by a system
that is not under direct conscious control. On this view, the motor program
is the agent determining which muscles are to contract, in what order, when,
and for how long (timing).
Practice, which leads to learning skilled actions, is thought of as “building”
new, more stable, more precise, or longer-operating motor programs (or
some combination of these). Initially a program might be capable only of
controlling a short string of actions. With practice, however, the program
becomes more elaborate, capable of controlling longer and longer strings of
behavior, perhaps even modulating various reflexive activities that support
the overall movement goal. These programs are then stored in long-term
memory and must be retrieved and prepared for initiation during the
response programming stage.
Open-Loop Control in the Conceptual Model
How does this concept of open-loop control and the motor program fit with
the conceptual model of human performance? Figure 5.2 shows the
conceptual model developed in chapter 4 (figure 4.10), now with the
portions highlighted (light-green shading) that comprise the open-loop
components. The conceptual model can here be thought of as an open-loop
control system with feedback added (the parts not shaded) to produce
corrections through the other loops discussed previously. This more
complete conceptual model has two basic ways of operating, depending on
the task. If the movement is very slow or of long duration (e.g., threading a
needle), the control is dominated by the feedback processes. If the
movement is very fast or brief (e.g., a punch or kick), then the open-loop
portions tend to dominate. In most tasks, motor behavior is not either open
or closed loop alone but a complex blend of the two.
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Figure 5.2 Conceptual model with the open-loop processes highlighted in
light green.
For very fast and or brief actions, the theory of motor programs is useful
because it gives a set of ideas and a vocabulary to talk about a functional
organization of the motor system. If a given movement is said to be “a
programmed action,” it appears to be organized in advance, triggered more
or less as a whole, and carried out without much modification from sensory
feedback. This language describes a style of motor control with central
movement organization, where movement details are determined by the
central nervous system and are then sent to the muscles, rather than
controlled by peripheral processes involving feedback. Of course, both
styles of control are possible, depending on the nature of the task, the time
involved, and other factors.
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Evidence for Motor Programs
A number of separate lines of evidence converge to support the existence of
motor program control. This evidence comes from some rather diverse
areas of research: (a) studies of reaction time in humans; (b) experiments on
animals and case studies involving animals and humans in which feedback
has been removed; (c) the impact on performance when movement is
unexpectedly blocked; (d) the analysis of behaviors when humans attempt to
stop or change an action; and (e) studies of movements initiated by startling
stimuli.
Reaction-Time Evidence
In RT experiments, the duration of the RT delay was slowed when more
information needed to be processed (e.g., Hick’s Law), when processing
was not “natural” (e.g., in S-R incompatible situations), and so on.
Generally, RT was determined mainly by the slowness of the stimulus
identification and response selection stages. In this section we review
evidence that RT is also influenced by the factors affecting the movement
programming stage.
Response Complexity Effects
Subjects in RT experiments are typically asked to respond to a stimulus by
initiating and carrying out a predetermined movement as quickly as possible
(as discussed in chapter 2). Duration of the RT delay is measured as the
interval from the presentation of the stimulus until the movement begins, so
any added time for the movement itself does not contribute directly to RT.
However, beginning with the work of Henry and Rogers (1960; see Focus
on Research 5.1), many experimenters have shown that RT is affected by
several features of the movement to be performed, presumably by
influencing the complexity (and duration) of the movement programming
stage.
Henry and Rogers (1960) found that simple RT was elevated with increases
in the complexity of the movement to be performed after the response was
initiated. This, plus much more research on this important finding since the
publication of Henry and Rogers’ work, has produced the following set of
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findings (Klapp, 1996):
RT increases when additional elements in a series are added to the
action (e.g., a unidirectional forward stroke in table tennis would
likely be initiated with a shorter RT than a backswing plus a forward
stroke).
RT increases when more limbs must be coordinated (e.g., a one-
handed piano chord would be initiated with a shorter RT than a two-
handed chord).
RT increases when the duration of the movement becomes longer (e.g.,
a 100 ms bat swing would be initiated with a shorter RT than a 300 ms
bat swing).
The interpretation is that when the to-be-produced movement is more
“complex” in any of these ways (number of elements, number of limbs
involved, the overall duration of the action), RT is longer because more
time is required to organize the motor system before the initiation of the
action. This prior organization occurs, as discussed in chapter 2, in the
movement programming stage. The effect on RT of the nature of the to-be-
performed movement provides evidence that at least some of the action is
organized in advance, just as a motor program theory would expect.
Focus on Research 5.1
The Henry–Rogers Experiment
One of Franklin Henry’s many important contributions was a paper
that he and Donald Rogers published in 1960. The experiment was
simple, as many important experiments are. Subjects responded as
quickly as possible to a stimulus by making one of three movements
that were prepared in advance. Only one of these movements would
be required for a long string of trials, so this was essentially a
simple-RT paradigm (see chapter 2). The movements, designed to
be different in complexity, were (a) a simple finger lift, (b) a simple
finger lift plus a reach to slap a suspended ball, and (c) a movement
requiring a finger lift followed by slapping the most-distant ball
with the back of the hand, then moving to the push button, and then
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grasping the near ball (see Fischman, Christina, & Anson, 2008, for
details; see also figure 5.3).
The seated subject would begin with his finger on the release key
(labeled as “D” in Figure 5.3). For the most complex action, the
subject would respond to the stimulus lights by lifting his finger
from the release key, reaching forward to slap the far tennis ball,
moving down to push the button on the base (E), then, finally,
reaching forward and upwards to slap the second tennis ball; for the
movement of intermediate complexity, the subject responded to the
stimulus lights by lifting his finger off the release key, and then
reaching to slap the far tennis ball; for the simplest movement, the
subject only had to lift his finger off the release key. Each of these
actions was to be done as quickly as possible.
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Figure 5.3 Apparatus used by subjects in the Henry and Rogers
(1960) experiment. Relevant parts are: A = tennis balls; D = release
key; E = push button; H = stimulus lights.
Reprinted from Howell 1953.
Henry and Rogers measured the RT to initiate each of these actions
—the interval from the presentation of the stimulus until the
beginning of the required movement. (Remember that the RT does
not include the time to complete the movement itself.) They found
that the time to initiate the movement increased with added
movement complexity. The finger-lift movement (a) had an RT of
150 ms; the intermediate-complexity movement (b) had an RT of
195 ms; and the movement with two reversals in direction (c) had
an RT of 208 ms.
Notice that in each case, the stimulus to signal the movement
(processed during the stimulus identification stage) and the number
of movement choices (processed during the response selection
stage) remained constant across the different conditions. Thus,
because the only factor that varied was the complexity of the
movement, the interpretation was that the elevated RTs were
somehow caused by increased time for movement programming to
occur before the action. This notion has had profound effects on the
understanding of movement organization processes and has led to
many further research efforts to study these processes more
systematically (reviewed in Christina, 1992). Most importantly,
these data support the idea that rapid movement is organized in
advance, which is consistent with the motor program concept.
Exploring Further
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1. Analyze the differences in actions required for the three
movements in the Henry and Rogers study. Describe at least
three differences in the movements’ requirements that might
have led to increases in the complexity of the motor program.
2. What additional changes could be made to the action
requirements of the most complex movement (C) that might be
expected to increase movement programming time?
Startled Reactions
In the previous section we discussed the idea that RT becomes longer with
increases in the “complexity” of the to-be-performed movement. Here, we
focus on research showing that RT can be dramatically shortened under
certain conditions.
We have all been in situations in which a completely unexpected event, such
as a very loud noise or very bright light, caused a severe reaction—we
were startled. The response is often accompanied by contractions in the
muscles of the face and neck and protective movements of the upper limbs.
A very interesting property of the startle response (RT) is that these
movements are initiated much faster than can be accounted for by voluntary
responses to a stimulus.
An innovative series of studies involved the startle RT as a paradigm to
reveal insights about movement programming. In these studies (reviewed in
Carlsen et al., 2011; Valls-Solé, Kumru, & Kofler, 2008), the subject is
typically asked to prepare to make a rapid, forceful, sometimes complex
response to a moderately intense stimulus (auditory or visual).
Occasionally, the stimulus is accompanied by an extremely loud acoustic
signal (e.g., 130 decibels [dB]; by comparison, the sound of a chainsaw is
about 110 dB).The loud acoustic signal usually produces the typical startle
indicators (clenched neck and jaw muscles, among other reactions).
However, what also happens is that the prepared movement is produced
normally, but with an RT that may be up to 100 ms shorter than on the
control trials without the loud stimulus. The pattern of the actions remained
unchanged.
These findings fit quite well into the motor program concept. The idea here
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is that the executive has prepared a motor program in advance of the
stimulus to respond, which is normally released by a voluntary, internal
“go” signal from the executive to the effectors. The startle RT has the effect
of hastening the release of this signal, by means of either speeding up the
executive’s processing time or perhaps even bypassing the executive
altogether. The research is unclear at this point as to exactly why the same
movement is initiated much faster on startled trials than on normal,
unstartled trials, but the role of the motor program in carrying out the
response is clearly implicated.
Deafferentation Experiments
In chapter 4, we mentioned that information from the muscles, the joints, and
the skin are collected together in sensory nerves, which enter the spinal cord
at various levels. A surgical technique termed deafferentation involves
cutting (via surgery one or more of) an animal’s afferent nerve bundles
where they enter the cord, so the central nervous system no longer can
receive information from some portion of the periphery. The motor
pathways are not affected by this procedure as information about motor
activity passes through the (uncut) ventral (front) side of the cord. Sensory
information from an entire limb, or even from several limbs, can be
eliminated by this procedure.
What are experimental animals capable of doing when deprived of feedback
from the limbs? Films of monkeys with deafferented upper limbs reveal that
they are still able to climb around, playfully chase each other, groom, and
feed themselves essentially normally. It is indeed difficult to recognize that
these animals have a total loss of sensory information from the upper limbs
(Taub, 1976; Taub & Berman, 1968). The monkeys are impaired in some
ways, however; they have difficulty in fine finger control, as in picking up a
pea or manipulating small objects. On balance, though, it is remarkable how
little impaired these animals are in most activities.
If the movement is quick enough, the motor program controls the entire
action; the movement is carried out as though the performer were deprived
of feedback. The capability to move quickly thus gives additional support to
the idea that some central program handles the movement control, at least
until feedback from the movement can begin to have an effect.
Case studies of humans also support this general conclusion. Lashley (1917)
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found that a patient with a gunshot wound to the back, who was without
sensory feedback information from the legs, could still position his knee at a
specified angle without feedback. And individuals who have lost much of
their sensory feedback (so-called sensory neuropathy patients) are able to
perform quite well in their environments as long as visual information is
available (Blouin et al., 1996).
These studies show that sensory information from the moving limb is
certainly not absolutely critical for movement production, and it is clear that
many movements can occur nearly normally without it. This evidence
suggests that theories of movement control must be generally incorrect if
they require sensory information from the responding limb. Because
feedback-based theories cannot account for these actions, many theorists
have argued that the movements must be organized centrally via motor
programs and carried out in an open-loop way, not critically dependent on
feedback (e.g., Keele, 1968). In this sense, the deafferentation evidence
supports the idea that movements can be organized centrally in motor
programs.
Central Pattern Generator
The idea of motor programs is similar to that of the central pattern
generator (CPG), which was developed to explain certain features of
locomotion in animals, such as swimming in fish, chewing in hamsters, and
slithering in snakes (Grillner, 1975). A genetically defined central
organization is established in the brainstem or the spinal cord. When this
organization is initiated by a brief triggering stimulus from the brain,
sometimes called a command neuron, it produces rhythmic, oscillating
commands to the musculature as if it were defining a sequence of right–left–
right activities, such as might serve as the basis of locomotion. These
commands occur even if the sensory nerves are cut (deafferented),
suggesting that the organization is truly central in origin.
An example of a simple network that could account for the alternating
flexor–extensor patterns in locomotion is shown in figure 5.4. Here, the
input trigger activates neuron 1, which activates the flexors as well as
neuron 2. Then neuron 3 is activated, which activates the extensors. Neuron
4 is then activated, which activates neuron 1 again, and the process
continues. This is, of course, far too simple to account for all of the events
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in locomotion, but it shows how a collection of single neurons could be
connected to each other in the spinal cord to produce an alternating pattern.
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Figure 5.4 A simplified illustration of a central pattern generator.
Reprinted by permission from Schmidt and Lee 2011.
The notion of the CPG is almost identical to that of the motor program. The
main difference is that the motor program involves learned activities that
are centrally controlled (such as kicking and throwing), whereas the CPG
involves more genetically-defined activities, such as locomotion, chewing,
and breathing. In any case, there is good evidence that many genetically
defined activities are controlled by CPGs (Zehr, 2005).
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The concept of a central pattern generator is used to describe simple,
genetically-defined activities such as walking, whereas motor program
theory applies to learned skills such as riding a bicycle.
Inhibiting Actions
Another line of evidence to support motor program control can be found in
experiments in which subjects are required to inhibit or stop a movement
after having initiated the process of making the action. This is the kind of
activity that one sees quite frequently in baseball batting (see Focus on
Application 5.1 on checked swings). The question asked by researchers
concerns the “point of no return”—at what point after starting the processing
stages that lead to a movement is one committed to making, or at least
starting, the action? In other words, at what point is the signal released to
send the motor program to the muscles?
The “stop-signal” paradigm is the method most frequently used to study
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action inhibition, and an early contribution to this research was provided by
Slater-Hammel (1960), described in detail in Focus on Research 5.2. The
findings of this study, which involved a very simple finger lift off a key
(presumably with little biomechanical delay), suggested that the point of no
return occurred about 150 to 170 ms before the time when the movement
was initiated. An action such as a baseball swing has a much longer
movement completion time than the finger lift used by Slater-Hammel.
Nevertheless, considerable evidence suggests that a motor program is
released that is responsible for initiating the action in tasks like this and that
serves to carry out the entire action unless a second stop-signal program is
initiated in time to arrest its completion (see Focus on Research 5.2; also
Verbruggen & Logan, 2008).
Focus on Application 5.1
Checked Swings in Baseball
The bat swing in baseball is a good example of a motor program in
action. The typical swing consists of a coordinated action involving
a step with the lead foot toward the oncoming ball, followed by a
rapid rotation of the trunk and shoulders, propelling the bat with a
large angular velocity and minimum overall movement time. There
is good reason to believe that the step and swing are part of a single
motor program, initiated by good batters on almost every pitch; but
parts of this swing can be inhibited before a full execution on many
of those pitches. How do batters do this, and how successful are
they at doing it?
The physics of baseball tell us that there is very little time available
for a major league batter to hit a baseball. For pitches in the range
of 85 to 95 mph (137 to 153 kph), the ball takes less than a half
second (500 ms) to reach the hitting zone after being released from
the pitcher’s hand. The batter typically prepares for the pitch and
may initiate the step before the pitcher has actually released the ball.
And at some point along the way, usually before the ball reaches the
midpoint in its flight toward the plate, the batter must decide
whether to proceed with the swing (including where to aim the bat
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for its intended collision with the ball) or inhibit its execution. The
result is four different types of batter responses (see Gray, 2009, for
much more on these ideas):
1. The batter successfully inhibits the motor program, and the
swing is never initiated.
2. The batter starts the swing but inhibits the completion of the
motor program, resulting in the bat stopping before it crosses
the plate (which defines it as a “nonswing”).
3. The batter starts the swing but fails to inhibit the motor
program in time, resulting in a slowed velocity of the bat as it
crosses the plate (resulting in a “completed swing,” according
to the rules of baseball).
4. The batter starts and completes the motor program without
attempting to inhibit the swing—a classic example of a
completed swing.
Focus on Research 5.2
Initiating a Motor Program
Not so long ago, races like the 100 m sprint were timed by hand,
with a stopwatch. The timing judge started her stopwatch when she
saw the smoke of the starter’s pistol and stopped it when the runners
crossed the finish line. But, let’s consider how she stopped her
stopwatch. If she stopped it when she saw the runner cross the line,
then the clock would actually be stopped a short time later, because
completing her action would be delayed by two factors: (1) the
amount of time required to send the motor instructions to the hand
holding the watch and (2) the biomechanical delays in pushing the
button.
Our interest, of course, is the first concern—how long does it take to
send the motor instructions? To answer this question, Arthur Slater-
Hammel (1960) conducted an experiment that was similar to the
example of the timing judge just presented. Subjects held a finger on
a key while watching an analog timer moving at one revolution per
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second; lifting off the key brought the sweep hand to an
instantaneous stop. Subjects were instructed to lift their finger from
the key such that the clock hand would stop at exactly the point
marked 800, or roughly at the 10 o-clock position on the clock (800
refers to a lapse of 800 ms after the start from the 12:00 position;
see figure 5.5a). Note that in order to do this task accurately, they
would need to initiate the action at some point before the clock hand
actually reached the 800 position (just as the timing judge would
need to initiate the action of stopping her timer before the sprinter
crossed the finish line).
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Figure 5.5 Slater-Hammel’s (1960) task (a) and results (b).
Reprinted by permission from Schmidt and Lee 2011. Part b data from Slater-Hammel
1960.
An important aspect of the Slater-Hammel study was the insertion of
special (probe) trials that occurred rarely and unpredictably. On
these probe trials the experimenter would stop the clock hand at
various locations before it reached 800. If this happened, the
subjects’ job was simply to keep their finger on the key; thus, the
probe trials required an inhibition of the normal task of lifting the
finger to stop the sweep hand. The rationale was simple—if the
motor program had not yet been sent to the muscles when the sweep
hand stopped, then the subject should be able to inhibit the finger lift
successfully. But there would be little chance of changing such a
short, ballistic action if the sweep hand stopped after the motor
program to lift the finger that had already been sent.
Slater-Hammel plotted the probability of inhibiting the action
successfully as a function of the time interval between 800 and
where the clock hand had stopped. The data are shown in figure
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5.5b. When the interval before the intended finger lift was relatively
large (greater than 210 ms), stopping the clock hand resulted in
inhibiting the movement successfully almost all the time. However,
as this interval decreased, the subjects would lift the finger more
and more often, to the point that when the clock hand stopped at
−700 (100 ms before the 800 position), the subject could almost
never inhibit the movement. Generally, when the clock hand was
stopped about 150 to 170 ms before the intended finger lift, the
subject could inhibit the movement successfully about half the time.
This finding can be interpreted to mean that the internal “go” signal
is issued about 150 to 170 ms before the intended action. This “go”
signal is a trigger for action, after which the movement occurs even
though new information indicates that the movement should be
inhibited.
Exploring Further
1. Slater-Hammel’s estimate of the time required to anticipate the
sweep hand’s arrival at the 800 position is complicated by the
fact that subjects had a +26 ms constant error (CE) on the
normal trials. What are the implications of this positively
biased CE?
2. How could this stop-signal paradigm be adapted to examine
the time required to make anticipatory actions in sport tasks
such as batting a baseball?
Muscle Response Patterns
The final line of evidence supporting motor program control comes from
experiments in which patterns of muscle activity are examined when a
performer is instructed to make a brief limb action (moving a lever in the
extension direction from one position to another). Figure 5.6 shows
integrated electromyogram (EMG) tracings from a quick elbow extension
movement (Wadman et al., 1979). In the normal movement (red traces), first
there is a burst of the agonist (here, the triceps) muscle; then the triceps
turns off and the antagonist muscle (the biceps) is activated to decelerate the
limb; and finally the agonist comes on again near the end to stabilize the
limb at the target area. This triple-burst (agonist–antagonist–agonist) pattern
is typical of quick movements of this kind.
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Figure 5.6 Electromyographic results from agonist (upper traces) and
antagonist (lower traces) muscles when the subject actually produced the
movement (normal trials— red lines) and when the movement was blocked
by a mechanical perturbation (blocked trials—blue lines).
Reprinted by permission from Wadman et al. 1979.
Occasionally, and quite unexpectedly, on some trials the lever was blocked
mechanically by the experimenter so that no movement was possible. Figure
5.6 also shows what happens to the EMG patterning on these blocked trials
(blue lines). Even though the limb does not move at all, there is a similar
pattern of muscular organization, with the onset of the agonist and the
antagonist occurring at about the same times as when the movement was not
blocked. Later, after about 120 ms or so, there is a slight modification of the
patterning, probably caused by the reflex activities (e.g., stretch reflexes)
discussed in chapter 4. But the most important findings are that the
antagonist (biceps) muscle even contracted at all when the movement was
blocked and that it contracted at the same time as in the normal movements.
The feedback from the blocked limb must have been massively disrupted,
yet the EMG patterning was essentially normal for 100 ms or so. Therefore,
these data contradict theories arguing that feedback from the moving limb
(during the action) acts as a signal (a trigger) to activate the antagonist
muscle contraction at the proper time. Rather, these findings support the
motor program idea that the movement activities are organized in advance
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and run off unmodified sensory information for 100 to 120 ms, at least until
the first reflexive activities can become involved.
Motor Programs and the Conceptual Model
Motor programs are a critical part of the conceptual model seen in figure
5.2, operating within the system, sometimes in conjunction with feedback, to
produce flexible skilled actions. The open-loop part of these actions
provides the organization, or pattern, that the feedback processes can later
modify if necessary. The following are some of the major roles of these
open-loop organizations:
To define and issue the commands to musculature that determine when,
how forcefully, and for how long muscles are to contract and which
ones are to contract
To organize the many degrees of freedom of the muscles and joints into
a single unit
To specify and initiate preliminary postural adjustments necessary to
support the upcoming action
To modulate the many reflex pathways to ensure that the movement goal
is achieved
In the following sections we see research examples of how motor programs
use anticipatory and feedback information to regulate movement control.
Anticipatory Adjustments
Imagine that you are standing with your arms at your sides and an
experimenter gives you a command to raise an arm quickly to point straight
ahead. What will be the first detectable EMG (muscular) activity associated
with this movement? Most people would guess that the first contraction
would be in the shoulder muscles that raise the arm. But, in fact, the EMG
activity in these muscles occurs relatively late in the sequence. Rather, the
first muscles to contract are in the lower back and legs, some 80 ms before
the first muscle in that shoulder (Belen’kii, Gurfinkel, & Pal’tsev, 1967).
This order may sound strange, but it is really quite a “smart” way for the
motor system to operate. Because the shoulder muscles are mechanically
linked to the rest of the body, their contractions influence the positions of the
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segments connected to the arm—the shoulder and the back. That is, the
movement of the arm affects posture. If no compensations in posture were
first made, raising the arm would cause the trunk to flex, as well as to shift
the center of gravity forward, causing a slight loss of balance. Therefore,
rather than adjust for these effects after the arm movement, the motor system
compensates before the movement through “knowing” what postural
modifications will soon be needed.
There is good evidence that these preparatory postural adjustments are
really just a part of the movement program for making the arm movement
(W.A. Lee, 1980). When the arm movement is organized, the motor program
contains instructions to adjust the posture in advance as well as the
instructions to move the arm, so that the action is a coordinated whole.
Thus, we should not think of the arm movement and the posture control as
separate events; rather, these are simply different parts of an integrated
action of raising the arm and maintaining posture and balance. Interestingly,
these preparatory adjustments vanish when the performer leans against a
support, because postural adjustments are not needed here.
Integration of Central and Feedback Control
Although it is clear that central organization of movements is a major source
of motor control, it is also very clear (see chapter 4) that sensory
information can modify these commands in several important ways, as seen
in the conceptual model in figure 5.2. Thus, the question becomes how and
under what conditions these commands from motor programs interact with
sensory information to define the overall movement pattern. This is one of
the most important research issues for understanding motor control.
Reflex-Reversal Phenomenon
In addition to the various classes of reflex mechanisms discussed in chapter
4 that can modify the originally programmed output (figure 4.10), another
class of reflexive modulations exists that has very different effects on the
movement behavior. Several experiments show how reflex responses are
integrated with open-loop programmed control.
In one study, for example, the experimenter applies a light tactile stimulus to
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the top of a cat’s foot while it is walking on a treadmill. If this stimulus is
applied as the cat is just placing its foot on the surface of the treadmill (in
preparation for load bearing), the response is to extend the leg slightly, as if
to carry more load on that foot. This response has a latency of about 30 to
50 ms and is clearly nonconscious and automatic. If exactly the same
stimulus is applied when the cat is just lifting the foot from the surface (in
preparation for the swing phase), the response is very different. The leg
flexes upward at the hip and the knee so the foot travels above the usual
trajectory in the swing phase. Thus, the same stimulus has different
(reversed) effects when it is presented at different locations in the step
cycle.
These alterations in the reflex—reversing its effect from extension to flexion
(or vice versa) depending on where in the step cycle the stimulus is applied
—has been called the reflex-reversal phenomenon (Forssberg, Grillner, &
Rossignol, 1975). It challenges our usual conceptualizations of a reflex,
which is typically defined as an automatic, stereotyped, unavoidable
response to a given stimulus: Here the same stimulus has generated two
different responses.
These variations in response must occur through interactions of sensory
pathways and the ongoing movement program for locomotion (the CPG,
discussed earlier). The CPG is responsible for many of the major events,
such as muscle contractions and their timing, that occur in locomotion and
other rhythmical activities. In addition, the CPGs are thought to be involved
in the modulation of reflexes, enabling responses such as the reflex-reversal
phenomenon. The logic is that the CPG determines whether and when
certain reflex pathways can be activated in the action, as illustrated in figure
5.7a and b. During the part of the action when the cat’s foot is being lifted
from the ground (swing phase), the CPG inhibits the extension reflex and
enables the flexion reflex (i.e., allows it to be activated, Figure 5.7a). If the
stimulus occurs, it is routed to the flexion musculature, not to the extension
musculature. When the foot is being placed on the walking surface, the CPG
inhibits the flexion reflex and enables the extension reflex (Figure 5.7b). It
does this all over again on the next step cycle. Finally, notice that if no
stimulus occurs at all, there is no reflex activity, and the CPG carries out the
action “normally” without the contribution of either reflex.
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Figure 5.7 Role of CPGs in reflex reversals. In (a), the application of a
tactile stimulus at the start of the swing phase of a CPG results in movement
flexion; in (b), the application of the same tactile stimulus at the start of the
stance phase of a CPG results in movement extension. The effect of the
stimulus has been reversed.
Movement Flexibility
There is much more to be learned about these complex reflex responses, but
they undoubtedly play an important role in the flexibility and control of
skills. The cat’s reflexes are probably organized to have an important
survival role. Receiving a tactile stimulus on the top of the foot while it is
swinging forward probably means that the foot has struck some object and
that the cat will trip if the foot is not lifted quickly over the object.
However, if the stimulus is received during the beginning of stance, flexing
the leg would cause the animal to fall because it is swinging the opposite
leg at this time. These can be thought of as temporary reflexes in that they
exist only in the context of performing a particular part of a particular
action, ensuring that the goal is achieved even if a disturbance is
encountered. Analogous findings have been produced in speech, where
slight, unexpected “tugs” on the lower lip during the production of a sound
cause rapid, reflexive modulation, with the actual responses critically
dependent on the particular sound being attempted (Abbs, Gracco, & Cole,
1984; Kelso et al., 1984). The critical goal for the motor system in such
situations seems to be to ensure that the intended action is generated and that
the environmental goal (in this case, the desired speech sound) is achieved.
This adaptable feature of a movement program provides considerable
flexibility in its operation. The movement can be carried out as programmed
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if nothing goes wrong. If something does go wrong, then appropriate
reflexes are allowed to participate in the movement to ensure that the goal is
met.
Problems in Motor Program Theory: The
Novelty and Storage Problems
Open-loop control occurs primarily to allow the motor system to organize
an entire, usually rapid, action without having to rely on the relatively slow
information processing involved in a closed-loop control mode. Several
processes must be handled by this prior organization. At a minimum, the
following must be specified in the programming process in order to generate
skilled movements:
The particular muscles that are to participate in the action
The order in which these muscles are to be involved
The forces of the muscle contractions
The relative timing and sequencing among these contractions
The duration of each contraction
Most theories of motor programs assume that a movement is organized in
advance by the establishment of a neural mechanism, or network, that
contains time and event information. A kind of movement “script” specifies
certain essential details of the action as it runs off in time. Therefore,
scientists speak of “running” a motor program, which is clearly analogous to
the processes involved in running computer programs.
However, motor program theory, at least as developed so far in this chapter,
does not account for several important aspects of movement behavior.
Perhaps the most severe limitations of motor program theory are (1) the
failure to account for how novel movements are produced in the first place,
and (2) lack of the efficiency that would be required to store the massive
number of motor programs that would be required in order to move.
This capability for producing novel actions raises problems for the simple
motor program theory as we have developed it to this point in the chapter.
On this view, a given movement is represented by a program stored in long-
term memory. Therefore, each variation in a tennis stroke, for example,
associated with variations in the height and speed of the ball, the position of
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the opponent, the distance to the net, and so on, would need a unique and
separate program stored in memory because the instructions for the
musculature would be different for each variation. Extending this view
further suggests that we would need literally a countless number of motor
programs stored in memory just in order to play tennis. Adding to this the
number of movements possible in all other activities, the result would be an
absurdly large number of programs stored in long-term memory. This leads
to what has been called the storage problem (Schmidt, 1975), which
concerns how all of these separate programs could be stored in memory.
There is also the novelty problem. For example, when I am playing tennis,
no two strokes, strictly speaking, are the same. That is, every stroke
requires a very slight difference in the amount of contraction of the
participating muscles. In this sense, then, every stroke I hit is ‘novel’,
implying that the system would need a separate program for every shot.” If
motor programs that are stored in memory are responsible for all such rapid
movements, then how could something essentially novel be performed with
elegance and skill without violating the storage problem mentioned earlier?
The simple motor program theory, as presented here to this point, is at a loss
to explain the performance of such novel actions.
To summarize, these observations raise two problems for understanding
everyday movement behavior:
1. How (or where) do humans store the nearly countless number of motor
programs needed for future use the storage problem?
2. How do performers produce truly novel behavior such as performing a
variant of a tennis swing that you have never performed previously?
The program for such an action cannot be represented in an already
stored motor program: the novelty problem.
Many years ago the British psychologist Sir Fredrick Bartlett (1932), in
writing about tennis, said this: “When I make the stroke, I do not . . .
produce something absolutely new, and I never repeat something old” (p.
202). What did he mean? The first part of his statement means that, even
though a movement is in some sense novel, it is never totally brand-new.
Each of his ground strokes resembles his other ground strokes, possessing
his own style of hitting a tennis ball. The second part of Bartlett’s statement
conveys the idea that every movement is novel in that it has never been
performed exactly that way before.
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The novelty and storage problems for motor program theory discussed in the
previous section, and indeed, in explaining Bartlett’s keen insight regarding
the tennis stroke, motivated a search for alternative ways to understand
motor control. There was a desire to keep the appealing parts of motor
program theory, but to modify them to solve the storage and novelty
problems. The idea that emerged was that movement programs can be
generalized (Schmidt, 1975). This generalized motor program (GMP)
consists of a stored pattern, as before. The generalized program stored in
memory is thought to be adjusted at the time of movement execution,
allowing the action to be changed slightly to meet the current environmental
demands.
Generalized Motor Program Theory
The quote from Bartlett captures the essence of GMP theory: Some features
of the tennis stroke remain the same from shot to shot, and some features of
the stroke are changed each time. According to GMP theory, what remains
the same reflects the invariant features of a motor program—those features
that make the pattern appear the same, time after time. Invariant features are
the reason our unique writing style appears the same regardless of whether
we are using a pen to write in a notebook, a marker to write large enough on
a whiteboard for everyone in a large class to read, or our toe to write
something in the beach sand at Marina del Rey.
The aspect that allows changes from stroke to stroke (in Bartlett’s quote) is
represented in GMP theory as the relatively superficial, or surface features
of the movement. If the pattern represents the invariant features of your
writing style, then modifying what are called parameters determines how it
is executed, representing its surface features. Writing something slow or
fast, large or small, on paper or in the sand, and with a pen or a toe,
represents how the GMP is executed at any one time. The word parameter
comes from mathematics, and represents numerical values in an equation
which do not change the form of the equation. For example, in a linear
equation, whose general form is Y = a + bX, the values a and b are
parameters—Y and X are related to each other in the same way for any
values of a and b. The unique performance that occurs when certain
parameters are changed does not alter the invariant characteristics of the
GMP—the parameters change only how the GMP is expressed at any given
time.
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In GMP theory, movements are thought to be produced as follows. As
determined via sensory information processed in the stimulus identification
stage, a GMP for, say, throwing (as opposed to kicking) is chosen during the
response selection stage. This GMP is then retrieved from long-term
memory, much the same way that you retrieve your friend’s telephone
number from memory. During the movement programming stage, the motor
program is prepared for initiation, or parameterized.
One of the necessary processes here is to define how to execute this
program. Which limb to use, how fast to throw, which direction to throw,
and how far to throw must be decided based on the environmental
information available just before action. These decisions result in the
assignment (probably in the response selection stage) of movement
parameters—characteristics that define the nature of the program’s execution
without influencing the invariant characteristics (that determine its form) of
the GMP. Parameters include the speed of movement, its amplitude (overall
size), and the limb used. Once the parameters have been selected and
assigned to the program, the movement can be initiated and carried out with
this particular set of surface features.
According to GMP theory, the key variables to consider are what constitute
the invariant features of the GMP and what the parameters, or surface
features, are. These important issues are discussed in the next sections.
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Generalized motor program theory suggests that the motor program for
signing your name retains its invariant features, no matter what you are
writing on.
Invariant Features of a GMP
To begin to discover the nature of GMP representations, we need to know
what features of the flexible movement patterns remain invariant, or
constant, as the more surface features (such as movement speed, movement
amplitude, and forces) are altered. When movement time is altered, for
example, almost every other aspect of the movement changes too: The
forces and durations of contractions, the speed of the limbs, and the
distances the limbs travel all can change markedly as the movement speeds
up.
However, what if some aspects of these movements could be shown to
remain constant even though just about everything else was changing? If
such a value could be found, scientists argued, it might indicate something
fundamental about the structure of the GMP that serves as a basis for all of
these movements, thus providing evidence for how motor programs are
organized or represented in long-term memory. Such a constant value is
termed an invariance, and the most important invariance concerns the
temporal structuring of the pattern (or the pattern’s “rhythm”).
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Relative Timing
Rhythm, or relative timing, is a fundamental feature of many of our daily
activities. Of course, rhythm is critically important in such activities as
music and dance. But timing is also a key feature of many sporting activities
(such as the golf swing) and work activities (e.g., typing, hammering). There
is strong evidence to suggest that relative time is an invariant feature of the
GMP. An example is the evidence provided in the Armstrong (1970) study,
discussed in Focus on Research 5.3.
The graph in figure 5.8 shows a sample trial in which one of Armstrong’s
(1970) subjects produced a pattern from memory that was made too quickly
(red trace, overall time about 3.2 s rather than the goal movement [blue
trace], to be done in about 4.0 s). But compare the red movement pattern
with the blue goal pattern in this figure—you will notice that the whole
movement appeared to speed up as a unit. That is, each of the peaks
(movement reversals) occurred sooner and sooner in real time, but occurred
at about the same time relative to the overall time of the pattern; hence the
term relative time is used to refer to the constant occurrence of these peaks
(see Gentner, 1987, or Schmidt & Lee, 2011, for more on these issues).
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Figure 5.8 Subjects learned to make a timed movement of a lever with their
right arm. The goal movement pattern is depicted by the blue line. The trace
in red represents a trial in which the movement is made too rapidly; the
error in the timing of the reversals increases as the movement unfolds,
which is what would happen if the red trace were simply a speeded-up
version of the blue trace.
Adapted by permission from Armstrong 1970.
Relative timing is the fundamental temporal structure of a movement pattern
that is independent of its overall speed or amplitude. Relative timing
represents the movement’s fundamental “deep structure,” as opposed to the
“surface” features seen in the easily modified alterations in movement time.
This deep temporal organization in movements seems to be invariant, even
when the actions are produced at different speeds or amplitudes.
More specifically, relative timing refers to the set of ratios of the durations
of several intervals within the movement, as illustrated in figure 5.9.
Consider two hypothetical throwing movements, with movement 1 being
performed with a shorter movement time than movement 2. Imagine that you
measure and record the EMGs from three of the important muscles involved
in each action (in principle, nearly any feature of the movement could be
measured). If you measure several of these contraction durations, you can
define relative timing by a set of ratios, each of which is the duration of a
part of the action divided by the total duration. For example, in movement 1,
the ratios b/a = .40, c/a = .30, and d/a = .60 can be calculated from the
figure. This pattern of ratios is characteristic of this throwing movement,
describing its temporal structure relatively accurately.
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Figure 5.9 Hypothetical relative timing of EMG traces from three muscles
for two hypothetical throwing movements. The relative-time ratios are
computed by dividing the muscle EMG durations (i.e., b, c, and d) for each
muscle (i.e., 1, 2, and 3) by the overall movement time (i.e., a). Notice that
these ratios remain roughly constant when the movement time of the action
changes (upper versus lower panel).
Reprinted by permission from Schmidt and Lee 2011.
This set of ratios (the relative timing) stays the same for movement 2 (even
though the duration of movement 2 is longer), because the values of b/a, c/a,
and d/a are the same as in movement 1. When this set of ratios is constant in
two different movements, we say that the relative timing was invariant.
Notice that movement 2 seems to be simply an elongated (horizontally
“stretched”) version of movement 1, with all of the temporal events
occurring systematically more slowly. This will always be found when
relative timing is invariant. According to the GMP theory, movement 2 was
produced with a slower timing parameter than movement 1, so the whole
movement was slowed down as a unit but its relative timing was preserved.
One of the important principles of movement control is that, when a brief,
rapid movement is changed in terms of the speed of the action (a fast vs. a
slow throw), the size of the action (making your signature large or small), or
the trajectory of the action (throwing overarm vs. sidearm), these alterations
seem to be made with an invariant relative timing. Relative timing is
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invariant across several different kinds of “surface” modifications, so the
form of the movement is preserved even though the superficial features of it
may change. There is some controversy about whether relative timing is
perfectly invariant (Gentner, 1987; Heuer, 1988), but there can be no doubt
that relative timing is at least approximately invariant.
Focus on Research 5.3
Invariances and Parameters
An important contribution to the development of the GMP theory
was made by Armstrong (1970) in analyzing the patterns of
movements that subjects made in one of his experiments. In
Armstrong’s experiment, learners attempted to move a lever (figure
5.8) from side to side in such a way that a pattern of movement at
the elbow joint (defined in space and time) occurred, as depicted by
the blue line in figure 5.8. This goal movement (blue line) had four
major reversals in direction, each of which was to be produced at a
particular time in the action, with the total movement occupying
about 4 s.
Armstrong noticed that when the learner made the first reversal
movement too quickly (red trace), the whole movement was also
done too quickly. Notice that the red line’s first peak (at reversal)
was just a little early (at .66 rather than at .75). The discrepancy
between the actual and goal reversal times increased roughly
proportionally as the movement progressed (1.72 vs. 1.95; 2.28 vs.
2.90; and 2.94 vs. 3.59). This gives the impression that every aspect
of the movement pattern was produced essentially correctly but that
the entire pattern was simply run off too quickly.
Armstrong’s findings provided an early impetus to the development
of the idea that the motor program can be generalized (Schmidt,
1975). Here, the program controlled the relative timing of the
movement reversals. When an early reversal appeared sooner or
later than the goal time, then all of the subsequent reversals sped up
or slowed proportionally.
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Exploring Further
1. In Armstrong’s figure (figure 5.8), sketch a graph of how you
predict an action with a 4.5 s overall movement time would
look if the subject had preserved the same relative-timing
structure.
2. Suppose Armstrong’s subjects had performed the pattern again,
one month after the original sessions of practice. Which do you
think would be remembered better, the overall timing or the
relative-time structure of the pattern? Give reasons for your
answer.
Classes of Movements
You can think of an activity like throwing as a class consisting of a nearly
infinite number of particular movements (e.g., throwing a lighter object
overarm, throwing more rapidly). The theory holds that the entire class is
represented by a single GMP, with a specific, rigidly defined relative-
timing structure. This program can have parameters in several dimensions
(e.g., movement time, amplitude), making possible an essentially limitless
number of combinations of specific throwing movements, each of which
contains the same relative timing.
Locomotion represents another class of movements that could be considered
to be controlled by a GMP. However, the research by Shapiro and
colleagues suggests that, in fact, there are at least two separate GMPs for
gait, each with unique relative timings—one for walking and another for
running. Note, however, that we can speed up and slow down either the
walking or running gait selectively without having to abandon the GMP (see
Focus on Research 5.4).
Note that the relative timing actually produced by a performer can be
thought of as a kind of fingerprint unique to a particular movement class.
This pattern can be used to identify which of several motor programs has
been executed (Schneider & Schmidt, 1995; Young & Schmidt, 1990).
Focus on Application 5.2 provides more examples of how our GMPs reflect
other kinds of biological “fingerprints.”
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The relative timing of a person’s motor program for typing a name or
password has the potential to serve as verification of identity.
Focus on Research 5.4
Relative Timing in Locomotion
Shapiro and collaborators (1981) studied the shifts in relative
timing in locomotion. They filmed people on a treadmill at speeds
ranging from 3 to 12 kph and measured the durations of various
phases of the step cycle as the movement speed increased. The step
cycle can be separated into four parts, as shown in figure 5.10 a.
For the right leg, the interval between the heel strike at the left until
the leg has finished yielding (flexing) under the body’s load is
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termed extension phase 2 (or E2), and the interval from maximum
flexion until toe-off is E3; together, E2 and E3 make up the stance
phase. The interval from toe-off until maximum knee flexion is
termed the flexion phase (F), and the interval from maximum flexion
to heel strike is E1; together F and E1 make up the swing phase.
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Figure 5.10 One cycle of gait can be divided into four parts,
representing the swing and stance portions (a). These four parts of
the step cycle occupy relatively consistent relative timing within
walking and running speeds, but change between gaits (b).
Reprinted by permission from Shapiro et al. 1981.
The locomotion data shown in Figure 5.10b are expressed as the
proportions of the step cycle occupied by each of the four phases;
the duration of each phase is divided by the total step-cycle time.
When the treadmill speed ranged from 3 to 6 kph, all subjects
walked, each with a particular pattern of relative timing. About half
of the step cycle was occupied by E3; about 10% of it was occupied
by F and E2 each, with about 28% occupied by E1. Notice that as
speed increased from 3 to 6 kph, there was almost no shift in the
relative timing for any of the parts of the step cycle. When the speed
was increased to 8 kph, however, where now all subjects were
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running, we see that the relative-timing pattern was completely
different. Now E1 had the largest percentage of the step cycle
(32%), and E2 had the smallest (15%). E3, which had the largest
proportion of the step cycle in walking, was now intermediate, at
about 28%. But notice that as the running speed increased from 8 to
12 kph, there again was a tendency for these proportions to remain
nearly invariant.
The interpretation is that there are two GMPs operating here—one
for walking and one for running. Each has its own pattern of relative
timing and is quite different from the other. When the treadmill
speed increases for walking, the parameter values are changed,
which speeds up the movement with the same program while
maintaining the relative timing. At about 7 kph, a critical speed is
reached, and the subject abruptly shifts to a running program; the
relative timing of this activity is maintained nearly perfectly as
running speed is increased further.
Exploring Further
1. Suppose that Shapiro and colleagues had found that a single
GMP controlled both the walking and running gaits. How
would the graph in figure 5.10b have appeared if the findings
had supported this alternative hypothetical result?
2. Another gait that humans sometimes use is skipping. Given the
findings of Shapiro and colleagues, how would the various
parts of the step cycle appear during skipping slowly versus
quickly?
Focus on Application 5.2
Relative-Timing Fingerprints
Identity fraud has represented a major threat to security for years.
Forging someone’s signature on a check and hacking into an account
with someone’s password are just two methods used by fraudsters
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to get illegal access to wealth and information. However, the
invariant features of one’s GMP provide an important tool to combat
the problem.
A person’s signature is usually considered unique and different from
anyone else’s signature. Forging the spatial characteristics of a
signature is not a very difficult task. All the forger needs to do is
obtain the target signature, compare the illegal and legal signatures,
and continue to practice by making improvements on the
imperfections until a realistic forgery is very difficult to distinguish
from the original. A password that is typed into an account on a
computer is even easier to forge if the fraudster knows the
characters to enter. All that is needed is to enter the correct
sequence, and access is granted. However, relative timing is the
missing ingredient in both cases of fraudulent information.
Suppose, for example, that when you signed your name, the spatial
and temporal recordings of each of the various loops and cursives
in producing the letters, as well as the timing of your “t” crossings
and “i” dottings and so on, were compared to a data bank in which a
large number of your previous signatures had been stored.
According to GMP theory, the invariant characteristics of your
signature would be repeated regardless of the tool you used to sign
your name (e.g., familiar or unfamiliar pen), the surface on which
you wrote it (e.g., paper or digital tablet), or the size of the writing.
Moreover, the fraudster who had access only to the spatial
characteristics of your signature would be completely at a loss to
replicate its relative timing.
It is possible that typing your password also has a relative-timing
characteristic that is uniquely yours, especially for those such as the
authors of this book who are not trained typists. We each have our
own unique style of typing—which letters are typically contacted
with which fingers, how long each key is held down (dwell time),
and the transition times that usually occur between particular letters.
Once again, a data bank of previous executions of our passwords
would give rise to a range of overall timings of these dwell and
transition times, from which a relative-timing “profile” could be
derived and to which the fraudster would not have access.
Fortunately for us, these methods of using digital knowledge of our
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GMPs are now a reality. A Google search of terms such as
“keystroke dynamics” reveals a large number of articles about the
theory and technology underlying this security advance, as well as
information about a growing number of security firms that are
developing the industry. In many ways you can think of your
signature and passwords as relative-timing “fingerprints” that are
unique to you.
Parameters Added to the GMP
In the previous section we discussed some of the features of movement that
remain the same from one time to the next—the invariant features of the
GMP. According to the theory, surface features need to be specified each
time a movement is performed. That is, the GMP needs to be parameterized
before it can be executed. What are some of these parameters?
Movement Time
Both the Armstrong study (Focus on Research 5.3) and the study by Shapiro
and collaborators (Focus on Research 5.4) provided strong evidence that
overall movement time could be varied without affecting the relative timing
of the GMP. In Armstrong`s study, the subject who accidentally sped up the
movement pattern still retained that same timing of reversals in the
movement. And the subjects in the study by Shapiro and colleagues could
vary speeds of walking and running without disrupting the relative timing of
the step cycle. This also agrees with the common experience that we seem
to have no trouble speeding up and slowing down a given movement, such
as throwing a ball at various speeds, or writing more slowly or more
quickly. These findings indicate that, when movement time is changed, the
new movement preserves the essential temporal-pattern features of the old
movement. Therefore, both movements are represented by a common
underlying temporal (and sequential) pattern that can be run off at different
speeds. Therefore, overall movement time is a parameter of the GMP.
Movement Amplitude
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The amplitude of movements can also be modulated easily in a way that is
much like varying the time. For example, you can write your signature either
on a check or five times larger on a blackboard, and in each case the
signature is clearly “yours” (Lashley, 1942; Merton, 1972). Making this size
change seems almost trivially easy.
The handwriting phenomenon was studied more formally by Hollerbach
(1978), who had subjects write the word “hell” in different sizes. He
measured the accelerations of the pen (or, alternatively, the forces delivered
to the pen) during the production of the words. These accelerations are
graphed in figure 5.11. When the trace moves upward, this indicates
acceleration (force) away from the body; a downward trace indicates
acceleration toward the body. Of course, when the word is written larger,
the overall magnitude of the accelerations produced must be larger, seen as
the uniformly larger amplitudes for the larger word. But what is of most
interest is that the temporal patterns of acceleration over time are almost
identical for the two words, with the accelerations having similar
modulations in upward and downward fluctuations.
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Figure 5.11 Acceleration-time tracings of two instances of writing the word
“hell,” once in small script (red) and again in larger script (blue). Although
the amplitudes (which are proportional to the forces exerted on the pen) for
the two traces are markedly different, the temporal organization of the
patterning remains nearly the same in the two instances.
Adapted by permission from Hollerbach 1978.
This leads to an observation similar to the one just made about movement
time. It is easy to increase the amplitude of the movements by uniformly
increasing the accelerations (forces) that are applied, while preserving their
temporal patterning. Therefore, the same word is written with different
amplitudes can be based on a common underlying structure that can be run
off with scaled forces that govern the entire movement in order to produce
different actions of overall different sizes. Therefore, overall amplitude of
force is a parameter of the GMP.
Effectors
A performer can also modulate a movement by using a different limb – and,
hence, different muscles – to produce the action. In the signature example,
writing on a blackboard involves very different muscles and joints than
writing on a check. In blackboard writing, the fingers are mainly fixed, and
the writing is done with the muscles controlling the elbow and the shoulder.
In check writing the elbow and the shoulder are mainly fixed, and the
writing is done with the muscles controlling the fingers. Yet the writing
patterns produced are essentially the same. This indicates that a given
pattern can be produced even when the effectors – and the muscles that
drive them – are different.
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These phenomena were studied by Raibert (1977), who wrote the sentence
“Able was I ere I saw Elba” (a palindrome, spelled the same way
backward as forward) with different effectors (i.e., limbs). In figure 5.12,
line a shows his writing with the right (dominant) hand, line b with the right
arm with the wrist immobilized, and line c with the left hand. These patterns
are very similar. Even more remarkable is that line d was written with the
pen gripped in the teeth, and line e used the pen taped to the foot! There are
obvious similarities among the writing styles, and it seems clear that the
same person wrote each of them, yet the effector system was completely
different for each.
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Figure 5.12 Five samples of writing a palindrome by the same subject,
using (A) the dominant hand, (B) the dominant hand with the wrist
immobilized, (C) the nondominant hand, (D) with pen gripped by the teeth,
and (E) with the pen taped between toes of the foot.
Reprinted by permission from Raibert 1977.
This all indicates that changing the limb and effector system can preserve
the essential features of the movement pattern relatively easily. Therefore,
the selection of effectors can be thought of as a kind of “parameter” that
must be selected prior to action. There is some underlying temporal
structure common to these actions, which can be run off with different
effector systems while using the same GMP.
Summary of GMP Concepts
Some of these elements of the theory of GMPs can be summarized as
follows:
A GMP underlies a class of movements and is structured in memory
with a rigidly defined temporal organization.
This structure is characterized by its relative timing, which can be
measured by a set of ratios among the durations of various events in the
movement.
Variations in movement time, movement amplitude, and the limb used
represent the movement’s surface structure, achieved by executing the
action with different parameters, whereas relative timing represents its
deep, fundamental structure.
Even though a movement may be carried out with different surface
features (e.g., duration, amplitude), the relative timing remains
invariant.
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Whereas surface features are very easy to alter by parameter
adjustment, the deeper relative-timing structure is very difficult to
alter.
A particularly good way to understand the invariant features of a GMP with
certain added parameters is to consider movement as you would the various
components of a stereo system (see Focus on Application 5.3).
We started this section on GMPs by expressing dissatisfaction with the
simple motor program views as developed earlier in the chapter. Two
issues were considered to be especially problematic: the storage problem
and the novelty problem. The GMP theory provides solutions to both of
these problems. For the storage problem, the theory holds that an infinite
number of movements can be produced by a single GMP, so only one
program needs to be stored for each class of movement rather than an
infinite number. And for the novelty problem, the theory suggests that a
second memory representation, a schema, is the theoretical structure
responsible for supplying parameters needed at the time of movement
execution. Note that, by using a parameter not used before, the person can
produce a novel action. Much more will be said about schema development
in later chapters. But, for now, think of the schema as a mechanism
responsible for selecting the parameters for the chosen GMP.
Focus on Application 5.3
The Stereo System Analogy
A good analogy for GMPs involves the standard phonograph/stereo
system, in which a turntable sends signals from a record into an
amplifier, whose output is delivered to speakers. In this analogy,
illustrated in the top portion of figure 5.13, the phonograph record
itself is the GMP, and the speakers are the muscles and limbs. (Does
anyone remember what a record is?) The record has all of the
features of programs, such as information about the order of events
(the guitar comes before the harmonica), the temporal structure
among the events (i.e., the rhythm, or relative timing), and the
relative amplitudes of the sounds (the first drumbeat is twice as loud
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as the second). This information is stored on the record, just as
GMP theory says that the analogous information is stored in the
program. Also, there are many different records to choose from, just
as humans have many motor programs to choose from (e.g.,
throwing, jumping), each stored with different kinds of information.
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Figure 5.13 Illustration of the stereo system analogy.
Notice, though, that the record’s output is not fixed (lower portion of
figure 5.13): The speed of output can be changed if the speed of the
turntable is increased. Yet relative timing (rhythm) is preserved
even though the speed of the music is increased. You can change the
amplitude of the output by raising the volume uniformly; this
increases the amplitudes of all the features of the sounds. Also, you
have a choice of which effectors to use: You can switch the output
from a set of speakers in the den to a second set of speakers in the
living room; this is analogous to hammering either with the left hand
or with the right, or with a different hammer, still using the same
pattern. Perhaps if you think of the theory of GMPs in concrete terms
like a stereo system, you can understand most of the important
features of the theory more easily. For example, when subjects in the
study by Shapiro and colleagues switched from walking to running
(Focus on Research 5.4), they first had to remove the walking
“record” and replace it with a running “record.” Then they had to
parameterize it, like setting the volume, speed, and speaker controls.
This analogy of the GMP and its parameters to the characteristics of
a stereo system sometimes helps people to understand the basic
idea.
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Summary
In very brief actions, there is no time for the system to process feedback
about errors and to correct them. The mechanism that controls this type of
behavior is open loop, called the “motor program.” This chapter is about
motor programming activities. Considerable evidence supports the motor
program idea: (a) Reaction time is longer for more complex movements; (b)
complex movements can be elicited in their complete form by certain
stimuli; (c) animals deprived of feedback information by deafferentation are
capable of strong, relatively effective movements; (d) some cyclical
movements in animals are controlled by inherited central pattern generators;
and (e) a limb’s electrical muscle activity patterns are unaffected for 100 to
120 ms when the limb is blocked by a mechanical perturbation.
Even though the motor program is responsible for the major events in the
movement pattern, there is considerable interaction with sensory processes,
such as the organization of various reflex processes to generate rapid
corrections, making the movement flexible in the face of changing
environmental demands. Finally, motor programs are thought to be
generalized to account for a class of actions (such as throwing), and
parameters must be supplied to define the way in which the pattern is to be
executed (such as throwing either rapidly or slowly). The schema concept
and how a schema is acquired with experience is discussed extensively in
chapter 10.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 5.1: Indicate whether each in a list of statements applies to
simple motor program theory or general motor program theory.
Activity 5.2: Determine whether motor skills are controlled by open-
loop or closed-loop processes, or a combination of both.
Activity 5.3: Review the conceptual model of motor control by
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identifying which elements are associated with open-loop control
processes only, closed-loop control processes only, or with open- and
closed-loop processes.
Principles-to-Application Exercise
Activity 5.4: The principles-to-application exercise for this chapter
prompts you to choose a skill and identify components of the movement
that a person would control using open-loop and closed-loop
processing as well as situations in which both types of control would
be important.
Check Your Understanding
1. Name the two distinct parts of an open-loop control system. How does
an open-loop control system differ from a closed-loop control system?
Describe how each part of an open-loop control system might function
for a child tossing toy blocks into a bin.
2. Research evidence for the existence of motor program control comes
from diverse research areas. List the six types of research evidence
and discuss how two of these areas provide evidence for movements
being planned in advance.
3. Though there were appealing parts of motor program theory, a desire to
modify motor program theory to solve the storage and novelty
problems arose. What idea emerged from this desire? How does it
help to explain novel movements? How does it deal with the storage
problem?
Apply Your Knowledge
1. A student is packing her lunch for school. List three movements (or
components of movements) involved in packing a lunch that would be
controlled using open-loop processes and three that would be
controlled using closed-loop processes. Choose one of the open-loop
controlled movements and describe a parameter of the generalized
motor program that the student could modify.
2. A woodworker is building a piece of furniture that includes large,
small, simple, and complex pieces. Describe two GMPs that may be
used in building the furniture, and discuss two parameters that the
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woodworker might need to modify throughout the project for each
GMP.
Suggestions for Further Reading
Keele (1968) has provided a historical review of the motor program
concept. Selverston (2010) reviews CPGs in invertebrate models. The
effects of response complexity on RT are reviewed by Christina (1992).
More on the startle RT paradigm can be found in Carlsen et al. (2011).
Schmidt introduces the concept of GMPs (1975) and later provided reviews
of the evidence for them (1985). A more thorough treatment of all of the
issues discussed in this chapter can be found in chapter 6 of Schmidt and
Lee (2011). See the reference list for these additional resources.
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Chapter 6
Principles of Speed, Accuracy, and
Coordination
Controlling and Timing Movements
Chapter Outline
Speed–Accuracy Trade-Offs
Sources of Error in Rapid Movements
Exceptions to the Speed–Accuracy Trade-Off
Analyzing a Rapid Movement: Baseball Batting
Accuracy in Coordinated Actions
Summary
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Chapter Objectives
Chapter 6 describes various principles and laws of simple and
coordinated actions. This chapter will help you to understand
the speed–accuracy trade-off in simple aiming movements,
logarithmic and linear relationships between speed and
accuracy,
the relationship between timing accuracy and movement time,
and
principles of bimanual timing and the role of self-organizing
principles.
Key Terms
amplitude
anti-phase
effective target width (We)
Fitts’ Law
index of difficulty
in-phase
self-organization
speed–accuracy trade-off
width
The construction worker is pounding nails into the shingles of a new roof
when she notices a storm approaching. She quickens her pace, but in so
doing, she notices that her aims are missing the nail more and more often—
something that occurs rarely when she is working at her normal pace. Why
is this happening? How does working at a faster pace and swinging her
hammer with more force contribute to more frequent misses?
This chapter addresses questions such as these about movement control.
Some of the most fundamental principles of movement production—
analogous to the simple laws of physics—are shown to govern the
relationship between movement speed, distance, and accuracy. Along the
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way, we reveal some of the underlying causes of errors in movements and
discuss ways to minimize these errors. These laws of movement production
are considered first as they apply in the control of relatively simple
movements; later in the chapter we discuss some of the ideas related to
more complex, coordinated actions.
The first several sections of this chapter deal with the laws or principles of
simple movements, describing fundamental relationships such as how the
time required for a movement changes as the distance to be moved
increases, and how accuracy is affected by movement speed. These basic
principles form the foundation of much of our knowledge about movements.
One of the most fundamental principles concerns the relationships among the
speed of a movement, its amplitude, and the resulting accuracy.
Speed–Accuracy Trade-Offs
Everybody knows that when you do things too quickly, you tend to do them
with less accuracy or effectiveness. The old English saying “Haste makes
waste” is evidence that this idea has been prevalent for many centuries.
Woodworth (1899) studied these phenomena early in the history of motor
skills research, showing that the accuracy of line-drawing movements
decreased as their length and speed were increased. A major contribution to
our understanding of this problem was provided in 1954 by the psychologist
Paul Fitts, who described for the first time a mathematical principle of
speed and accuracy that has come to be known as Fitts’ Law.
Fitts’ Law
Fitts used a paradigm in which the subject tapped alternately between two
target plates as quickly as possible. The separation between the targets (A,
or movement amplitude) and the width of the targets (W, or target width)
could be varied in different combinations (see figure 6.1). The movement
time (MT) taken to complete these rapid taps increased systematically with
either increases in the movement amplitude (due to a larger distance
between the targets) or decreases in the target width (due to a smaller
target-landing area). These relationships were combined into a formal
mathematical statement that is now known as Fitts’ Law (see Focus on
Research 6.1).
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Figure 6.1 Illustration of a subject performing a Fitts tapping task. The
subject taps between two targets of varying width (W) and with varying
amplitude between them (A), attempting to move as rapidly as possible
while minimizing the number of target misses.
Adapted from Fitts 1954.
Fitts’ Law states that MT is constant whenever the ratio of the movement
amplitude (A) to target width (W) remains constant. So, very long
movements to wide targets require about the same time as very short
movements to narrow targets. In addition, Fitts found that the MT increased
as the ratio of A to W increased by either making A larger, making W
smaller, or both. He combined these various effects into a single equation:
MT = a + b [Log2(2A/W)]
where a and b are constants (the MT-intercept and slope, respectively) and
A and W are defined as before. The relationships between A, W, and MT are
plotted in figure 6.2 for one of Fitts’ data sets. The term Log2(2A/W) is
referred to as the index of difficulty (abbreviated ID), which seems to
define the “difficulty” of the various combinations of A and W. Therefore,
Fitts’ Law says that MT is linearly related to the Log2(2A/W), or that MT is
linearly related to the index of movement difficulty (ID).
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Figure 6.2 Average movement time (MT) as a function of the index of
difficulty (ID).
Reprinted by permission from Schmidt and Lee 2011; Data from Fitts 1954.
An important point is that Fitts’ Law describes the tendency for performers
to trade speed for accuracy. In what has now become the “typical” Fitts
tapping task, subjects are instructed explicitly to minimize the number of
target misses. In other words, they are instructed to adjust movement time so
that the errors are acceptably small. Thus, when the target size is increased,
the accuracy requirements are relaxed and MTs are smaller than when
narrow targets are used. This has led to the general notion of a speed–
accuracy trade-off —the tendency for people to “give up” speed in order
to trade speed off for acceptable levels of accuracy—as one of the most
fundamental principles of movement behavior.
Fitts’ Law, which describes MT as a function of the movement distance and
the accuracy requirements of a task, has been found to hold under many
different conditions (tapping underwater as well as in outer space), for
many different classifications of people (children, older adults, individuals
with neurological impairments), and for movements made by different body
parts (hand-held, foot-held, and even head-mounted pointing devices) (see
Schmidt & Lee, 2011; Plamondon & Alimi, 1997). Fitts’ Law also applies
in many tasks of everyday living (see Focus on Application 6.1).
The movements studied with the Fitts tapping task are almost certainly
blends of programmed actions with feedback added near the end. That is, in
these movements the performer generates a programmed initial segment of
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the action toward the target, probably processes visual feedback about the
accuracy of this action during the movement, and initiates one (or sometimes
more) feedback-based corrections to guide the limb to the target area
(Keele, 1968). As discussed in chapter 4, such visual compensations are
probably processed through the dorsal visual stream and might not be
controlled consciously. Thus, Fitts’ Law describes the effectiveness of the
combined open- and closed-loop processes that operate in these common
kinds of actions, where potentially all of the open- and closed-loop
processes shown in the conceptual model in figure 4.10 are operating
together.
Finally, it is reasonable to suspect that slower movements are more
accurate, at least in part, because there is more time to detect errors and to
make corrections (as discussed in chapter 4), and that movement time
lengthens when the number of corrections to be made increases. In this way,
the main reason MT increases with narrow target widths is that each
correction takes a finite amount of time, and the times for multiple
corrections each contribute to MT. Meyer and colleagues (1988) provided a
formal model of processes involved in the speed–accuracy trade-off that
extends our understanding of Fitts’ principles.
Wishing to extend their ideas to tasks that are more typical and realistic,
Fitts and Peterson (1964) used the same idea and variables as in the
reciprocal-tapping task (figure 6.1), but used them with movements in which
a single action was required from a starting position to a single target.
These targets were located various distances (A) from the starting position
and were of different sizes (W). As in the reciprocal paradigm, these single
actions were to be done as rapidly as possible while maintaining an
“acceptable” (to the experimenter) rate of error. The independent variables
A and W and the dependent variable MT were related to each other in
essentially the same way as they were in the reciprocal task. That is to say,
the equation for Fitts’ Law also applied to the single-movement paradigm,
which increases our confidence that Fitts’ Law is one of the truly
fundamental laws of motor behavior.
In brief, Fitts’ Law tells us the following:
Movement time (MT) increases as the movement amplitude (A)
increases.
MT increases as the aiming accuracy requirement increases, that is, as
target width (W) decreases.
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MT is essentially constant for a given ratio of movement amplitude (A)
to target width (W).
These principles are valid for a wide variety of conditions, subject
variables, tasks or paradigms, and body parts used.
However, a number of other questions remained unanswered. What about
movements that are completed in a very short period of time, where
presumably no feedback is involved during the movement? How can MT
depend on the number of corrections when there isn’t enough time to make
even a single correction? Some of these questions are answered in the next
section.
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How can Fitts’ Law help to explain the varied sizes of keys on a keyboard?
Focus on Research 6.1
Fitts Tasks
In his most well-known experiment, Fitts (1954) asked subjects to
make movements of a handheld stylus between two target plates. In
this task, which is now typically known as the Fitts tapping task (see
figure 6.1), the widths (W) of each target and the amplitude (A)
between the targets were varied in different conditions, and the
subject’s goal was to alternately tap each target as quickly as
possible while making as few errors as possible (usually less than
5% misses). The experimenter would measure the number of taps
completed in, say, a 20 s trial, and then compute the average time
per movement, or movement time (MT).
However, this typical Fitts task is not the only variation of
reciprocal movements that has been studied in this type of rapid
aiming paradigm. In fact, Fitts studied two lesser-known variants in
his classic paper (Fitts, 1954). Figure 6.3 illustrates these tasks. In
figure 6.3a, the subject’s task was to move small metal discs with
holes in the center (like carpenters’ washers) from one peg to
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another. In figure 6.3b the task was to move small pegs (like the
pegs used in the game of cribbage) from one hole to another. In both
of these task variations, Fitts defined the ID in terms of the
“clearance” between the target pegs and the washers (part a) or the
diameter of the holes in the plate in relation to the diameter of the
peg (part b). Defined in this manner, Fitts found that the same
equation [MT = a + b (ID)] held well in accounting for the effects of
the task parameters of movement speed.
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Figure 6.3 Alternative reciprocal-movement tasks used by Fitts
(1954). (a) Disc-transfer task; (b) pin-transfer task.
Adapted from Fitts 1954.
How do all of these experimental tasks converge to define Fitts’
Law? The first part is easy—amplitude is the distance-covering
portion of MT and is common to each task. The target size is more
complicated. In the aiming task, this is essentially just target width.
However, in the disc-transfer (figure 6.3a) and peg-transfer tasks
(figure 6.3b), the target size is operationalized as the difference
between sizes of the object and the target. For example, in the peg-
transfer task, a large hole only represents an easy ID if the peg being
transferred is relatively narrow. If the peg is wide, then the task
becomes more difficult because there is less tolerance for error.
Thus, all three of these tasks converge nicely upon the central
problem of the speed–accuracy trade-off—how the task parameters
cause the subject to vary MT in order to make the end product of the
aimed movement accurate.
Exploring Further
1. What would be the ID for a tapping task that had W = 4 and A
= 16?
2. What changes in the foot’s travel time from the accelerator to
the brake pedal would you expect to see if you doubled the size
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of the brake pedal?
Focus on Application 6.1
Fitts’ Law in Everyday Actions
Fitts’ Law has many obvious applications in sport, in the design of
industrial workspaces, and in the organization of controls in
automobiles, aircraft, and so on. One example, of which you might
not be aware, is the design of keyboards and calculators. Have a
look at the keyboard on your computer, cell phone, or other text
input device. If the layout uses the principles consistent with Fitts’
Law, you will notice that some keys are much larger than others
(e.g., “Enter,” “Backspace,” and “Shift”). For example, the space
bar on most keyboard layouts is much larger than any of the other
keys. Having a larger key means that we can make the movements to
frequently pressed keys (“Backspace” and “Enter” are others) very
quickly without the risk of making an error. In other words, we can
sacrifice a considerable amount of precision if we aim at a
relatively large, as opposed to relatively small, key. This feature
allows us to move very quickly to these often-pressed keys. What
other keys on your keyboard are afforded the same “privilege”?
Does your calculator or cell phone keyboard layout have similar
advantages for one or more keys?
Navigating a cursor around a computer monitor also uses the
principles of Fitts’ Law, as noted in some pioneering work by Card,
English, and Burr (1978). For example, the size of an icon affects
the time to “acquire” it; icons that are increased in size as the cursor
approaches them also influence MT and error, and the size and
distance of pop-up menus have obvious implications for time and
errors when we are aiming the cursor at them.
Some designs use Fitts’ Law in the opposite way too. For example,
the next time you navigate to a website with a pop-up advertisement
that can be closed by clicking the “x” icon in one of the corners,
note how small the “x” is and whether or not it is moving or
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stationary. Presumably, the longer it takes for you to get your cursor
over the icon before clicking the mouse button, the longer the
information on the screen will have been there for you to process it
(perhaps unwillingly). In this way, the design purposely reduces the
size of the target to make the user slow down. How many other
applications can you think of in which the designer’s intention is to
make you move more slowly in order to be accurate?
The Linear Speed–Accuracy Trade-Off
Suppose that you were to make a quick action to move your hand or an
object, as in the example of swinging a hammer at the start of this chapter.
How does your accuracy change as the time for the movement and the
distance of the movement vary? Such factors have been studied in aiming
movements, where the subject directs a handheld stylus from a starting
position to a target, with MT and movement distance being varied
experimentally. The subject is instructed to move with a given MT (e.g., 150
ms) and receives feedback after each movement to help maintain the proper
MT. One set of results from this kind of task is shown in figure 6.4, where
accuracy is expressed as the amount of “spread” or inconsistency of the
movement end points about the target area. This measure, called effective
target width (We), is the standard deviation of the target end points. This
measure is analogous to the target size that the subject “actually used” in
making the action with the required MT. (Note that We is used here as a
variant of Fitts’ W.)
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Figure 6.4 Variability of movement end points (defined as the standard
deviation of the produced movement distances, or effective target width,
We) in a rapid aiming task as a function of MT and distance.
Reprinted by permission from Schmidt et al. 1979.
Notice from the legend in figure 6.4 that these movements are very fast, with
all MTs of 200 ms or less. From the previous chapters, you would expect
that such actions are controlled primarily by motor programming processes,
with negligible feedback-based corrections. Even with these quick actions,
as the movement distance increases, there is a gradual increase in the spread
of the movements, that is, their inaccuracy around the target for each of the
different MT conditions (e.g., compare the We for the 140 ms MT condition
for the three different distances). Similarly, the inaccuracy of the movement
increases as the MT is reduced at each of the distances (e.g., compare the
We for the three MT conditions at the 30 cm distance). These two effects are
more or less independent, as if the effects of increased distance can be
added to the effect of reduced MT to produce aiming errors.
These effects of movement distance and MT suggest that open-loop
processes necessary to produce the movement are also subject to the speed–
accuracy trade-off. That is, the decreases in accuracy when MTs are short
are not due simply to the fact that there is less time for feedback utilization;
these effects of MT occur even in movements too brief to have any feedback
modulations at all. Decreases in MT also seem to have effects on the
consistency of the processes that generate the initial parts of the movement,
that is, on the open-loop processes necessary to produce quick movements.
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This is consistent with the ideas from Fitts’ Law. In that situation, if the
subject tries to make movements of a given distance too quickly, the result
will be too many failures to hit the targets (which is unacceptable in terms
of the experimenter’s instructions that generally demand errors on no more
than about 5% of the movements). So, the subject must slow down to
comply with the experimenter’s instructions, decreasing the variability in
the movements and hitting the target more often.
These separate effects of movement amplitude and MT can be combined
into a single expression, more or less as done by Fitts. Research from the
first author’s lab showed that the amount of movement error (We, the effective
target width) was linearly related to the movement’s average velocity, that is, to
the ratio A/MT (Schmidt et al., 1979). For example, in figure 6.5 the
variability in hitting the target is plotted against the movement’s average
velocity (in centimeters per second, or cm/s), showing that, as the movement
velocity increased, the aiming errors increased almost linearly as well. This
principle, the linear speed–accuracy trade-off, suggests that, for various
combinations of movement amplitude and MT that have a constant ratio (that
is, a constant average velocity), the aiming errors are about the same. Thus,
increases in movement distance and decreases in MT can be traded off with
each other to maintain movement accuracy in these rapid tasks.
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Figure 6.5 Variability of movement end points (We) in a rapid aiming task
as a function of average movement velocity (A/MT).
Reprinted by permission from Schmidt et al. 1979.
Sources of Error in Rapid Movements
Why do very rapid movements, in which there is little time for feedback
processing and corrections, produce more errors as the movement distance
increases or the MT decreases? The answer seems to lie with the processes
that translate the motor program’s output in the central nervous system into
movements of the body part. Earlier sections showed that motor programs
are responsible for determining the ordering of muscle contractions and the
amounts of force that must be generated in the participating muscles. How
might these sources of error contribute to movement inaccuracy?
It has been known for a long time that even if the performer attempts to
produce the same force over and over on successive trials, the actual force
produced will be somewhat inconsistent. This variability is thought to be
caused by the relatively “noisy” (i.e., inconsistent) processes that convert
central nervous system impulses into the activation of muscle motor units,
which ultimately exert forces on bones, thus causing movements. Also, there
is variability in the contractions generated by various reflex activities.
The presence of these “noisy” processes in the system means that the forces
actually produced in a contraction are not exactly what were intended by the
motor program level. This can also be seen in the phonograph record
analogy presented in chapter 5, where noise can be introduced in several
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places in the stereo system, such as scratches on the record, noise inherent
in the electronics and wires that run from the turntable, and quality of the
speakers. These deviations from perfect fidelity in the stereo system make
the sounds heard through the speakers slightly different than the sounds as
originally recorded.
In movement control, these noisy processes are not constant; they change as
the amount of contraction force changes. This has been studied using tasks in
which the subject is asked to produce brief (“ballistic”) force applications
to an apparatus handle; these force applications are such that the peak force
produced on any contraction matches a (submaximal) goal force. Figure 6.6
has a typical set of results. Notice that as the contraction force increases,
there is more variability in these forces, as if the noisy processes were
becoming larger as well. In the figure, the variability in these forces (i.e.,
the within-subject standard deviation of the force productions), which is
interpreted as the size of the noise component, is shown as a function of the
size of the contraction, expressed as a percentage of the performer’s
maximum force. The noise component generally increases as the amount of
force increases, up through about 70% of the subject’s maximum. However,
when the contractions are very large, approaching maximal values, the
amount of force variability appears to level off again, with perhaps a slight
decrease in the force variability in nearly maximal contractions (Sherwood,
Schmidt, & Walter, 1988).
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Figure 6.6 The relationship between the variability in force produced as a
function of the percentage of maximum force used.
How does this information help in understanding error generation? To
extend the example discussed at the beginning of the chapter, consider a
movement like hammering a nail into a wall by swinging the hammer with
the arm and hand. In such a movement, many muscles operate on the hands,
arms, and upper torso to produce forces against the bones, which direct the
hammer toward the nail. The direction of action of some of these muscles
may happen to be lined up with the intended movement, but most of them are
not; rather, the muscles are aligned at various angles to the action, as shown
in figure 6.7. And, in many actions such as this, gravity acts as one of the
contributing forces as well. To complete such an action perfectly, the
various muscles must contract with just the right amounts of force, in
coordination with each other, so that the resultant force is in line with the
intended movement. Of course, if any of these forces is substantially in
error, for example if the contraction of muscle 1 is too great, the movement’s
direction will be in error as well, with the movement missing the target.
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Figure 6.7 A hammer, swung at a nail on a vertical board by an arm and
hand, is influenced by many forces.
Now, what happens when a given movement is made more rapidly? Of
course, as the MT decreases, the forces exerted against the bones of the arm
must increase. When these forces increase (up to about 70% of maximum),
figure 6.6 shows that the “noisiness” of these forces increases as well. This
has the effect of adding a slight error component (with the variations in
muscle force being independent) to the contraction of each of the involved
muscles, causing them to contract slightly differently from how the
movement program intended. If these forces are no longer perfectly
coordinated with each other, the movement will tend to miss its target. Thus,
the movement’s inaccuracy increases as MT decreases, primarily because of
the increased noise involved in the stronger muscle contractions.
In summary, this is why increasing the speed of a rapid movement
contributes to its inaccuracy:
The relative contraction forces of the various participating muscles are
a major factor in determining the ultimate trajectory of the limb.
The inconsistency in these forces increases with increased force.
When MT decreases, more force is required.
When amplitude increases, more force is required.
More force generates more variability, which causes the movement to
deviate from the intended trajectory, resulting in errors.
Exceptions to the Speed–Accuracy Trade-
Off
As common as the speed–accuracy trade-off seems to be for movement
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behavior, there are a few situations in which it does not appear to hold, or at
least in cases in which the principles are somewhat different from those
indicated in the previous sections. These situations involve cases in which
(a) extremely rapid and forceful actions are involved and (b) accuracy in
timing is the action’s critical feature.
Very Forceful Movements
Many human movements, especially those in sport, require extremely
forceful contractions of muscles, leading to nearly maximal movement
speeds, as in kicking a football or hitting a golf ball. Making the movement
at near-maximal speed is often only a part of the problem because these
actions often must be performed with great precision in space and time. As
it turns out, alterations in movement speed affect these nearly maximal
actions somewhat differently from many of the less forceful actions
discussed so far.
Consider a rapid, horizontal, straight-arm movement in which a handheld
pointer is aimed at a target as if it were a ball to be hit. What would happen
to the spatial accuracy if the required MT decreased, so the movements
would be closer and closer to the performer’s maximal force capabilities?
This is similar to your swinging of a hammer harder and harder, with the
limit being your own force capabilities. As you might expect from the
speed–accuracy trade-off principles, movements with shorter MTs are less
spatially accurate, but only up to a point, as seen in figure 6.8. When the MT
was reduced further, from 102 to 80 ms (moving leftward on the graph from
158 ms to 102 ms, or from 21% to 50% of the subject’s maximum force
capability), which increased the percentage of maximum force capability
required to improve, but, from figure 6.8, decreasing the MT further from
102 to 80 ms increased the percentage of maximum force form 50% to 84%.
Also raising the percentage of maximum force from 50% to 84% decreased
the variability in force (see figure 6.6). Thus, very rapid and very slow
movements have the most spatial accuracy and the moderate-speed
movements having the least accuracy. This set of data goes against the strict
view of the speed–accuracy trade-off, in which faster movements are
always less spatially accurate.
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Figure 6.8 The effect of movement time (MT) on the positional variability
in horizontal arm swing movements. The percentage values above the x-axis
(corresponding to mean MTs) are the percentages of the subject’s maximum
force produced over those MTs. Thus, the movement becomes increasingly
accurate with increasing MTs.
Reprinted by permission from Schmidt and Sherwood 1982.
How can these movements be made so rapidly yet be so spatially accurate?
These movements are very much like those illustrated in figure 6.7, where
several muscles operate in coordination to determine the limb’s trajectory.
Also recall that, when the forces are very large, approaching maximum, the
force-variability levels off and actually decreases slightly, as seen in figure
6.6 by the small downturn near the highest levels of force. Therefore, the
nearly maximal movements in figure 6.8 are operating in a range where the
forces are becoming more consistent with increases in force. This low
force variability allows these very forceful actions to be very consistent
spatially.
In summary, here’s how the theory attempts to explain what happens when a
movement requires very high levels of muscular contractions (greater than
about 70% of the subject’s capabilities):
Increasing speed by reducing MT can decrease spatial and timing
error.
Because a greater muscular force requirement actually increases
accuracy in this range, adding inertial load to the movement can
decrease error, up to a point.
An inverted-U relationship exists between spatial accuracy and force
requirements, with least accuracy at moderate levels of force.
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Very forceful movements performed at nearly maximal speed are an
exception to the speed–accuracy trade-off.
Movement Timing
In previous sections the concern was with situations in which spatial
accuracy was the major goal, and we showed how this changes as
movement velocity changes. However, for many skills the main goal is
temporal accuracy (e.g., batting a baseball). In such skills a movement must
be timed so that some part of it is produced at a particular moment (e.g., the
bat must cross the plate at the same time the ball is there). The timing
accuracy is critical to the success of the movement. For example, a perfectly
struck chord on the guitar makes the most contribution to the music when it
is timed just right.
Still other skills have both temporal and spatial goals, intermixed in
complicated ways. Of course, batting a pitched baseball requires accuracy
in terms of where to swing to meet the ball (spatial) as well as when to
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swing (temporal). But an important skill in batting demands that the
performer be able to time the duration of the swing. Knowing or predicting
the duration of the swing is critical in order for the batter to determine when
to initiate the swing so the bat arrives over the plate coincident with the
arrival of the ball. So, being able to make a fast movement that occupies a
predictable amount of time is a critical factor in batting effectiveness.
In this section we are concerned with the temporal component of such skills,
discussing the factors that affect timing accuracy. The temporal component
can be isolated somewhat in the rapid task in which the performer makes a
fast movement, whose goal is to produce a particular MT as accurately as
possible. Timing accuracy is studied as a function of changes in MT, as well
as other variables. It turns out that skills with purely temporal goals seem to
follow somewhat different principles than those having purely spatial goals
(Schmidt et al., 1979).
What happens when subjects are asked to produce one of these movements
of a given distance, but with the MT goal reduced from 300 ms to 150 ms?
One might expect that because the velocity of the movement is larger, it
would have more error, as was found in figures 6.4 and 6.5. Not so.
Decreasing the MT has the effect of decreasing the timing error, making the
movement more accurate in time, not less. This can be seen in figure 6.9, in
which variability in timed actions increases almost linearly with increases
in goal MT; halving the goal MT (within limits) almost tends to halve the
timing errors. An additional finding is that this relationship for MT and
timing variability holds not only for discrete, single-action movements but
also for repetitive movements (Wing & Kristofferson, 1973). Therefore, for
skills in which timing error has to be reduced, the main factor is the MT,
which is quite different from the situation for skills with spatial goals as
demonstrated in figures 6.4 and 6.5 (see Schmidt et al., 1979).
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Figure 6.9 The effect of average MT duration on the variability of timing.
As MT decreases (i.e., movements are made faster), the variability of
timing decreases (i.e., becomes more stable).
Reprinted by permission from Schmidt, Zelaznik, and Frank 1978.
These findings about timing errors are not as strange as they seem at first, as
you will see if you do the following simple demonstration. Use the timer
function on a cell phone or watch and, without looking at it, start and stop
the timer in order to generate exactly 10 s. Do this 10 times in total and
record the amount of error you make on each trial. Next do 10 trials of the
task again, but this time try to generate 5 s. Compare the error measures
generated from the 10 trials on the two tasks. You will likely find that the
amount of error you make in estimating 5 s will be about half the amount of
error for 10 s. Why? The system that generates these durations (including
both the stopwatch and arm movement tasks) is “noisy” or variable, and the
amount of this variability increases, or accumulates, as the duration of the
event to be timed increases.
Analyzing a Rapid Movement: Baseball
Batting
It may seem from the previous section that sometimes contradictory
principles are involved in these rapid actions. To help in understanding, it
will be useful to apply these principles to a familiar task like batting in
baseball. This task requires several of the processes discussed so far, such
as anticipation and timing, prediction of the ball’s spatial trajectory and its
arrival time at the coincidence point, and rapid movements that must be both
forceful and accurate, so the principles can be applied to various parts of
this action. To examine the effects of altering the MT of the swing of the bat,
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let’s assume that some factors are held constant, such as the nature of the
pitch and the situation in the game.
A few facts about the timelines involved in hitting a baseball are
summarized in figure 6.10. In elite skill–level baseball, a 90 mph (145 kph)
pitch requires about 460 ms to travel from the pitcher to the plate, and the
MT of the swing of the bat is about 160 ms (Hubbard & Seng, 1954).
Evidence presented earlier showed that the internal signal to trigger the
swing occurs about 170 ms before the movement starts (Slater-Hammel,
1960; review figure 5.5b and Focus on Research 5.2). With these process
durations combined, the signal to trigger the action must be given about 330
ms before the ball arrives at the plate—that is, 170 ms to prepare the swing
plus 160 ms to carry it out. Therefore, the decision about whether or not to
swing at the ball must be made well before the ball has traveled halfway to
the plate, or after only 130 ms of ball travel. Although some late, visually-
based corrections in the movement are possible, as discussed in chapter 4,
the majority of the action must be planned in advance and initiated by the
central nervous system some 330 ms before the ball arrives. Making
decisions relative to the occurrence of these critical times plays a decisive
role in a batter’s success in hitting a pitched ball and also in making changes
to an initial decision to swing (see Focus on Application 5.1 for more on
checked swings).
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Figure 6.10 Timeline of events as a baseball leaves the pitcher’s hand and
arrives at the plate. The pitch is traveling at a velocity of 90 mph. A “fast”
swing (140 ms) has 20 ms less MT than a slower swing (160 ms).
An important consideration, given the previous discussion of speed and
accuracy processes in the chapter, is this: What would happen if the batter
could speed up the swing, say from 160 ms to 140 ms? The bat swing’s MT
could be made shorter through instructions or training to make the actual
movement faster, through shortening the movement distance by reducing the
backswing (a very slight effect), through using a lighter bat, or through
changing the biomechanics of the movement in various ways. Reducing the
bat swing MT by 20 ms would affect several separate factors discussed in
the previous few sections.
Visual Information Processing
Figure 6.10 shows that shortening the MT delays the beginning of the swing,
hence the point at which the details of the action have to be specified, to a
position several feet later in the ball’s flight. This provides additional time
for viewing the ball’s trajectory and for determining time to contact, and
should allow more accurate anticipation of where and when the ball will
arrive. And this extra information comes at a point that is maximally useful
—when the ball is closer to the batter—making these extra 20 ms of
viewing time particularly beneficial. Therefore, shortening the MT provides
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more effective anticipation of the ball’s trajectory.
Swing-Initiation Timing Accuracy
If the swing of the bat is speeded up, the decision about when to initiate the
movement is made later and is more temporally accurate. In an experiment
on a simulated batting task, shortening the MT stabilized the initiation time
of the movement, as if the batter were more certain of when to start the
swing (Schmidt, 1969). Starting the swing at a more stable time therefore
translates into a more stable time for the movement end point at the plate,
which yields greater movement timing accuracy.
Movement Timing Accuracy
One process the batter must go through in planning the swing is to estimate
the duration of his own movement (Poulton [1974] termed this “effector
anticipation”). Therefore, the batter selects a MT, then initiates this action at
such a time that the “middle” of the movement coincides with the arrival of
the ball at the plate. If the actual MT is different from the one predicted, the
“middle” of the movement will be too early or late, causing timing errors in
hitting the ball. Because reduced MT increases movement timing
consistency (figure 6.9), the movement’s actual duration will be closer to the
batter’s estimate. This results in greater accuracy in hitting the ball,
particularly in terms of the timing aspects (see also Schmidt, 1969).
Movement Spatial Accuracy
Making the movement faster also influences spatial accuracy, as discussed
earlier. If the movement is already relatively slow, instructions to decrease
the MT have a detrimental effect on accuracy in hitting the ball. However,
most bat swing movements are already quite fast, near the performer’s limits
in producing force. Recall that when movements are very fast and forceful,
reducing the MT tends to increase—not decrease—accuracy (figure 6.9),
because the force variability decreases in this range with decreases in MT
(figure 6.6). Therefore, reducing the MT when it is already quite short
results in improved spatial accuracy, giving more frequent ball contact.
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Ball Impact
Finally, of course, a faster swing gives more impact to the ball if it is hit—a
critical factor in the particular game of baseball. Increasing the load by
having a heavier bat can improve spatial accuracy (Schmidt & Sherwood,
1982) and would have only minimal negative effects on movement speed.
Clearly, both added bat mass and a faster MT contribute to greater impact
with the ball if and when it is hit.
Nearly every factor associated with decreased bat swing MT discussed
here would be expected to influence the chances of hitting the ball. Perhaps
understanding these factors makes it clearer why professional batters seem
to swing nearly maximally.
Accuracy in Coordinated Actions
The previous section of this chapter presented various factors related to the
speed and accuracy in making rapid, mainly single-limb, aiming movements.
Much of this discussion focused on actions similar to the kind you might see
in moving a finger or limb at a target (e.g., moving a computer mouse to
point a cursor at an icon, or moving your foot from the accelerator to the
brake pedal), or moving a single object with more than one limb (e.g.,
swinging a baseball bat to hit a ball, or swinging an ax to split a block of
wood). The principles discussed already, such as Fitts’ Law and the
relationship between force and force variability, appear to describe speed–
accuracy trade-offs well for these types of movements.
But, consider now what happens when we coordinate limbs not with the
purpose of moving a single object (e.g., a bat or an ax), but rather with
distinct goals for each limb. For example, a pianist often maintains a bass
rhythm with the left hand while performing a lead with the right hand; the
skill of knitting requires that two hands control the separate movements of
two needles, interspersed by grasping and moving the thread according to a
mental representation of the desired stitch; and a plumber welds a pipe
fitting by holding a flaming torch in one hand while spreading a bead of
solder over the joint with the other hand. Are these types of actions
explained by the same principles as previously discussed, or are unique
principles required to explain them?
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Bimanual Aiming Tasks
Simultaneously performing two tasks that each require spatial precision
combines two topics discussed in previous chapters. In chapter 4, we
discussed closed-loop processes—if MT is sufficiently long, then end-point
precision is facilitated when we guide the limb visually toward the target.
And, in chapter 3, we said that attention is limited to producing and
controlling only one motor program at a time. Putting these two discussions
together, then, how is the simultaneous, visually guided control of two
separate limbs achieved when moving to separate targets? As we will see
in the following sections, speed and accuracy becomes a much more
complex issue when two or more limbs have distinct spatial goals.
Bimanual Fitts Task
The bimanual Fitts task is a simple variation of the single-limb task. A
bimanual version of the continuous task (Fitts, 1954) was first used by
Robinson and Kavinsky (1976), and a bimanual version of the discrete task
(Fitts & Peterson, 1964) was introduced by Kelso, Southard, and Goodman
(1979). In the bimanual version, both limbs could be assigned identical
tasks, with either low (figure 6.11a) or high IDs (figure 6.11b). However,
the limbs could also be assigned to different (incongruent) tasks, say one
with a low ID and one with a high ID (figure 6.11c). According to Fitts’
Law, MT is a function of the task parameters, width (W) and amplitude (A)
—the MT for any particular task should be simply a function of its ID.
Therefore, a strict prediction of Fitts’ Law would be that each limb would
arrive at its target in a MT that was consistent with that task’s ID. For
congruent tasks, MTs should be similar; for incongruent tasks, MT should be
faster for the limb moving to the smaller ID.
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Figure 6.11 Three variants of the bimanual Fitts task: (a) Both limbs move
to a low-ID task; (b) both limbs move to a high-ID task; and (c) incongruent
limb-ID assignment, where the right hand performs a low-ID task and the
left hand performs a high-ID task. Note that the blue-filled circles represent
the starting points for each limb; red rectangles are the targets.
The studies by Kelso and colleagues (1979) showed that these predictions
did not hold true. For example, when paired with a limb moving to a high
ID, the MT of the limb moving to a low-ID task was considerably slower
than would be expected. The set of findings from this research is rather
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complex, but in general, the conclusion was that the explanatory power of
Fitts’ Law is reduced when separate and incongruent task demands are
required of two limbs. This finding could be a result of an attempt by the
executive to deal with an overloaded attentional demand by issuing a single
motor program that controls both limbs.
Such a conclusion is supported by other bimanual research (Kelso, Putnam,
& Goodman, 1983) in which one limb was to clear a physical barrier
placed between the home position and the target. The barrier required the
limb to be elevated in order to clear it. However, the limb without the
barrier did not have to be lifted in this way. The latter limb, even though not
physically required to do so, was lifted so as to match the barrier limb.
Similar common kinematic evidence was found in tasks in which subjects
had to reach and grasp two objects at different locations (Jackson, Jackson,
& Kritikos, 1999). Together, these findings support a view in which the MT
and kinematics for both limbs are not determined independently but rather
by a joint command.
The Gamma–V Experiment
Try this simple experiment for yourself. With a pencil in your right hand,
practice drawing small (2 in. [5 cm]) figures that represent the Greek letter
gamma (γ). Draw the “γ” relatively quickly, without modification during its
production. Start with the pencil against the edge of a ruler laid on the
paper, and finish with the pencil against the ruler again. The figure must
cross over near the center and have a rounded bottom. When you can do this
effectively, use the other hand to draw regular “V”s. The procedure is the
same except that now the figure must not cross over itself and must have a
pointed bottom. Based on chapter 5, each figure is represented by its own
motor program because the temporal structures for the two figures are
different: down–up for the “V” and down–over–up for the “γ.” Most people
do not have any trouble producing these figures when each is drawn on its
own.
Now try to produce these two figures together, using the same hands as
before. You will find, as Bender (1987) did, that doing both tasks at the
same time is very difficult, with results such as those shown in figure 6.12.
Most performers show a strong tendency to make the same figure with both
hands or at least to produce certain features of the different figures with both
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hands (e.g., a rounded bottom). Clearly, the fact that the subjects could
produce these actions separately was evidence that there was a motor
program for each of them. Even after considerable practice, most people
cannot do this dual task effectively. This demonstration indicates that, with
separate programs for producing a “V” and a “γ,” these programs cannot be
run off at the same time without considerable interference between the
hands.
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Figure 6.12 The gamma–V task. Subjects are asked to produce the capital
letter “V” with the left hand and the Greek letter gamma (“γ”) with the right
hand. In unimanual trials only one letter is written at a time; in the bimanual
trials both letters are written simultaneously.
Reprinted by permission from Bender 1987.
These findings, together with the results from Robinson and Kavinsky
(1976) and Kelso and coauthors (1979, 1983) presented in the previous
sections, can be interpreted to suggest that the motor system can produce
only a single motor program at one time. This is an extension of the idea
expressed earlier that the movement programming stage could organize
(during reaction time [RT]) only a single action at a time. But now the focus
is on the production of the movement itself, after the RT has been
completed.
Complex Coordination Patterns
A more complex version of the gamma–V experiment by Heuer, Schmidt,
and Ghodsian (1995) revealed more evidence for this single motor program
view. In this experiment, subjects were given extensive practice in
performing a single-reversal arm movement of a lever with the left arm
(flexion, then extension), together with a double-reversal lever movement of
the right arm (flexion, extension, flexion). Analysis of the movement
kinematics revealed a tight coupling (i.e., high within-subject [over the
trials] correlations) of the temporal occurrence of specific landmarks of
each limb pattern, suggesting again that such a complex coordination was
being governed by a single motor program. Also, subjects found it almost
impossible to move one limb faster than the other if asked to do so.
Moreover, the subjects were asked to do a probe-RT task, involving a foot
response to a tone stimulus. Although these data are not included in the
paper, the strong impression was that people could do the two-hand
coordination task more or less automatically without interfering with the
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probe-RT task. This finding, plus the evidence of a strong coupling between
the arms, provided more support for the idea that a common GMP can be
applied to the control of both arms to control a coordinated, simultaneous
action.
Focus on Application 6.2
Coordination in Golf Putting
Almost every golf instructor contends that body sway during the golf
putt is detrimental to accuracy—the golfer should keep the lower
body, torso, and, most importantly, the head as still as possible and
simply rotate the shoulders to move the putter and strike the ball.
But, for a number of reasons, this is very difficult to do. For
example, a putting study by Lee and coauthors (2008) showed that
both novice and expert golfers moved their heads considerably
during a putt. However, they did so in fundamentally different ways.
Figure 6.13 illustrates 60 putts taken by one of the novices (top) in
the study and one of the experts (bottom). The blue lines show the
movement velocities of the head during a putt, and the red lines
trace the velocity profiles of the putter during the same time period.
Note that although both the novices and experts moved their heads
during each and every putt, they did so in fundamentally different
ways—while the novice moved the head in the same direction as the
movement of the putter, the expert moved the head in the opposite
direction of the putter.
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Figure 6.13 Velocity traces of 60 putts made by a novice golfer (a)
and an expert golfer (b). Each line represents the kinematic timeline
of one putt. It seems clear that both novices and experts moved their
heads during the putt—but in opposite directions!
Reprinted by permission from Lee et al. 2008.
Regardless of the direction of head movement, have another look at
both graphs. Do you notice some similarity between the two? The
point at which the putter velocity traces reversed their direction in
the graphs coincided generally with the reversal of the head
velocity. We take this evidence as suggesting that the timing of the
movements of both the putter and head are the result of the common
motor program. The novices dealt with the “head problem” by
moving it together with the motion of the putter; the experts dealt
with the head problem by moving it in opposition to the putter
motion.
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Analysis of the movements of novice and expert golfers during a putt
showed different coordination patterns between head movement and putter
movement.
Continuous Bimanual Tasks
Controlling the continuous movement of two limbs, each with its own
spatial or temporal goal (or both), represents a different problem for the
motor control system (e. g., rubbing your head while patting your tummy).
Because the movements are ongoing, the executive has the flexibility to use
a common movement command to control the movements of both limbs (as
discussed for discrete movements) or to switch attention rapidly between
the executions of the two tasks.
Continuous Bimanual Timing
Try this simple example of a continuous bimanual task: Point your index
fingers on both hands straight ahead of you, as if you were pointing two
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pistols at a target. Now start to wiggle both fingers. Most people
spontaneously do this task by wiggling each finger toward their midline,
then away, cycling back and forth using what researchers call an in-phase
mode of coordination. Here, “in-phase” means that right-finger flexion (and
any other feature, such as peak velocity, time of reversal-point arrival) and
the corresponding left-finger flexion occur at the same time and are
controlled by a common structure with relatively fixed timing. Considering
the limitless ways in which one could choose to wiggle both fingers at the
same time, why do most people choose this in-phase mode of coordination?
Two studies from Kelso’s lab provide evidence for an answer. In one study
(Kelso, Scholz, & Schöner, 1986), subjects were instructed to coordinate
their fingers by starting in either an in-phase position, as just defined, or in
an anti-phase position in which both fingers point to the right and then to the
left, like the movements of windshield wipers on many cars. The movements
started slowly and then were gradually sped up in tempo. Interestingly, the
in-phase mode of coordination was maintained regardless of the tempo, but
the anti-phase mode became highly variable. In another study, Kelso,
Scholz, and Schöner (1988) asked subjects to start moving with one
coordination pattern (either in-phase or anti-phase) and then to switch to the
other mode as quickly as possible. The switching took longer to achieve and
to stabilize when going from in-phase to anti-phase than it did from anti-
phase to in-phase. Together, these two sets of findings suggest that in-phase
was the more stable of the two coordination patterns—it was easier to
switch into than away from and was more resistant to the effects of speed.
So, what is the importance of having stable coordination patterns, and to
have one that is the most preferred? From one viewpoint, controlling the
timing of two fingers as a single coordination pattern should reduce the
attention demanded for control, compared to controlling them as two
independent events (Temprado, 2004; see also Focus on Research 6.2,
“Coordination as a Self-Organization Process”). In essence, rather than
having distinct lines of control (including separate executive commands and
feedback channels), the limbs are controlled as a single unit, which greatly
reduces the role of the executive in issuing commands and evaluating
feedback.
The advantage of having one pattern that is preferred (i.e., more stable)
over all others probably provides us with one way to deal with the speed–
accuracy trade-off (although this is described somewhat differently than in
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the discussion earlier in the chapter). Let’s go back to the results of Kelso
and colleagues (1986) once again. Recall that subjects performed rhythmic
movements of the two index fingers and gradually increased their speed.
Anti-phase accuracy and stability diminished with increasing speed but,
surprisingly, only to a point. Kelso and colleagues had instructed their
subjects to start with a specific pattern (either in-phase or anti-phase) but to
go with whatever pattern felt more comfortable if coordination stability
became threatened at higher speeds. Figure 6.14 illustrates their findings.
The accuracy (figure 6.14a) and stability (figure 6.14b) measures of in-
phase and anti-phase patterns are presented as a function of the cycling
frequency. As noted earlier, increased speed caused the anti-phase pattern to
lose accuracy and stability. But note that something curious happened,
starting at around a frequency of 2.25 Hz (Hertz = number of cycles per
second): the anti-phase pattern (defined as 180° relative phase, blue line)
switched to an in-phase pattern of coordination (figure 6.14a). Now look at
the blue line at the corresponding point (2.25 Hz) in figure 6.14b—at the
same time that the anti-phase pattern switched to in-phase, the stability of
the pattern was reduced dramatically.
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Figure 6.14 Mean relative phase and standard deviations for coordination
patterns starting as in-phase (red) or anti-phase (blue), as a function of
movement speed.
Reprinted by permissions from Kelso, Scholz, and Schöner 1986.
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The self-organization perspective views human movement as analogous to a
conductor-less orchestra.
Focus on Research 6.2
Coordination as a Self-Organization Process
The notion of the motor program is not without its critics. Some of
those with opposing views have offered an alternative that is
generally termed the dynamical systems, or self-organizational
perspective (Haken, Kelso, & Bunz, 1985; Kelso, 1995; Turvey,
1977) or simply, the dynamical perspective. These critics argue that
the program notion assumes too much cognition, neural computation,
and direct control by brain and spinal cord mechanisms, so that
every movement must have an explicit representation stored in the
central nervous system.
Investigators from the self-organization perspective hold that the
regularities of movement patterns are not represented in programs
but rather emerge naturally (that is, through physics) out of the
complex interactions among many connected elements, or degrees of
freedom. This is analogous to the ways in which many complex
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physical systems achieve organization and structure without having
any central program or set of commands, such as the sudden
transformation of still water to rolling patterns as it begins to boil
and the organization among molecules to form crystals. Just as it
would make little sense to postulate a central program for governing
the patterns in boiling water, these researchers argue that it is
incorrect to think that complex patterns of human motor activity are
controlled by such programs. Kelso and Engstrøm (2005) provide a
useful analogy here. Think of the motor program perspective as an
orchestra whose actions are under the supervision of a conductor,
and the self-organization perspective as a conductor-less orchestra.
A scientific debate about these issues has been continuing for
several decades now. At best, what has emerged is an agreement to
disagree. The two sides of the debate tend to study different tasks
(e.g., rapid, discrete movements vs. ongoing, cyclical motions), so
there is little basis on which compare theoretical predictions. In the
end, it is likely that neither theoretical perspective will be correct in
all aspects, which should lead to the development of new theories
with stronger predictive powers. And this is a good thing, as such is
the fate of a healthy science.
Exploring Further
1. Name two features of discrete tasks that make them more
suitable than continuous tasks for motor program study.
2. Name two features of continuous tasks that make them more
suitable than discrete tasks for self-organizational study.
The Speed–Accuracy Trade-Off Reconsidered
The findings of Kelso and colleagues (1986) are very important, for they
suggest that an alternative solution to the speed–accuracy trade-off is
achieved in different types of tasks. In the first part of the chapter we noted
that MT is slowed as task demands are increased in order to maintain
accuracy (Fitts’ Law). For very rapid (i.e., brief) movements, the linear
speed–accuracy trade-off suggested that error increases steadily as MT
decreases. The effect on timing was increased variability as MT became
longer. However, the findings of Kelso and coworkers (1986) suggest that
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when performance accuracy (or stability) is threatened by increased speed,
an alternative solution is that the motor system seeks out a different, more
stable coordination pattern to take its place. In other words, accuracy does
not continue to diminish with increasing speed; instead, the coordination
pattern changes so that stability can be reestablished.
These findings are not peculiar to moving two fingers—we change from a
walk to a run when the gait stability is pushed to the limit at high walking
speeds (Diedrich & Warren, 1995). And many four-legged animals have
three or more gaits from which to choose as the task demands change
(Alexander, 2003). Why might solutions to the speed–accuracy trade-off
change when movements become more complex?
Presumably, simultaneous movements of two limbs have more ways to be
organized than do movements of a single limb. Having more degrees of
freedom to organize, although burdensome from an organizational point of
view, also provides more flexibility in how to solve the problem. When
increases in speed result in decreased accuracy (more instability), the motor
system is faced with at least three alternatives: (a) reduce the speed and
maintain accuracy; (b) decrease inaccuracy and maintain speed; or (c)
maintain the speed and change the movement pattern in order to reestablish
stability.
Summary
The accuracy of rapid movements controlled by motor programs is
influenced by speed and amplitude variations, and these actions display a
typical speed–accuracy trade-off. Increases in speed (decreases in MT)
usually degrade spatial accuracy unless the movements are very rapid and
forceful. On the other hand, decreasing the MT usually enhances timing
accuracy. These effects are caused by relatively noisy low-level processes
in the spinal cord and the muscles that make the contractions differ slightly
from those originally intended. Movements involving more than one limb
are not controlled independently, but rather by a command structure that
coordinates both actions simultaneously. The increased complexity of
coordinating two movements also provides more flexibility, such that
increases in speed result in changes to the coordination pattern in order to
maintain stability.
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Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 6.1: Identify the correct equation for Fitts’ Law.
Activity 6.2: Select the type of movement to which each statement
applies to better understand the speed–accuracy trade-off and
exceptions to it.
Principles-to-Application Exercise
Activity 6.3: The principles-to-application exercise for this chapter
prompts you to identify a skill that involves rapid movement and
requires accuracy, then to explore how the speed–accuracy trade-off
phenomenon applies to the skill you have chosen and explain the
sources of error in rapid movements.
Check Your Understanding
1. Distinguish between temporal and spatial accuracy. Give an example
of an activity (e.g., a game of tennis) where both might be important.
Describe a situation where temporal accuracy is important and explain
why this is so, and then do the same for a situation where spatial
accuracy is important.
2. In words, explain what Fitts’ Law tells us about motor control and
speed–accuracy trade-offs.
3. Describe both a single-limb and a bimanual Fitts’ task. Explain, in
general terms, how findings using the bimanual Fitts’ task were
different than those using the single-limb task. What view (along with
other findings) do these differences support?
Apply Your Knowledge
1. Your friend has come up with a silly competition: At the driving range
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http://www.HumanKinetics.com/MotorLearningAndPerformance

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30136

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30137

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30138

you race to see who can go through a bucket of golf balls the fastest,
while keeping score for accuracy in hitting a middle distance on the
range. The winner is determined by a combined score of time and error
(distance from the target). Discuss two strategies that you might use to
win the competition. Would your strategies change if the winner were
determined by time and the combined distance of your shots? What if
the competition got even sillier and the accuracy in timing between the
shots mattered?
Suggestions for Further Reading
Woodworth’s legacy on speed and accuracy research is presented by Elliott,
Helson, and Chua (2001). Further details on sources of error in motor
control for aiming movements have been reviewed by Meyer and colleagues
(1988), who provide an elegant theory of these processes that applies to
many different kinds of limb movement situations. And Meyer and coauthors
(1990) have written an interesting and readable review of the history of
thought about speed–accuracy trade-off effects. Wing (2002) provides a
detailed account of timing variability from an information-processing
viewpoint, which presents an interesting contrast to a self-organization view
(see Kelso, 1995). See the reference list for these additional resources.
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Chapter 7
Individual Differences
How People Differ in Their
Performance Capabilities
Chapter Outline
The Study of Individual Differences
Abilities Versus Skills
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Is There a General Motor Ability?
Abilities and the Production of Skills
Prediction and Selection Based on Ability
Summary
Chapter Objectives
Chapter 7 describes research that considers why and how people
differ in motor skills and abilities. This chapter will help you to
understand
the scientific approach to the study of individual differences,
the nature of abilities and how they are distinguished from
skills,
two approaches to conceptualizing the “all-around athlete,”
and
the difficulty in predicting future successes in motor
performance.
Key Terms
ability
correlation coefficient (r)
differential method
experimental method
general motor ability
individual differences
prediction
reference tests
relative-age effect
skill
specificity hypothesis
superability
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To win the gold medal at the 2008 Summer Olympics, decathlete Bryan Clay
had to achieve elite-level performances in the 100 meter sprint, long jump,
shot put, high jump, 400 meter sprint, 110 meter hurdles, discus throw, pole
vault, javelin throw, and the 1500 meter run. Many ascribe the title of
“World’s Greatest Athlete” to the winner of this event. But what makes one
athlete skilled at so many disparate skills? Is there a single athletic ability
relevant to all ten of these events, or are there ten skills independent of each
other? This chapter will address how we can describe and understand the
wide variability in people’s motor performance capabilities.
This chapter changes tacks rather markedly, to deal with an area of
psychology and motor behavior that, at least on the surface, appears to be
very different from the motor skills topics we have discussed in the
previous chapters. Previously, the focus was on the effects of certain
variables on the motor behavior of people in general. This method is
termed the experimental method because the methods typically involve
conducting actual experiments (this is also the basis for the term
“experimental psychology”). This experimental tradition is by far the more
popular part of movement science. This experimental method typically
treats people identically and tacitly assumes that all people behave in the
same way when treated in the same way. In fact, differences between and
among people are thought to be one of the sources of error, or “noise,” in the
experimental tradition; and a great deal of effort is usually devoted to
eliminating or reducing these sources of variability in the experiment.
The Study of Individual Differences
A research area that diverges from the experimental method concerns the
differences between and among people on some measure, frequently on
measures of performance. This area, sometimes referred to as the
differential method, is concerned with the fact that not all of us are the
same, and, as we will see as the chapter unfolds, it focuses on the ways in
which we are different from one another. This shifted interest implies rather
different ways of thinking, different ways of doing research, even different
ways of collecting data and analyzing them statistically; as a result, research
in individual differences tends to look very different from experimental
research. This subfield of psychology and of motor behavior is sometimes
called individual differences; more formally, in psychology it is referred to
as differential psychology. Consider the conceptual model presented in
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figure 5.2 as illustrating all the ways in which individual differences could
occur. Essentially, every one of the processes we discussed in the earlier
parts of the book is a candidate for having individual differences in its
functioning.
It is interesting to note that the very aspects of the experimental method that
are viewed as “noise” or “error” by the experimental tradition are, in many
ways, precisely the topic of interest to the individual-differences researcher.
The study of individual differences actually involves two, rather distinct
emphases: the study of abilities and the study of prediction. These are
discussed next.
Studying Abilities
The first of these emphases is the study of the fundamental, largely-
genetically determined factors that cause us to be different from one another.
A typical question might be, Why is so-and-so such a standout surgeon? One
answer, on which we focus in the second part of the book, concerns practice
and experience. That is, it is usually the case that the standout surgeon has
devoted many hours to practicing her craft, and, of course, we all know that
practice makes a large contribution to skilled performance.
But is that all there is to it? Can we really account for the differences among
all of us by considering only practice? Most scientists, especially those who
study individual differences, would answer this question with a resounding
“No.” One answer to these kinds of questions suggests that the surgeon
inherited a certain fundamental capability that allows her to perform at a
high level. If so, what is this capability, what is its nature, how do we
discover what it is, and how do we measure it? Most scientists in the area
of motor behavior would refer to this capability as an “ability.” In this
regard we offer the following definition of ability: An ability is a
fundamental characteristic of different individuals that tends to underlie
particular skills; ability is largely inherited genetically and is not
modifiable by practice.
Studying Prediction
The second aspect of individual-differences research concerns what is
called prediction. Here is a typical real-world example. In the car
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insurance industry we are charged rates that are dependent, at least in part,
on the likelihood that we will have an automobile accident. For example,
young male drivers (16-25 years), statistically at least, have more accidents
than females; for this age range, this elevated accident rate (vs. that for
older drivers) appears to be larger with younger drivers of both genders.
Essentially, what the insurance company is doing is estimating the
probability that you will have an accident based on your age, where you
live, and your driving history in terms of citations and accidents, among
other things. The insurance company knows that there is a relationship
between certain fundamental features of the drivers (e.g., the driver’s age,
accident record) that are relatively strongly related to future accident
propensity. All these features of the driver (such as driving experience, age)
can be thought of as abilities. So, we could say that the insurance company
is predicting your likelihood of an accident based on some measure of your
abilities. Of course, the company cannot predict with 100% accuracy
whether or not you will have an accident next year; but if you are in a
younger age group, your chance of having an accident is somewhat larger
than that of someone in an older age group.
The following photo shows three-time gold medalist Misty May Treanor
returning a volleyball in the 2012 Olympic beach volleyball finals. Tallness
is an obvious ability related to beach volleyball; Misty and her volleyball
partner (Keri Walsh Jennings, who is 6 ft 3 in, or 1.9 m) are quite tall, and
the Misty–Keri team made good use of their height abilities in winning the
gold medal in 2012.
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Misty May Treanor (here, right) has an ability (height) that has facilitated
her development as a top-level beach volleyball player.
Within the field of motor behavior, prediction is all around us. The
gymnastics coach predicts who will not become a collegiate gymnast on the
basis of, say, body configuration. People who are over 6 ft tall (1.8 m) and
who weigh more than 210 lb (95 kg) are probably better suited for a
different sport. Universities typically use various test measures as estimates
of who among the applicants is most likely to succeed in their programs
(e.g., the SAT test), and they then admit the most promising students. In
another area, some dental schools use various “spatial abilities” tests as a
means to screen applicants for admittance into their programs.
Defining Individual Differences
Here we define individual differences as stable, enduring differences
among people in terms of some measurable characteristic (e.g., one’s age)
or perhaps performance of some task (e.g., one’s reaction time in a certain
situation). Two people can differ on a given performance in at least two
different ways. First, if the test involves a very stable measure such as body
weight, after a single measurement we might conclude that one person is
really heavier than the other. Although the scales might have some small
degree of variability, the repeatability of the measure is very good. This is
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an example of a measured characteristic that reveals a stable, enduring
difference between two people.
A second difference between people, however, can also occur when no
stable enduring difference is really present. For example, if one person rolls
a strike in bowling on one attempt and another person rolls a gutter ball, it
might not be wise to conclude immediately that the first person is a better
bowler than the second person based on this one measurement. Why not?
The answer is related to the fact that almost anything can happen on a
particular performance attempt by chance alone, and individual differences
must be based on stable, enduring differences. In the first case (weighing
people on scales), you are relatively confident of stable, enduring
differences in the measured trait, whereas in the second case you are not.
To summarize, individual differences in skills have these characteristics:
Differences tend to be stable from attempt to attempt.
Differences endure across time.
Differences on a single measurement are often not sufficient to
establish individual differences.
Abilities Versus Skills
It is useful to distinguish between the concepts of ability and skill. In
common language, these words are used more or less interchangeably, as in
“Buddy has good ability in [or skill at] ______.” However, scientists
generally define an ability as genetically determined and largely
unmodifiable by practice or experience. An ability, therefore, can be thought
of as a part of the basic “equipment” people inherit in order to perform
various real-world tasks. Skill, on the other hand, refers to one’s proficiency
at a particular task, such as shooting a basketball. Skills, of course, can be
modified by practice, are countless in number, and represent the person’s
potential to perform those particular activities. Thus, one could say “Eric
has good visual ability,” implying that he can generally see very well; but
Eric has developed the specific skill of identifying patterns of motion in
football through considerable practice, and this skill has Eric’s visual
ability as an underlying component. Differences between abilities and skills
are summarized in table 7.1.
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It is helpful to think of ability as a factor that sets limits on performance.
The authors will never become linemen in professional football, regardless
of how much time they devote to practice, because they do not have the
proper body-configuration ability for this skill. People who are color-blind
will never be effective in the skill of identifying and classifying
wildflowers, and someone with weak “courage ability” (should this actually
exist) should probably not be encouraged to join the circus as a tightrope
walker. Thus, limitations in the requisite ability for a particular task are
seen to limit the level of performance that a particular individual can
eventually attain.
On the other hand, if a novice does not perform very well on a particular
task, this might lead to the suspicion that he does not have the proper ability
for the task. However, much of this deficit can often be made up through
effective practice, as we discuss in the last section of this chapter. Notice
that even though measures of the skill would change with practice and
learning, the ability underlying this skill would not change with practice
(see the definition of ability presented earlier). It would be a mistake to
make a firm and final judgment about someone’s ability for a task when the
person has reached only the novice level of proficiency. Several factors can
change through practice to improve performance, as we will see later in the
chapter.
Is There a General Motor Ability?
What does the term “all-around athlete” really mean? Most of us have
known kids from school who starred on the football and basketball teams
and who also won medals in track and field. And then there were those
other kids—the all-around nonathletes. They seemed to have no proficiency
in motor skills whatsoever. How do we understand these apparent all-
around athletes and nonathletes? Two hypotheses, quite different in their
approach to answering this question, have been proposed and are discussed
next (see also Focus on Application 7.1).
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Focus on Application 7.1
The Babe (Mildred “Babe” Zaharias)
If any single athlete were to be given the label “all-around athlete,”
it would certainly be Mildred “Babe” Zaharias. She played
professional basketball, baseball, tennis, and bowling; she won two
gold medals and one silver medal in track and field at the 1932
Summer Olympics; and she dominated women’s golf at both the
amateur and professional levels for two decades. In fact, Babe
Zaharias was the first female to ever play in a Professional Golfers’
Association event, 65 years before Annika Sorenstam gained
notoriety for doing it at the Colonial tournament in 2003. The Babe
would surely have continued to impress people with her athleticism
had she not met her untimely death from cancer at the age of 45.
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Mildred “Babe” Zaharias—greatest all-around athlete, ever.
But, what was it that made Babe Zaharias so special? One view is
that she had one, very strong, all-around ability that was superior to
most others’ and that allowed her to perform many motor skills at a
superior level. Another view is that she had many separate abilities
that allowed her to develop certain specific skills at which she was
highly proficient.
General Motor Ability Hypothesis
An outdated view, made popular in the first half of the 20th century, held
that all motor performances are based on a single ability called general
motor ability. On this view, the all-around athlete is one who possesses a
strong general capability for skilled motor performance. Conversely, the
“all-around nonathlete” is the person who lacks a strong general motor
ability and thus succeeds in essentially no skilled physical activities.
A similar concept of a generalized capability to learn new skills was also
popular at the time; this concept was called motor educability by Brace
(1927). Analogous to the idea of the intelligence quotient (IQ)—the innate
capability to learn cognitive materials, generally—motor educability was
thought to represent some general ability to acquire new motor skills. Early
attempts to create tests that would measure motor educability were made by
Brace (1927: the Brace Test) and McCloy (1934: the General Motor
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Capacity Test). These tests tended to use whole-body actions that purported
to measure the general capability to learn athletic skills. Motor educability
is not considered a viable concept today, for reasons that will become clear
later.
Not surprisingly, the idea of a general motor ability shared many similarities
with ideas popular in the early 20th century about the structure of other
skills. This kind of thinking led to the idea of “general intelligence,” which
attempted to explain a person’s supposed potential for cognitive activities in
terms of an overall, unitary value—the IQ. Further, general cognitive ability
(IQ) and general motor ability were thought to be relatively separate, with
intelligence not contributing very much to movement skills and vice versa.
This general motor ability notion can be summarized as follows:
A single, inherited motor ability is assumed.
This ability presumably underlies all movement or motor tasks.
A person with strong general motor ability should be good at all motor
tasks.
Henry’s Specificity Hypothesis
In the 1950s and 1960s, Franklin Henry (1958/1968; personal
communication, University of California at Berkeley, 1965) examined a
very important statistical prediction about general motor ability. He
reasoned this way: Assume that a relatively large number of people are
tested on each of two skills, A and B. Henry reasoned that if one person was
an outstanding performer on skill A, then this person would be assumed to
have a strong general motor ability. If so, this person should also score well
on task B, which also depended on general motor ability. Conversely, if
another person did not score well on skill A, at least part of the reason
would be that this person had a weak general motor ability, and this person
would be expected to score relatively poorly on skill B also. In this way,
skill A and skill B are related to each other, in that “good” scores on A go
with “good” scores on B, and “poor” scores on A go with “poor” scores on
B.
With this kind of relationship, if we were to plot skill A against skill B, as
we have done in figure 7.1, where each dot represents a single subject
measured on both tests A and B, these two tests should plot linearly with
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each other, which they tend to do in figure 7.1a. However, if skill A and
skill B are not related to each other, then they should plot more or less as
seen in figure 7.1b. One interpretation of these plots is that in figure 7.1a,
skill A and skill B tend to be measures of the same thing, and the usual
interpretation is that they are both measures of the same ability. In figure
7.1b, on the other hand, we would be forced to say that skill A and skill B
are not measures of the same ability. This leads to the straightforward
prediction that if general motor ability exists, with all skills being
dependent on a single general motor ability, then all skills should show
strong relationships among them, as in figure 7.1a.
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Figure 7.1 Scatter plot of two tests revealing a high positive correlation (a)
and a very low positive correlation (b). Each dot in the figure represents the
performance of one individual, plotting performance on one test against
performance on the other test.
Statistically, a scatter plot such as that in Figure 7.1a implies that the two
skills are correlated with each other; that is, the statistical correlation
coefficient (r) computed between these two tests should be close to +1, and
far away from zero. The scatter plot illustrated in figure 7.1b implies a very
low positive correlation between the two tests and an r value just slightly
above zero. (See Focus on Research 7.1 for more on correlations.)
Focus on Research 7.1
Correlation: The Statistic of Individual Differences
An important concept for understanding abilities is correlation, a
statistic for measuring the strength of a relationship between two or
more tests. Assume we administered two tests, A and B, to a large
group of subjects (say, 100 people), such that each person has been
measured on both tests. The goals are to determine whether the two
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tests are related to each other and whether they share any underlying
features, such as abilities.
Figure 7.1 shows special graphs called scatter plots, with test A on
one axis and test B on the other. Each subject’s score is represented
as a single dot on each of the two tests. If the dots tend to lie along a
line, then we say that test A and test B are related to each other in
that scores on one test are associated with scores on the other. In the
case of figure 7.1a, this relationship is strong and positive: Those
individuals with high scores on test A tend to be the same
individuals with high scores on test B. The scatter plot in figure
7.1b shows only a small positive relationship: The scores on test A
are virtually unrelated to those on test B. The direction of the
relationship is given by the sign of the correlation coefficient.
The correlation can range in size from −1.0 to +1.0. The size of the
correlation indicates the strength of the relationship, or how close
the individual dots are to the best-fitting line passing through them.
If the line is sloped negatively – downward to the right – then the
sign of the correlation will be negative. If the dots are close to the
line, as they are in figure 7.1a, the correlation is close to +1.0,
indicating a very strong tendency for skill in A to be associated with
skill in B (r = +.90). If the dots lie relatively far from the line, as
they do in figure 7.1b, the correlation is closer to zero, indicating a
relatively weak tendency for scores in A to be associated with
scores in B (r = +.15). The strength of a relationship is estimated by
the squared correlation coefficient multiplied by 100. Thus, the
correlation of +.15 means that the two tests have .152 = (.15 × .15) ×
100, or about 2% in common with each other. Note that the size of
the correlation has nothing to do with its sign, because a strong
correlation can be either positive or negative. Finally, a correlation
of .00 indicates that the line of best fit is a horizontal line (a slope of
0), with the dots scattered about it in a random way. In such a case,
tests A and B would not be related at all.
Correlations are used in studying abilities. If two tests are related to
each other, then they have some underlying feature(s) in common. In
the study of skills, these common features are assumed to be the
abilities that underlie the two tests in question. If the correlation
between two tests is large in value (e.g., ±.80), we conclude that
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there is at least one ability that underlies both tests. On the other
hand, if the correlation is near zero, we conclude that there are no
abilities underlying both tests; in other words, the abilities
underlying one test are separate from those underlying the other.
Exploring Further
1. What would be the expected correlation value that would
support a general, underlying ability for running fast (low time
score) and jumping far (large distance score)?
2. What would be the expected correlation value for Henry’s
specificity view for the two skills in question 1?
Correlations Among Various Skills
Scientists have examined both field and laboratory data to determine
whether or not high correlations among motor skills could be found. There
are numerous data sets in the literature, but one by Drowatzky and Zuccato
(1967) makes the point particularly well. The authors examined six tests of
balance typically found in the physical education literature. A large group of
subjects was given all six tests, and the correlations between each pair of
tests were computed (15 pairs in all). These values are shown in the
correlation matrix in table 7.2, which contains the correlation of every test
with every other test. The highest correlation in the entire matrix was
between the tests named “bass stand” and “sideward stand” (r = .31). All of
the other correlations were numerically lower than this, ranging from .03 to
.26. Even the highest correlation of .31 means that there was only .312 × 100
= 9.6% in common between these two tests; over 90% of the abilities
underlying the two tests were different (i.e., 100% − 9.6% = 90.4% being
different). Based on these data, it is impossible to argue that there was some
single, underlying general motor ability that accounted for individual
differences in all of these tests.
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The argument for general motor ability is even weaker considering that the
tests in this study were all tests of different ways of balancing. There
seemed to be no general ability even to balance, with each test measuring
some separate ability to control posture. Lotter (1960) obtained similar
findings for correlations among tasks that purport to measure movement
speed; the highest correlation between any two tasks was .36. In a study of
50 tests in the Armed Services Testing Program (Fleishman & Parker,
1962), the correlations among tasks were generally less than .50 unless the
tests were practically identical to each other.
Henry based most of his thinking on numerous studies that examined
correlations among skills, all of which showed patterns similar to that seen
in table 7.2. In seeking additional evidence about specificity, though, Henry
(personal communication, University of California at Berkeley, 1964) once
studied four different populations representing different sport groupings:
basketball players, gymnasts, rifle shooters, and people who had never
performed on any athletic team, ever. He compared these groups on four
novel laboratory skills. If a general motor ability view were correct, then
the athletes, who assumedly possessed strong general abilities, would be
expected to outperform the nonathlete group on the laboratory skills. Henry
found that all of the groups performed essentially similarly, which tended to
support his specificity hypothesis and provided even more evidence
against the notion of general motor ability.
This large body of literature on correlations among skills is remarkably
consistent in supporting the following conclusions:
Correlations computed among different skills are generally very low.
Even skills that appear to be quite similar usually correlate poorly.
This overall lack of correlation among skills argues against the concept
of a general motor ability.
On the other hand, two skills with only minor differences (e.g.,
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throwing 10 m for accuracy and throwing 15 m for accuracy) can
correlate strongly.
The data tell us that there are many abilities and not simply a single
general motor ability.
Abilities and the Production of Skills
From the evidence just presented, scientists have been forced to conclude
that a single, general motor ability simply does not exist. Such an idea
cannot cope with the mass of correlational evidence like that seen in figure
7.2. Alternatively, scientists have argued that there are many abilities, each
with a relatively narrow group of tasks that it supports. This leaves many
questions unanswered, such as how many abilities exist, what abilities
might underlie some particular performance of interest, and how these
abilities are organized with respect to each other.
In baseball, for example, we might suspect that running speed is one of the
abilities that underlies, or supports, base running. There must be many such
abilities useful in various human performance tasks, such as visual acuity
and color vision, body configuration (height and build), numerical ability,
reaction speed, manual dexterity, and kinesthetic sensitivity. But only some
of these will be related to baseball. Of course, these various abilities are
spread throughout the motor system. The individual-differences research in
motor behavior is based on a wide variety of tasks, ranging from simple
laboratory skills to relatively complex skills associated with flying an
airplane. Although there is still much research to be done, at present we
understand a great deal about the structure of human motor abilities.
Types of Motor Ability
Individual-differences researcher Edwin Fleishman (1964) conducted
numerous investigations of abilities that underlie skills. Following is a brief
list (there are many more abilities that are not listed here). For many years
Fleishman employed a statistical technique called factor analysis, which
uses as a starting point the correlations among skills (information analogous
to that seen in table 7.2). This brief list gives the name of the ability
(generated by Fleishman), a brief description of what it is thought to
measure, and an example from real-world activities that might use this
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ability.
Reaction time. Important in tasks with a single stimulus and a response
to the stimulus, where speed of reaction is critical, as in simple
reaction time. An example is a start in a running or swimming race.
Response orientation. Involves quick choices among a number of
alternative movements, more or less as in choice reaction time. An
example is batting in baseball, where the nature of the pitch and thus
the bat positioning are uncertain.
Speed of movement. Underlies tasks in which the arm(s) must move
quickly but without a reaction-time stimulus, the goal being simply to
minimize movement time. An example is swinging a cricket bat.
Finger dexterity. Involves tasks in which small objects are
manipulated by the fingers and hands. An example is threading a
needle.
Manual dexterity. Underlies tasks in which relatively large objects are
manipulated with the hands and arms. An example is dribbling a
basketball.
Response integration. Involved in tasks in which many sources of
sensory information must be integrated to make an effective response.
An example is playing quarterback in American football.
Physical proficiency abilities. Fleishman (1964) also identified
several abilities that do not have so much to do with skills but rather
involve what he termed “physical proficiency.” Here, nine additional
abilities, such as dynamic strength, explosive strength, gross body
coordination, and stamina (cardiovascular endurance), have been
identified. There are probably many others: This group of tests can be
best thought of as related to what is usually called physical fitness.
These ideas about abilities have serious implications for some of the most
common beliefs held by coaches, sportscasters, and the public in general
about the structure of movement ability. Consider often heard statements like
“B.B. has good hands.” What does that mean? For many people it usually
means that, given the many different activities in which B.B. might
participate, the use of his hands is generally effective. But examine the
preceding list of abilities. Except for physical proficiency, each ability
involves the hands in some way, either to move small or large objects, to
move quickly to press a button, or to follow a moving target with a handheld
apparatus. Yet each ability is independent of the others. Therefore, there
must be no general “good hands” ability; rather, the abilities needed for a
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particular task depend on what the hands are asked to do.
Here’s another example. We often hear something like “Jessie Mae is
quick,” the speaker meaning that Jessie Mae generally reacts, responds, and
moves quickly whenever speedy actions are required. Yet the preceding list
includes at least three separate abilities to act quickly: (a) reaction time
(simple reaction time), where a single stimulus leads to a single response;
(b) response orientation (choice reaction time), where one of many stimuli
is presented, each of which requires its own speeded response; and (c)
speed of movement (movement time), which measures the time of a
movement produced without an initiating stimulus (i.e., not including
reaction time). Subjectively, each of these abilities involves what we would
call “quickness.” However, as in the situation with B.B’s “good hands,”
these abilities are separate and independent, indicating that there are at least
three ways to have the ability to be quick. Therefore, being quick depends
on the particular circumstances under which speedy responses are required.
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What types of motor ability would contribute to the success of an expert
surgeon?
General Motor Ability Reconsidered
Looking collectively at the research on the different types of motor ability,
there may be a very minor way in which a general motor ability hypothesis
does make sense after all. While the correlations among skills are generally
very low, they are not exactly zero, meaning that the tests correlate with
each other to a very minor extent (as illustrated in figure 7.2). Thus, there
may be a very weak general factor underlying most movement skills, giving
a slight advantage to those individuals with such a strong ability. This is
sometimes called a superability, to distinguish it from the earlier notion of
general motor ability. In any case, such a superability must not be very
strong, given that the correlations between skills are generally so low.
Perhaps abilities for skills are similar to the abilities for intellectual
activities: A weak general intellectual ability (IQ) is thought to underlie
almost all cognitive functioning, but several specific abilities are far more
important (e.g., numerical abilities and verbal abilities); something similar
seems to be the case for motor skills.
Be careful, though, because this argument in no way makes correct the
earlier notion that all movement capabilities are based on a single general
motor ability. There is simply too much evidence against this view, and it
has no place in our modern thinking about human motor abilities.
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This section on abilities can be summarized with a diagram of how the
important concepts of superability, abilities, and skills are related. Figure
7.2 shows just some of the abilities and skills discussed so far. At the left of
this structure is a superability, which contributes in a minor way to all the
separate motor skills. Next would come 20 to 50 motor abilities (only 7 are
shown), which provide the specific capabilities to perform these many
skills.
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Figure 7.2 Link between a superability, various motor abilities, and
selected movement skills. Every task is composed of a selection of abilities,
and any given ability can contribute to a number of separate tasks.
There are several important features to notice here. A given skill, say that of
the race car driver, is contributed to by a small number of the underlying
abilities. We might imagine that movement speed, manual dexterity, and
reaction time are represented in this skill, whereas other abilities (e.g.,
response orientation) might not be. This goes along with the view that
particular skills are based on combinations of several underlying abilities.
Also, different skills can use overlapping subsets of abilities. A successful
quarterback’s pattern of abilities is different from that of the race car driver;
yet a few of the same abilities are used in both skills (e.g., possibly reaction
time), whereas other abilities are not shared between the two (e.g., possibly
finger dexterity). This is necessarily so because there are countless
individual skills and only a relatively few abilities that can support them.
To summarize the involvement of abilities in the production of skills, the
following conclusions can be stated:
Any given skill has contributions from several of the fundamental
motor abilities.
Some of the abilities underlying a skill play very dominant roles,
whereas others have relatively weak roles.
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Two different skills will have different patterns of underlying abilities.
Two different skills can have a few abilities in common.
Abilities as a Basis for Skill Classification
In chapter 1, we classified skills as (a) open versus closed skills, (b)
serial–continuous–discrete skills, and so on. The study of individual
differences also leads us to identify the types of ability that underlie skills,
which allows for additional skill classification. These classifications are
important for practical application because they allow instructors to orient
instruction and practice methods to particular task requirements, thereby
facilitating performance and speeding up learning. Classifications help with
instruction in several ways:
1. The principles of performance and learning are somewhat different for
different classes of activities. Therefore, in order to apply those
principles properly to the appropriate classification of action—not
mixing principles intended for task type A when attempting to teach
task type B, for which the principle might not apply, seems essential.
2. Second, knowing that a task has a strong cognitive component or has a
particular emphasis on kinesthetic feel could influence the ways you
instruct the learner during practice. You can orient instruction and
practice methods to particular task requirements, thereby facilitating
performance and speeding learning.
3. Finally, task analysis tells you the category in which the task you are
teaching might lie so that you can adjust your advanced teaching
methods accordingly.
Therefore, effective classification allows the instructor to ensure that the
learning principles she is using are appropriate for the skill being taught, to
give the learner more assistance with underlying features of the skill
important for movement control, and to choose an individual for advanced
training based on the match of abilities possessed by the person and
involved in the task.
Classifications in terms of abilities can be made either casually or formally,
with differing precision as a result. On a very casual level, you can simply
produce an “educated guess” about the underlying abilities in a skill by
asking yourself which actions seem to require which of the abilities.
Alternatively, many have used the method whereby experts or coaches are
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asked about the fundamental structure of the task. Because expert
performers, teachers, and coaches can be very sophisticated about skills,
this kind of analysis has the potential for uncovering much useful
information (e.g., Fleishman & Stephenson, 1970).
The disadvantage of this method is that, often, very highly proficient
performers do not know how they do what they do. As you have learned,
many processes in skills are nonconscious, such as the execution of motor
programs and the detection of optical-flow patterns; thus, performers do not
have good conscious access to them and can’t tell you how they use them. A
pertinent example comes from Polanyi (1958), who found that champion
cyclists could not explain the principles of balancing on the bicycle, which
of course was absolutely fundamental to their task. Also, the famous tennis
player Bjorn Borg claimed that immediately before striking the ball in a
forehand stroke, he would “roll” the racket “over the ball,” which produced
topspin on the ball so that it would drop more quickly after it crossed the
net. Braden (personal communication, 1975) had an opportunity to observe
Borg at Vic Braden Tennis College, where he made high-speed video
recordings of Borg’s forehand stroke. Braden learned that Borg did not
rotate his racket immediately before ball contact. It was true that Borg
struck the ball with the racket in a “rotated” orientation, presumably to aid
topspin, but this rotation occurred quite early in his stroke—not immediately
before striking the ball as Borg claimed. It is easy to imagine how a well-
meaning tennis instructor who had assumed that Borg’s characterization was
correct might ask students to attempt to “roll the racket over the ball,” just
as Borg had said. There are many examples like this in the sport world.
Much can be learned from champion performers, but you should be
prepared not to believe everything they tell you.
Prediction and Selection Based on Ability
As indicated early in this chapter, a large part of the work on individual
differences concerns prediction of performance or skill. We discussed the
insurance company’s attempt to predict the possibility that you will have a
vehicle accident on the basis of certain of your characteristics, or abilities.
In industry, a personnel director might want to predict which of several
applicants for a job will be most successful at the job, not right now but
after a year’s training and experience. In sport, Ed Fleishman (personal
communication, 1970) described the effort by the owner of the then–Kansas
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City Royals professional baseball team to develop a procedure whereby
abilities found to be critical for adult baseball could be measured in
relatively young (junior high school level) players. Knowing which of the
younger players had the “right” abilities for adult baseball, the team’s
coaches could devote extra attention and practice time to those players
possessing abilities related to adult-level baseball, of course with the idea
of drafting them later.
There are several features common to all these examples. First, someone
wants to know something about an individual’s future performance
capabilities at some “criterion skill”—the ultimate skill in which the person
is interested. It would be simple to estimate who at present is a good
performer, but it is another matter altogether to be able to predict who—
after growth, maturation, or additional training—will become most skillful
on the criterion test (see Fleishman & Hempel, 1955). Second, this
prediction process requires knowing which abilities are important for the
criterion task. The process could involve measurement of the abilities in
already skilled employees in industry, or measuring abilities of
accomplished athletes. Third, the process involves measurement (or at least
some estimation) of the abilities seen in the applicants’ present performance
that would predict which of the candidates has the pattern of abilities that
matches the criterion skill most completely.
Therefore, attempts at prediction involve these components:
Understanding the abilities that underlie the criterion task
Estimating the strength of these abilities in applicants as indications of
their future capabilities in the criterion task
Estimating (or predicting) the potential (i.e., future) skill level on the
criterion task based on present information about the applicants
If the individual’s potential for eventual skilled performance at some task
can be estimated, many advantages can be realized. Novices could be
directed toward those activities for which they could become most suited.
Of course, everyone could be trained for an extended time, and those people
who succeed at the end of this training period could then simply be selected
or hired. But training is generally expensive and time-consuming. Prediction
provides a method for reducing the total amount of training time that must be
used for a given task. Also, in this way, training can be more focused on the
selected individuals. For those individuals not selected for a particular
activity, training can be focused on other activities to which they are better
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suited. This is the rationale for the Olympic training and selection
procedures used by many countries recently.
Focus on Application 7.2
Moneyball
The recent book and the movie made from it, both titled Moneyball,
provide a realistic example of these ideas from professional
baseball. In earlier times, the variables (measured on high school or
college players) that were used to predict who could become
professional ballplayers used rather obvious predictor variables,
such as batting average and home runs hit. But one team, the
Oakland Athletics, decided that these variables were not as
important as some others, such as the number of times a player
reached first base (by whatever means) and the number of ground-
ball outs that a pitcher induced in a game. The book and movie set
forth some of the successes of these attempts at predicting success in
professional baseball. Changing methods appeared to have paid off
for the Oakland team.
Focus on Research 7.2
The Relative-Age Effect
Here is an interesting phenomenon. Assume that you are examining
the statistics on high-level hockey players in Canada (i.e., those
playing on Junior A or professional teams). These statistics, used
mostly for promotional purposes, include such things as each
player’s height, weight, position played, hometown, and birthday. If
you examine the birthdays of these players, you will find that hardly
anyone on the team was born in the late months of the year, and most
were born in January, February, and March. Why should it be the
case that most high-level hockey players were born early in the
year? Astrology?
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Beginning with the research of Barnsley, Thompson, and Legault
(1992), followed by many investigations since (reviewed by Cobley
et al., 2009), there is a very compelling and reasonable explanation.
(Malcolm Gladwell, author of several wonderful books, including
The Tipping Point [2000] and Outliers [2008], also had some
interesting ideas about the subject.)
Nearly everyone knows that in Canada, hockey is a very special and
traditional sport. Seemingly, most kids would like to see themselves
succeed at the highest level possible in hockey. As a result,
Canadian hockey is structured so that many age-group teams are
available for kids to join, starting at a very early age (as young as 5
years old). Typically, in the leagues in which they play, players are
assigned to teams on the basis of calendar-year age groupings. The
result is that a child playing on a 10-year-old team in the year 2014,
for example, would have been born sometime in the year 2004; if
the child’s birthday was early in 2005, then he would be directed to
the 9-year-old team. Very similar methods are used for many
different youth sports in many countries around the world.
This procedure creates a very interesting bias. For example, a child
born on January 1, 2005, would play on the 9-year-old team,
whereas a child born on December 31, 2004, would play on a team
of 10-year-olds, even though these two children are only one day
apart in age. We know, of course, that especially in young boys, a
year of age (especially at 10 years old) makes a big difference in
terms of maturation, body size, and so on; older boys (i.e., those
with a birth date earlier in the calendar year) tend to be bigger,
faster, and stronger, other things being equal. Naturally, coaches of
these age-group teams focus most of their attention on the most
effective players, setting the stage for a “rich-get-richer”
phenomenon. As a result they improve more than the kids born later
in the year, which carries over to the next age-group team: Now they
have an advantage because (a) they are still older than the kids born
late in the year, and (b) they had the extra coaching and attention
during the previous year because they were older—and so on, and
so on.
This phenomenon has been labeled the “relative-age effect”
because the players who are born early in a given year are
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“relatively older” than the players born late in that year, even
though, by traditional methods, they may be the “same age“. In a
way, this argument goes against the idea that champion players are
born with the “right” abilities; rather, this argument suggests that
those players who were “lucky enough” to be born early in the year
have an advantage over their late-in-the-year counterparts. This
theme—that high-level skill performers were simply “fortunate” in
various ways—occurs repeatedly in Gladwell’s (2008) Outliers.
Exploring Further
1. Suppose sport teams in school were based on school-year birth
dates rather than calendar-year birth dates. Which children
(born in which months) would be the beneficiaries and losers
in this “luck of the draw”?
2. Aside from hockey skills, name two motor skills that might be
similarly affected by this relative-age effect and two motor
skills that you might anticipate would be unaffected.
Patterns of Abilities Change With Practice
An important phenomenon to consider when attempting to predict skilled
performance is that the pattern of abilities underlying a particular task
changes with practice and experience. At one level this is obvious. For
beginners, considerable cognitive activity is involved in deciding what to
do; remembering what comes after what; and trying to figure out the
instructions, rules, task scoring, and the like. With some experience, as one
learns the intellectual parts of the task, these cognitive abilities are replaced
by more motor abilities related to limb movement.
This general idea was shown in a study by Fleishman and Hempel (1955).
They used what are called “reference tests,” relatively well-understood
tests from earlier studies (some of these are referred to earlier in the
chapter) that are used to measure abilities of various kinds (e.g., reaction
time, movement time, spatial relations). Fleishman and Hempel
administered these reference tests to a group of subjects to identify each
subject’s level of various abilities. The subjects then practiced a complex
visual discrimination reaction-time (RT) task. The task involved a series of
four colored lights (two red and two green) arranged in a square pattern.
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Four horizontal toggle switches, which could be pushed or pulled, were
used to respond to the lights, depending on a complex spatial relationship
among the stimuli. The researchers entered the scores on the reference tests,
along with the results of the discrimination RT test, into a factor analysis,
which allowed them to measure how much of the performance on the
discrimination RT test could be explained by each of the abilities measured
by the reference tests. More importantly, these factor analyses allowed the
researchers to determine how the relationship between task and the
reference tests changed as a function of practice on the discrimination
task.
First of all, note the large shaded area at the bottom of Figure 7.3 labeled
“discrimination reaction time, specific.” This line begins at about 20%
(meaning that it is a moderately large contributor to performance); then it
increases across practice to a value of approximately 40%—where it
becomes the largest single contributor to performance. This can be
interpreted as showing that the discrimination RT test becomes increasingly
specific with practice, meaning that discrimination RT relies less and less
on various reference tests as practice continues, or that discrimination RT
correlates less and less with other tests, or both. In essence, discrimination
RT becomes increasingly its “own task,” without as much reliance on other
abilities.
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Figure 7.3 Results from Fleishman and Hempel (1955), showing the
changes in variance accounted for by reference tests on a discrimination RT
task as a function of practice.
Reprinted by permission from Fleishman and Hempel 1955.
Second, notice that the contributions of the various reference tests also seem
to change as practice continues. For example, the second panel up from the
bottom of the graph in Figure 7.3 represents the contributions from the
reference test “spatial relations.” On the very first trial, “spatial relations”
is by far the most important ability in this task, accounting for more variance
(about 30%) than any of the other of the reference tests. But its contribution
decreases markedly across practice, to the point that it contributes only
about 5% to performance on the 15th trial. Notice also that the reference test
“rate of movement” increases its contribution to discrimination RT at the
same time. That is, the structure of this task seem to “change” with practice;
performance seems to depend on (or be related to) different abilities at the
start of practice as compared to the end of practice. That is, practice
produced changes in the relative contributions of the various abilities – not
changes in the abilities themselves.
Consider an activity such as surgery, and assume that it is known which
abilities underlie this skill when performers are essentially novices. As
seen in figure 7.4, when the person is a novice, this task is composed of
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hypothetical abilities A, C, T, and P (note the position of the darkened end
of each bar). With additional training at this task, this pattern of abilities
gradually changes, so the expert’s pattern of abilities involves abilities A,
C, Q, and R. Notice that two of the abilities, A and C, are present in both
novice and expert performers. Other abilities, T and P, drop out to be
replaced by abilities not represented earlier—Q and R. Perhaps abilities T
and P were cognitive abilities, which dropped out as practice continued
(e.g., see Fitts’ stages of learning in chapter 9). Still other abilities, X and Z,
are never represented in this skill, regardless of the skill level. Remember,
abilities are genetically defined and not modifiable by practice. It is the use
of, or the selection of, these abilities that changes with practice.
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Figure 7.4 Changes in the underlying abilities as the learner progresses
from novice to expert. Some abilities drop out and are replaced by others,
whereas other abilities remain.
The difficulty is that, although an individual might have the proper abilities
for novice performance (abilities A, C, T, and P in figure 7.4), this often is
not the proper pattern of abilities required for expert performance (abilities
A, C, Q, and R). Therefore, selecting people because they are good as
novices—or because they have strong abilities in A, C, T, and P, which is
the same thing—will capture only a part of the job of prediction. Most
knowledge about abilities is based on relatively novice-level performances;
and, unfortunately, little is known about the abilities that underlie very high-
level performances, making the task of predicting them particularly difficult.
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Running speed is one ability likely to influence success in base running.
What other abilities would be important, for either a novice or expert base-
runner?
Performances in Early Practice
This shift of abilities with practice and experience can be a problem if you
attempt to select performers on the basis of their performance in early
practice. A common procedure is to invite a large group of youngsters to try
out for a particular team or activity. After a relatively brief practice period
of a few hours, those performers most skilled at these activities are invited
to remain on the team, and the others are told that they will not be retained.
You can perhaps see the difficulty with this procedure. Referring to figure
7.4 again, assume that the people who have succeeded at this early stage of
practice are strong in abilities A, C, T, and P. These people, after extensive
practice, may not be very well suited for high-level proficiency because
they may not be strong in abilities Q and R.
The problem is even more serious than this. Consider an individual who has
the proper abilities for expert performance (say, A, C, Q, and R, to use our
current example). While this person does have the abilities to perform this
skill at the end of practice, he does not have the proper abilities for novice
performance; notice that if abilities T and P are not strong, there is the
likelihood that he will not even be selected to remain with the team after the
brief initial practice period. Because the abilities underlying a skill change
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with proficiency level, we stand a good chance of missing the “right”
people if we base selection on performance at the novice level. The
solution for this problem seems to be to allow as many performers as
possible the opportunity to participate as long as possible, so the applicants
can gradually move toward their own highest levels of proficiency. Then, by
evaluating high levels of skill and stable, expert patterns of abilities,
coaches and trainers can select more confidently.
How Effective Is Skill Prediction?
Prediction for future success sounds wonderful in principle, but there are
several difficulties in actual practice. For example, in various attempts to
predict success in activities such as military pilotry, subjects are measured
on a large number of “predictor tests,” which are presumably (as
determined by prior factor analysis research—reference tests) measures of
various underlying abilities. The relationship between this battery of
predictor tests and the criterion task of pilotry is computed using a
statistical technique called multiple regression. It is beyond our scope here
to provide much discussion of multiple regression; we will just say that
multiple regression procedures apply weights (indicating “importance in”)
to various predictor tests in such a way that the weighted sum of the
predictor variables correlates maximally with the criterion.
These correlations (called multiple correlations, abbreviated R) are not
usually very high in skills-prediction situations—perhaps .30 or .40; the
largest of these correlations that we have seen reported in the literature was
only .70 (Adams, 1953, 1956; Fleishman, 1956). Remember, with
correlations, this means that only .702 × 100 or 49% of the abilities
underlying the criterion pilotry task are being measured by the test battery;
the remaining abilities underlying pilotry are unknown. The situation is even
more dismal in athletics because this problem has received almost no
systematic study, whereas the prediction of pilotry has had much research
support. The result is that prediction in sport situations is not very effective.
Why is effective prediction so difficult to achieve, even with tasks that have
attracted strong research efforts? Several factors contribute to the problem.
Patterns of Abilities Are Not Generally Known
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One difficulty is that the pattern of abilities underlying successful
performance of various criterion tasks is generally not very well
understood. Coaches and instructors usually have some general ideas about
these abilities, of course, such as the abilities to be tall in basketball or
large in football. Beyond this, though, determining the abilities for various
sport activities is based mainly on guesswork. Related to this is the fact that
even if the abilities were known, no one is certain how to measure them.
Therefore, because the abilities underlying a given sport and performance
are generally poorly understood and difficult to measure, there is little basis
for effective prediction.
Many Abilities Underlie a Given Skill
Even if some of a particular criterion activity’s abilities were understood
and could be measured, there are probably many other abilities underlying
this task. For example, if 15 of the 50 or so abilities must be measured to
predict effectively, imagine the time and expense in measuring each ability
in each of a large group of applicants. Of course, some success at prediction
can be achieved using only one or two tests, but the advantage will be
relatively small because of the many relevant abilities that are not
considered.
Generally, the prediction of success in movement skills is not very effective
in motor behavior for the following reasons:
The underlying abilities in motor performances have not been studied
systematically and are not well understood.
The number of such underlying abilities is probably large, requiring
that many abilities be measured.
The pattern of relevant abilities shifts with practice and experience,
making prediction of expert performances difficult.
Summary
There are many interesting aspects of individual differences among people
and ways in which these variations can be understood. A critical concept is
that of an ability, which is defined as a mainly genetically-defined, stable,
enduring trait that underlies the performance of various tasks. An ability is
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distinguished from a skill, which is proficiency in some particular task.
Henry’s (and others’) research tells us that the old concept of a general
motor (or athletic) ability, with one ability thought to underlie all motor
proficiency, is simply incorrect. Generally, the relationships (measured by
correlations) between various skills are low, suggesting that there are many
abilities, which are very specific to particular tasks. There appear to be
many motor abilities—perhaps 50 or so, when they are all discovered—that
should be able to account for motor performances.
The capability to predict performers’ success in some future activity is a
critical individual-difference consideration, and the success of prediction is
based on the notion of abilities. However, even in the most thoroughly
studied areas of motor performance, prediction is not very effective,
probably because of the incomplete understanding of the fundamental
abilities that underlie performance. This is particularly so in sport, where
most areas have received little scientific study. Finally, the pattern of
abilities for a particular skill changes with practice, requiring caution in
attempts to predict a performer’s ultimate success on the basis of
performances in early practice.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 7.1: Explore the distinction between an ability and a skill by
indicating which in a list of descriptions applies to each.
Activity 7.2: Test your understanding of the correlations between
skills by interpreting two correlation graphs and how their
performance skills would most likely correlate.
Activity 7.3: Identify the factors for predicting an individual’s future
performance capability for a specific task.
Principles-to-Application Exercise
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Activity 7.4: The principles-to-application exercise for this chapter
prompts you to identify a sport or activity and analyze the abilities and
skills that would affect its performance, as well as consider the issues
that would arise when predicting who would be successful in the sport
or activity and what challenges might arise.
Check Your Understanding
1. How were statistical correlations used to examine abilities? What did
researchers find out about correlations among skills? What does this
tell you about the concept of a general motor ability?
2. Describe three components involved in attempts at prediction of a
future skill level on a criterion task. How effective is skill prediction
in a sport setting?
Apply Your Knowledge
1. Explain the differences between an ability and a skill. How would you
illustrate these differences to a friend who has told you that she would
like to train quickness in her young field hockey team? What might you
suggest to include in practice to improve on skills requiring speed?
2. What difficulties might a talent scout for a high-level swim team
encounter when predicting which young children at a swim camp are
likely to do well at an elite level? How might the abilities needed to
perform well as a novice differ from those needed after several years
of training?
Suggestions for Further Reading
Additional reading on early thinking about individual differences in motor
control can be found in Henry (1958/1968); other treatments have been
written by Fleishman (1957; Fleishman & Bartlett, 1969); and a short
discussion is included in Adams’ (1987) review. Ackerman has conducted
much of the most recent research and theorizing in the area of individual
differences (e.g., Ackerman, 2007). A general discussion of the history and
nature of motor abilities can be found in Schmidt and Lee (2011, chapter 9).
See the reference list for these additional resources.
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Part II
Principles of Skill Learning
Up to this point in the text, our focus has been on understanding some of the
factors that underlie motor behavior: the principles of movement control.
Most of the major variables that determine the quality of movement output
have been introduced and discussed. In addition, we have developed a
conceptual model of motor behavior. This model collects in one place most
of the important factors that determine motor output and indicates their
interactions, providing a relatively complete diagram representing how
skills are controlled. This conceptual model is consistent with the research
evidence; indeed, no process would have been included in the model unless
the empirical data suggested that it should be included. By this point, then,
you should have a reasonably good overall grasp of how skills are
performed and what some of the limiting factors might be.
Now is the time to put this model to work to help with understanding how
skills are acquired and improved with practice, as well as how the motor
system can adapt itself after stroke or injury so that motor behavior is
possible again. The concepts and terminology in part II should be familiar,
as they are mainly the same as those used in part I. A major focus in part II
is on the ways in which the components of the conceptual model can change
with practice and experience, as well as the research-based principles that
govern such changes. So, as in part I, a major emphasis is on research
indicating how certain variations in practice contribute to the future
capability for movement. As you will see, many of these variations of
practice are available to the coach or instructor to use directly with
learners; hence this discussion includes many ways in which practice can be
varied in real-world settings to maximize learning. Another important idea
concerns the notion of transfer of learning—the concept that practice on one
variation of the task can carry over, or “transfer to,” some different task.
Hence, a major concern is how practice on simulators may, or may not,
carry over well to some different task. Another related issue concerns the
extent to which skills are retained over time so they can be helpful to the
performer in the future. The practically-oriented reader should find this
section of the text useful.
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Chapter 8
Introduction to Motor Learning
Concepts and Methods in Research and
Application
Chapter Outline
Motor Learning Defined
How Is Motor Learning Measured?
Distinguishing Learning From Performance
Transfer of Learning
Summary
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Chapter Objectives
Chapter 8 introduces the concept of motor learning and describes
fundamental principles regarding how it is studied. This chapter
will help you to understand
a clear definition of motor learning and how it differs from
motor performance,
temporary and “relatively permanent” effects of practice
variables,
transfer designs and their importance in learning research, and
the measurement of transfer of motor skills.
Key Terms
capability
learning curves
motor learning
performance curve
retention test
transfer of learning
transfer test
Imagine that you are an instructor in a two-day cardiopulmonary
resuscitation (CPR) course, charged with teaching a set of skills to a group
of adults. For grading, you want to measure skill levels at the end of the
course, but are puzzled about how to do it. Would the best measure of skill
take into account the students’ levels of proficiency at the start of the class?
Would you measure the amount learned at the end of a course, when fatigue
might influence the measurements? Or would you measure skill at some time
later, after the course has finished, by which time some forgetting might have
occurred? What skills should you ask learners to perform as a test—the
same as practiced earlier or slight variations of them? And under what
conditions would the test be conducted—in the stress-free conditions in
which the skills were taught, or in the heightened levels of excitement that
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would no doubt put the skills to the test in a real emergency, or something in
between?
This chapter concerns motor skill learning, the remarkable set of processes
through which practice and experience can generate large, nearly permanent
gains in human performance. The initial focus is on understanding the
concept of learning, establishing some basic ideas about how learning is
defined and conceptualized. Then we turn to how, and with what standards,
one can measure and evaluate the effectiveness of practice, both in
laboratory and in practical settings with relevance to teaching. Finally is a
discussion of transfer of learning, by which the skills acquired in one
situation can be applied to another.
The capability to learn is critical to biological existence because it allows
organisms to adapt to the particular features of their environments and to
profit from experience. For humans, this learning is most critical of all.
Think how it would be to go through life equipped only with the capability
inherited at birth. Humans would be relatively simple beings indeed without
the capability to talk, write, or read, and certainly without the capability to
perform the complex movement skills seen in sport, music, or industry.
Although learning occurs for all kinds of human performances—cognitive,
verbal, interpersonal, and so on—the focus here is on the processes that
underlie learning the cognitive and motor capabilities that lead to skills as
defined earlier.
Learning seems to occur nearly continuously, almost as if everything you do
today generates knowledge or capabilities that affect how you do other
things tomorrow and beyond. However, this book takes a more restricted
view of learning, in which the focus is on situations involving practice, that
is, deliberate attempts to improve performance of a particular skill or
action. Practice, of course, often takes place in classes or lessons, either in
groups as might be seen in the CPR example provided earlier, or
individually, as in private ski lessons or physical therapy sessions. Usually,
but certainly not always, there is an instructor, therapist, or coach to guide
this practice, to evaluate the learner’s progress and give feedback about it,
and to decide about future activities to maximize progress. This focus on
practice with an instructor defines an important class of human activities
and requires investigation of the many factors—such as the nature of
instructions, evaluation, and scheduling—that collectively determine the
effectiveness of practice.
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Instructors in charge of practice are in an important position to influence
learning if they have a solid understanding of the fundamental processes
underlying practice settings. A critical starting point is understanding the
nature and definition of learning.
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For students learning CPR, how can learning be structured so that the skill
will transfer to real-world situations?
Motor Learning Defined
When a person practices, the obvious result is almost always an improved
performance level, which can be measured in a number of ways, such as a
lower golf score, reduced time to complete a simple surgical operation, or a
larger number of roofing shingles nailed in a 20 min period. But there is
more to learning than just improved performance. Psychologists have found
it useful to define learning in terms of the gain in the underlying capability
for skilled performance developed during practice, with the improved
capability leading to improved performance.
But, be aware that improved performance does not, by itself, define
learning. Rather, improved performance is an indication that learning may
have occurred, which represents an important distinction. This idea can be
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formalized by a definition:
Motor learning is a set of processes associated with practice or
experience leading to relatively permanent gains in the capability for
skilled performance.
There are several important aspects to this definition, which are discussed
in the next sections.
Learning Affects Capability
The term “capability” for performance may seem odd, but it simply reflects
the fact that any single performance may not reflect the skill level that
underlies the performance. Just as the fastest runner does not always win the
race, any performance may exceed or fall short of its theoretical true
capability. So, we are interested in measuring the underlying capability (or
capacity) for performance, being mindful that on any given occasion the
learner might not for various reasons perform up to her capability.
Learning Results From Practice or Experience
Everyone knows there are many factors that improve the capability for
skilled performance. However, learning is concerned with only some of
these factors—those related to practice or experience. For example, the
performance capabilities of children increase as they mature and grow.
However, these growth factors are not evidence of learning because they are
not related to practice. Similarly, gains in cardiovascular endurance or
strength could occur in training programs, leading to more effective
performance in activities like soccer; but these changes are not related to
practice as considered here.
Learning Is Not Directly Observable, But Its
Products Are
During practice there are many alterations to the central nervous system,
which some refer to as “brain plasticity,” where the term “plasticity” refers
to a brain that is changeable under various conditions. Some of these
alterations help establish relatively permanent changes in movement
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capability. These processes are generally not directly observable, though,
so their existence must usually be inferred from the changes in performance
they presumably support. It is useful to think of these changes as occurring to
the fundamental decision-making and movement-control processes,
discussed in the previous chapters, that are brought together in the
conceptual model of human performance. Figure 8.1 shows the conceptual
model again, this time highlighting some of the human performance
processes thought to be influenced by practice.
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Figure 8.1 Conceptual model with the processes that improve with practice
highlighted in dark blue.
Some examples of changes to these processes are (a) increased
automaticity, together with speed and accuracy, in analyzing the
environmental and movement feedback information (during stimulus
identification); (b) improvements in the ways actions are selected (during
response selection) and parameterized (in movement programming); (c)
building more effective generalized motor programs and effector processes;
(d) providing more accurate and precise feedback in several ways, and (e)
establishing more accurate references of correctness to aid in, for example,
balance. In fact, learning can occur at all levels of the central nervous
system, but the levels highlighted in figure 8.1 account for the biggest
changes. Of course, all of these processes have been discussed before; now
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is simply added the notion that they can be improved in various ways
through practice, leading to more effective performance.
Even though the underlying processes are not directly observable, we can
usually observe and measure the products of the learning process by
measuring changes in skill. Changes in underlying processes lead to more
effective capability for skill, which then allows more skillful performances.
Therefore, evidence about the development of these processes can be
gained by examining carefully-chosen performance tests. The performance
gains on these tests are usually assumed to result from gains in skill.
Learning Requires Relatively Permanent Changes
One important qualification must be added to the previous section. In order
for a change in skilled performance level to be regarded as due to learning,
the change must be relatively permanent. Many different factors affect the
momentary level of skilled performance, some of which are temporary and
transient. For example, skills can be affected by drugs, sleep loss, mood,
stress, motivation, and many other factors. Most of these variables alter
performance only for the moment, and their effects soon disappear. Consider
caffeine, for example; the performance gains from the caffeinated state to the
de-caffeinated state are not due to learning because the changes are transient
and reversible by adding caffeine again. There are many variations of
practice that can be shown to affect performance greatly, but often these
effects gradually wear off, allowing performance to return to its previous
level. These changes were clearly not relatively permanent.
In studying learning, it is important to understand those practice variables
that affect performance in a relatively permanent way. This changed
capability is then a relatively permanent part of the person’s makeup and is
available at some future time when the given skill is required.
An analogy might be useful. When water is heated to a boil, there are
changes in its behavior (analogous to performance). Of course, these are not
permanent because the water returns to the original state as soon as the
effects of the variable (heating) dissipate. These changes therefore would
not be analogous to learning changes because they are not relatively
permanent. However, when an egg is boiled, its state is also changed. This
change is relatively permanent because cooling the egg does not reverse its
state to the original. The relatively permanent changes in the egg are
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analogous to changes in the human due to learning. When people learn,
relatively permanent changes occur that survive the shift to other conditions
or the passage of time. After learning, you are not the same person you were
before, just as the egg is not the same egg.
The realization that performance alterations due to learning must be
relatively permanent has led to special methods for measuring learning and
evaluating the effects of practice variations. Essentially, these methods
allow scientists to separate relatively permanent changes (due to learning)
from temporary changes (due to transient factors). We return to this idea in a
subsequent section.
To emphasize the features of the definition of learning, the following
statements are important to keep in mind:
Learning results from practice or experience.
Learning is not directly observable.
Learning changes are inferred from certain performance changes.
Learning involves a set of processes in the central nervous system.
Not all changes in performance are due to learning.
Learning produces an acquired capability for skilled performance.
Learning changes are relatively permanent, not transitory.
How Is Motor Learning Measured?
For both the experimental effects of learning in the laboratory and the
practical effects of learning in applications of daily living, measuring
learning and evaluating progress are conducted in line with the same general
principles. Some of these are presented in this section.
Performance Curves
By far the most common and traditional way to evaluate learning progress
during practice is through performance curves. Assume that a large number
of people are practicing some task, and performance measures for each of
their attempts (called trials) have been collected. From these data, a graph
of the average performance for each trial can be drawn, as in figure 8.2.
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These data were generated from a rotary-pursuit tracking task, in which
subjects attempted to keep a handheld stylus in contact with a constantly
moving target. The measure of performance, time-on-target (the average
number of seconds in contact during a 10 s trial), shows improvement as
trials accumulate over five days of practice (Adams, 1952).
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Figure 8.2 Performance curve for a group of subjects practicing a rotary
pursuit tracking task. The score reflects the amount of time in contact with
the object to be tracked during a 10 s trial.
From Adams (1952), American Journal of Psychology. Copyright 1952 by the Board of Trustees of
University of Illinois. Used with the permission of the University of Illinois Press.
For practice with other tasks the curve slopes downward, such as those in
which time or errors are the performance measures. Figure 8.3 involves a
task in which the subjects attempted to match a complex goal pattern of arm
movement. Error in making the proper pattern (termed root-mean-square
error, or RMS error; see chapter 1) is the measure of performance, and it is
reduced quickly at first, then more slowly as practice continues. Similar to
what is seen in figure 8.2, there is a small regression in performance
between practice days, due to forgetting and other processes (such as warm-
up decrement; see chapter 9), but after a few trials the learners regained
their earlier performance levels and continued to improve.
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Figure 8.3 Performance curve for a group of subjects practicing an arm
patterning task. The score reflects the amount of error (expressed as root-
mean-square [RMS] error), which indicates how close the movements were
to the goal pattern.
Reprinted by permission from Winstein and Schmidt 1990.
Comparing figures 8.2 and 8.3, one can readily see that performance curves
slope upward or downward depending on whether the measured data
increase (distance run, number of successful completions, and so on) or
decrease (errors, time) with practice and experience. Another feature of
performance curves that is typified in both figures is that large changes
occur early in practice and then more gradually later on. In some cases the
improvements might be nearly completed after several dozen trials, whereas
in other cases the improvements could continue for years, although such
changes would be very small in later years.
This general form of performance curves—steep at first and more gradual
later—is one of the most common features of learning any task and reflects a
fundamental principle, called the “law of practice” (Snoddy, 1926). The
mathematical form of these curves and how they change with various
features of the task and the nature of the learners have been discussed in
some detail by numerous writers in the skill area (e.g., Newell, Liu, &
Mayer-Kress, 2001, 2009). Back in the 1960s, Franklin Henry (see chapter
1) was doing considerable logarithmic curve fitting, using data from both
motor learning tasks and fatigue tasks, and he attempted to understand
various changes in shapes of these performance curves (personal
communication, University of California at Berkeley, 1962).
The major points so far about performance curves can be summarized as
follows:
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Performance curves are plots of individual or average performance
against practice trials.
Such curves can either increase or decrease with practice, depending
on the particular way the task is scored.
The law of practice says that improvements are rapid at first and much
slower later—a nearly universal principle of practice.
Limitations of Performance Curves
There are many useful ways to use performance curves, such as to display a
given learner’s performance gains or to chart the progress of a group of
individuals. At the same time, several potential difficulties require caution
in drawing interpretations from these curves.
Performance Curves Are Not Learning Curves.
As useful as performance curves are for illustrating learners’ progress,
several characteristics limit their usefulness. First, these are not “learning
curves,” as if they somehow charted the progress of learning. These curves
are simply plots of (usually average) performance over practice trials,
which (as seen in the next sections) do not necessarily indicate much about
progress in the relatively permanent capability for performance, as learning
was defined earlier (see also Focus on Research 8.1).
Focus on Research 8.1
Learning Curves: Facts or Artifacts?
In an important early article, Bahrick and colleagues (1957)
identified a number of artifacts of so-called learning curves.
Subjects practiced a tracking task in which hand movements of a
lever were used to follow a variable cursor presented on a screen.
The researchers recorded the performances for analysis and later
scored them in three different ways. First they defined a narrow
band of correctness around the track (5% of the screen’s width) and
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counted the number of seconds out of each 90 s trial the subject was
on that target. Such scores, called time-on-target (TOT) scores,
measure the subject’s accuracy. Next Bahrick and coauthors
estimated TOT using a band of correctness that was somewhat
larger (15% of the screen’s width), and then they did it again for a
very large target band of correctness (30% of the screen’s width).
Then they plotted these various TOT scores for each trial, giving the
three curves shown in figure 8.4.
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Figure 8.4 Proportion of time-on-target for a group of subjects
practicing a tracking task, scored with three different criteria.
Performance was considered to be “on-target” whenever the
subject’s response was near the cursor within 5%, 15%, or 30% of
the screen’s width.
Adapted by permission from Bahrick, Fitts, and Briggs 1957.
Remember that these curves came from the same performances from
the same subjects, who were not aware of the scoring that Bahrick
and colleagues did afterward. If you were to think of these as
learning curves, you would be forced into three contradictory
conclusions: (a) The learning gains were rapid at first and slower
later (30% curve); (b) the learning gains were linear across practice
(15% curve); and (c) the learning gains were slow at first and more
rapid later (5% curve). In fact, only one rate of learning was
experienced by each subject, but it was estimated in three different
ways, which led to three different conclusions about the changes
with practice. These differences are caused by so-called scoring
“artifacts”. These artifacts occur whenever the measured scores
become less sensitive to the gains in the internal capability for
responding as they move closer to the best possible score on a trial.
When the performance maximum is reached this is called a ceiling
effect because a higher performance score is not possible. In this
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study, 100% TOT represents the ceiling. Performance minima can
also represent a scoring artifact. If tracking error had been measured
in this study (say, using root mean square error, or RMSE), then zero
error would be the minimum score possible, and would be called a
floor effect, because a lower performance score than this is not
possible. This experiment warns of the difficulties in using
performance curves and the potential errors that can be made in
making conclusions from these curves. This might give some clue as
to the origin of the title for their article Learning curves: Facts or
artifacts?
Exploring Further
1. Think of another motor learning task; describe how changes in
the criterion for success could be made and how these changes
might affect the shape of the performance curve over practice
trials.
2. Provide an example of a floor effect and a ceiling effect in the
task described for question 1.
Between-Subject Effects Are Masked
One of the main reasons for using performance curves is that they average or
“smooth out” the discrepant performances of different learners. Through
averaging of a large group of people together, performance changes in the
(mythical) average subject can be seen and, it is hoped, inferences can be
made about changes in general proficiency. This is particularly useful in
research settings, where the difference between two groups of subjects is
studied as a function of different practice methods, for example.
The drawback is that this averaging process hides any differences between
people, termed individual differences in chapter 7. Because of this, the
averaging method gives the impression that all subjects improve at the same
rate, or in the same way, which we know is not correct in most cases.
Within-Subject Variability Is Masked
A third drawback to performance curves is that the performance fluctuations
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within a single person tend to be obscured by averaging procedures. When
examining “smooth” performance curves, such as those in figures 8.2 and
8.3, it is tempting to assume that the individual learners’ performances
contributing to the curves progressed smoothly and gradually as well.
However, take another look at these figures, especially figure 8.3. Notice
that the label on the horizontal axis specifies that each data point represents
a block of nine trials. What this means is that nine separate trials for any
single subject are averaged to produce a single score, which is then
averaged over the group of subjects in an experimental condition. Thus, the
averaging process produces a curve that masks both within- and between-
subject variability.
Distinguishing Learning From
Performance
Critically important, not only for the experimental study of learning but also
for evaluating learning in practical settings, is the distinction between
learning and performance. According to this view, practice can have two
different kinds of influences on performance—one that is relatively
permanent and due to learning, and another that is only temporary and
transient.
Temporary and Relatively Permanent Effects of
Practice
One product of practice is learning—the establishment of a relatively
permanent improvement in the capability to perform. This effect produces a
relatively permanent change in the person (which really could be the result
of changes in a collection of processes, as seen in figure 8.1) that allows the
individual to perform a particular action in the future and that endures over
many days, or even many years. Essentially, the concern of researchers who
study motor learning is the discovery of practice conditions that maximize
the development of these relatively permanent changes, so that these
conditions can be used in various practical settings to enhance learning.
It is important to remember, however, that many practice conditions have
temporary effects as well as relatively permanent ones. Some effects are
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positive and contribute to increased performance levels (e.g., motivation),
whereas others are negative and degrade performance somewhat (e.g.,
fatigue). A key concern is identifying what these effects are and
distinguishing their impact on performance versus learning.
For example, various kinds of instructions or encouragement during practice
elevate performance due to a motivating or “energizing” effect. As seen in
chapter 11, giving the learner information about how he is progressing
during the practice of a task can have an elevating effect on performance.
Providing guidance in the form of physical help or verbal directions during
practice can also benefit performance. Various mood states can likewise
elevate performance temporarily, as can certain drugs. Other temporary
practice factors can be negative, degrading performance temporarily. For
example, sometimes practice generates physical or mental fatigue, which
can depress performance relative to rested conditions. Lethargic
performances can result if practice is boring or if learners become
discouraged at their lack of progress; this effect is more or less opposite to
the “energizing” effects just mentioned. Numerous other factors associated
with practice could exert similar effects.
Practice can have numerous important effects on the learner:
Relatively permanent effects that persist across many days, even years
Temporary effects that vanish with time or a change in conditions
Simultaneous temporary and relatively permanent effects that can
influence performance markedly
Separating Temporary and Relatively Permanent
Effects
Suppose that you are interested in trying out a new teaching aid for
improving the alignment skills of a golfer when setting up to make a putt.
(The device is not legal for actual competition and can be used only during
practice sessions.) Certainly your evaluation of this new teaching aid’s
benefits for learning will be based on whether or not it enhances
performance in a relatively permanent way—that is, after the device has
been removed (perhaps as in a golf game). After all, if the positive effects
of the teaching aid disappear as soon as it is removed, the aid cannot have
had much advantage as a teaching method.
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Whenever learners practice, and especially when instructors intervene to
enhance learning (e.g., by giving instructions and feedback), it is important
to have a way to separate the relatively permanent practice effects from the
temporary effects. Frequently in research settings, and sometimes in
practical settings as well, learners are divided into two separate classes or
groups. For example, let’s suppose that one group of golfers practices trying
to make 6 ft (1.8 m) putts with the alignment aid, and another group
practices without the aid. The two groups might practice under these two
different conditions for a period of time, perhaps over 10 sessions, and
record the percentage of putts made. You might average all of the golfers’
scores for each group separately and plot performance curves, essentially as
was done in figures 8.2 and 8.3. Such a plot might look like the one in figure
8.5, where the average percentage of putts made from 6 ft away is plotted
for the 10 sessions.
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Figure 8.5 Hypothetical performance curves for two groups practicing the
golf putt with or without an alignment aid.
Which condition is more effective for learning—practice with the alignment
aid or without it? This might seem like a silly question. Looking at the graph
in figure 8.5 reveals that the golfers using the alignment aid improved their
performance in practice more rapidly than the golfers without the aid, and
their final performance level (in the last session) was also higher. It seems
obvious that practice performance with the alignment aid was more accurate
than without it, and the difference might be due to learning, which would be
most interesting. As argued in the previous section, however, the difference
between these two groups might be only a temporary performance effect,
which could disappear as soon as the alignment aid is removed.
The problem can be posed more systematically in the form of hypotheses
about the two conditions:
Hypothesis 1: The group that practiced with the alignment aid learned
more than the group that practiced without it (a stronger relatively
permanent capability for performance had been developed).
Hypothesis 2: Although the group that practiced with the alignment aid
performed more accurately during the practice sessions than the group
that practiced without it, they were no better in the relatively
permanent capability for performance.
Which of these two hypotheses is correct? The answer, based only on the
data in figure 8.5, is unknown. The information presented gives no way to
tell whether the advantage of the new-device group is due to some relatively
permanent (learning) effect or to some temporary (performance) effect that
is likely to disappear once the alignment aid is no longer available. This is
a critical problem because there is no real basis for deciding which learning
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method is better. Fortunately, additional procedures are available that
permit separation of learning and performance effects.
Focus on Application 8.1
Self-Assessments of Learning
In most activities of daily life, the learner is responsible for making
the decisions about how to practice, such as session frequency and
duration. Practice for a motor skills competition is no different than
other types of studying—you determine how to practice and the
practice duration, and stop practicing when you feel competent or
confident in your predicted capability to perform when it counts
(e.g., on an examination). The critical question is this: What is the
basis for making this prediction?
The problem is that most learners interpret temporary indicators of
performance as permanent indicators of learning or remembering.
Motor skills practice can also result in a similar feeling of
overconfidence. One of the factors that is addressed in chapter 10
concerns blocked versus random practice scheduling. Drill-type or
block-ordered practice generally produces better skilled
performance than randomly-ordered practice trials. And, if asked to
predict what their performance would be in a delayed retention
test, subjects engaged in blocked practice predict that they will
have achieved far more learning compared to subjects engaged in
random practice. The reality is much different (actually reversed),
however (see chapter 10 for details); and it illustrates that self-
assessment judgments of learning can be quite unreliable, especially
when they are based on current indicators of performance during
practice. A good review of the research and applied nature of these
memory and learning issues is provided by Bjork (2011).
Transfer Designs
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A so-called transfer design can analyze whether a change that improves
performance in practice also improves learning by separating the relatively
permanent and temporary effects of a variable. This method has two
important features. First, the temporary effects of the variable must be
allowed to dissipate. In our golf example, the temporary effects of the
alignment aid (if any) might be informational or physical (a type of
guidance), operating mainly during actual performance, so very little time
for dissipation would be needed. As a result, any other temporary effects
(such as increased motivation) would dissipate relatively quickly, and
certainly would dissipate before the next session. (Other variables could
have temporary effects with much longer times for dissipation, such as a
week or a month.) Second, the learners in both groups are tested again under
common conditions in a transfer (or retention) test, either both with or both
without the alignment aid. This is done to equalize any temporary effects
that the test conditions themselves might have on retention performance.
Otherwise, the results would be difficult to interpret.
In general, the term transfer test usually refers to a change of task
conditions, whereas a retention test usually refers to a test given after an
empty period without practice (in reality, though, the terms are often used
interchangeably). The tests could be given a day or more after the last
practice session or several minutes after the last session. Because the
interest in the golf alignment aid example is mainly in the effect of the
alignment aid on performance when the aid is no longer available (which is
analogous to a competition), the decision is to test both groups without the
aid. The logic that underlies a transfer or retention test is this: If the
temporary effects have dissipated by the time of the test and any temporary
effects are not allowed to reappear (or they do reappear but at the same rate
in both groups), then any differences observed in the delayed test should be
due to the relatively permanent effects acquired through training with the aid
during the practice sessions. In this way, the learning effects of the alignment
aid are not evaluated during practice, but rather in the transfer or retention
test, when the temporary effects have disappeared, leaving the relatively
permanent effects behind to be revealed on the test.
The essential features of a transfer design can be summarized as follows:
Allow sufficient time (rest) for the supposed temporary effects of
practice to dissipate. The amount of time will vary depending on the
particular nature of the temporary effects.
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Evaluate learners again in a transfer or retention test, with all groups
performing under identical conditions.
Any differences observed in this transfer test are due to a difference in
the relatively permanent capability for performance acquired during
earlier practice, that is, in learning.
Consider the possible transfer test outcomes of the hypothetical experiment
just described. A few of these possibilities are shown in figure 8.6, labeled
A, B, C, and D in the right (transfer) portion of the figure. In transfer
outcome A, the performances of the two groups are different by
approximately the same amount as was present at the end of the practice
sessions (compare performance on session 10 with transfer outcome A). In
this case, the appropriate conclusion would be that all of the difference
between groups achieved by the last session of practice was due to a
relatively permanent effect because allowing the temporary effects (if any)
to dissipate did not change the groups’ relative status at all. Conclusion:
The alignment aid was more effective for learning than was practice without
the aid.
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Figure 8.6 Hypothetical effects on transfer tests of two groups practicing the
golf putt with or without an alignment aid. Four different possible outcomes
on the transfer test are illustrated in transfer outcomes A, B, C, and D.
Now consider transfer outcome B in figure 8.6, where the transfer
performance of the group practicing with the alignment aid is more accurate
than that of the other group, but the difference is not as large as it was in the
last practice session. From these test results one could argue that some of
the difference between the practice session performances of these groups
was due to temporary effects because dissipation reduced the difference
somewhat. However, not all of the practice session difference was
temporary because some of it remained in the transfer test, after the
temporary effects dissipated. Conclusion: The alignment aid elevated
performance temporarily, but it also produced some lasting effects on
learning, compared to practice without the aid.
Next, examine transfer outcome C in figure 8.6, where the transfer
performances of the two groups are essentially the same but at the level of
the no-aid group at the end of practice. Here, when the temporary effects
have dissipated, all of the differences that had accumulated between the
groups during practice dissipated as well. This leads to the conclusion that
all of the effect of the practice aid was due to some temporary elevating
change, and none of it was due to learning. Conclusion: The alignment aid
elevated performance compared to performance without the aid, but it had
no lasting effect on learning.
Finally, examine outcome D, where the performance of the group that had
practiced with the alignment aid resulted in transfer performance that was
less accurate than that of the group that had practiced without the aid. This is
a rather odd and counterintuitive result, for it reveals that not only did all
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performance advantages of the alignment aid disappear when the temporary
effects dissipated, but that the alignment aid resulted in a learning effect that
was smaller than practice without it. Conclusion: We will see some results
that look like this in future chapters. The alignment aid elevated
performance during practice, but had a degrading effect on learning
compared to the group that practiced without the aid.
Measuring Learning in Practical Settings
The issues just discussed may seem, at first glance, to relate mainly to
learning evaluation in research situations. However, transfer designs form
the basis for learning evaluation in many teaching situations as well. For
example, when a learner practices some skill, for example, producing the
proper amount of pressure in cardiopulmonary resuscitation (CPR), the
proficiency level reached at the end of a practice session may not reflect the
actual performance capability achieved. Because practice and various
factors involved in it may also affect performance temporarily, they may
mask the underlying acquired capability for responding.
Practice conditions may also not reflect accurately the emotional conditions
under which some skills are required. For example, CPR skills are usually
needed in emergency situations, where the emotional stress may be elevated
quite dramatically compared to the situations under which practice is
typically done. Therefore, various types of retention and transfer tests may
be required to assess the true level of skills learned during practice.
A related issue concerns evaluation for the purpose of grading. If a learner’s
grade in some activity is related to the amount learned, then basing the
grade on performance toward the end of some practice session would be
unwise. The learning level would tend to be masked by various temporary
practice effects. A far better method would be to evaluate the learner’s
performance in a delayed retention or transfer test, administered sufficiently
long after practice so that the temporary effects of practicing have
dissipated.
In addition, people may be affected differently by these temporary factors—
yet another form of individual differences (cf. chapter 7). For example, a
learner who is more susceptible to fatigue than another will show larger
temporary decrements in performance during practice, perhaps leading to
the false conclusion that learning progress has been slow. Evaluating the
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learning level by a delayed test under relatively rested conditions provides
a more complete representation of actual learning progress.
Transfer of Learning
Transfer, closely related to learning, is seen when practice on one task
contributes to performance capability in some other task. Transfer can be
positive or negative, depending on whether it enhances or degrades
performance on the other task, usually as compared to a no-practice control
condition. An important variation of the ideas about learning addressed thus
far requires further discussion—transfer of learning. As the name implies,
this concept involves the learning achieved in one task or in one practice
setting when it is applied to the performance of some other task, or in some
other setting, or both. A good example involving transfer of learning is the
handgun skills of a police officer. Transfer is a particularly important notion
for instructors of police officers because the conditions under which
practice is conducted are obviously quite different than the conditions in a
real situation in which handgun involvement is required. However, for the
safety of the public and the people involved, the skills acquired in practice
must be maximally transferrable to the broadest range of “transfer”
conditions possible. Therefore, teaching for transfer, or organizing practice
and instruction to facilitate transfer of learning, is an important goal for most
instructional programs.
Role of Transfer in Skill Learning Settings
Transfer is assumed whenever the skills learned in one task are applied
successfully to the performance of some other task version. Consider police
officers who undertake handgun shooting practice, for example. Shooting at
targets at a practice range under rested, nonstressful conditions perhaps
assumes that this experience will transfer to shooting in a life-or-death
situation. In this situation, the transfer from practice to the transfer task skill
situation must be substantial. If it is not, practicing the drills could be
largely a waste of time.
Transfer is also involved when instructors modify skills to make them easier
to practice. For example, relatively long-duration, serial skills, such as
doing a gymnastics routine, can be broken down into their elements for
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practice. Practicing the stunts in isolation must benefit the performance of
the whole routine (the criterion task), which is made up of the individual
stunts. However, in more rapid skills, such as a tennis serve, it is usually not
so clear that breaking down the skill into ball-toss and ball-strike portions
for part practice will be effective for transfer to the whole task. The
principles of transfer applicable to such situations are dealt with in chapter
9.
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Complex skills may be broken down into simpler elements for beginning
learners, but to be valuable, this learning must eventually transfer to the
criterion task.
How Is Transfer Measured?
Issues concerning the measurement of transfer are closely related to the
learning measurement issues discussed earlier. Essentially, we want to
estimate the performance level of the criterion task, with the relatively
permanent effects of learning separated from any temporary performance
effects. However, rather than asking how practice variations of a given task
affect learning, transfer concerns how performance on the transfer task is
influenced by practice on some other task.
Suppose you want to know whether practicing golf at the driving range
transfers to the actual game of golf. Consider three hypothetical groups of
subjects with different kinds of practice experiences. Group 1 practices for
4 h at the driving range; group 2 does not receive any practice; and group 3
practices for 4 h at miniature golf. After these various practice activities, all
groups transfer to (are tested on) five rounds of golf on an actual golf
course. The results of this hypothetical experiment are shown in figure 8.7,
where the average scores for the five rounds of golf are plotted separately
for the three groups. Assuming that the groups are equivalent at the start of
the experiment, the only reason for the groups to be different on the first
(and subsequent) rounds of golf is that the previous experiences have
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somehow contributed to, or detracted from, actual golf skill. Therefore, the
focus for transfer would be on the relative differences among the groups on
the criterion task.
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Figure 8.7 Hypothetical average scores for a round of golf as a function of
prior practice experiences. Earlier, group 1 (red line) practiced at a driving
range, group 3 (green line) practiced miniature golf, and group 2 (blue line)
received no practice. The differences between groups 1 and 2 demonstrate
positive transfer from practice at the driving range. The differences between
groups 2 and 3 reveal negative transfer from practice at miniature golf.
Figure 8.7 shows that group 1, which had practiced at the driving range
before five rounds of golf, performed more effectively than group 2, which
received no previous practice. This difference is usually measured on the
first trial, or at least on the first few trials, before the additional practice on
the criterion task can alter the skill levels very much. In this case, we would
have said that the driving range experience transferred positively to golf
because it facilitated golf performance over and above no practice. If this
were an actual experiment, you might conclude that the skills developed on
the driving range were applicable in some way to those on the golf course,
but those for miniature-golf practice were not.
Transfer can also be negative, as you can see by comparing groups 2 and 3
in figure 8.7. Notice that group 3, which had practice only on the miniature-
golf task, performed more poorly on the rounds of golf than group 2, which
had no prior practice at all. In this case, miniature-golf experience
transferred negatively to golf performance. If this had happened in an actual
experiment, you might conclude that the skills learned in miniature golf not
only were different from those required on a golf course, but in fact led to
disruption in the performance and learning of those skills needed in the golf
game.
How much positive transfer occurred, and how can we put a measurement
number on it? One way is through so-called percentage transfer. A means of
doing this is to provide an estimate of the total amount of improvement of
the group that had no prior practice on any task—in this case, group 2, and
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then compare that to the initial performance of the group that had the type of
practice being evaluated. Simply taking the initial performance score of the
no-practice group (120 strokes) and subtracting the final performance score
(109 strokes) shows an improvement from 120 to 109 = 11 strokes. Then we
see that group 1 (driving range practice) and group 2 (no prior practice)
differ by about five strokes on the first session; that is, of the total 11 strokes
of improvement that group 2 realized, five of them were gained in driving
range practice. Researchers often describe these changes in terms of
percentages—here, 5/11 = 45% transfer.
Alternatively, some researchers represent transfer in terms of a “savings
score,” where the amount of savings (here, in practice time) generated on
the criterion task is the result of having practiced at the driving range. In this
example, referring to figure 8.7 again, if we were to draw a horizontal line
from group 1’s initial performance (115 strokes) until it intersected the trend
line for group 2, and then dropped a perpendicular line to the x-axis, we
could compute a savings score—in this case about 1.7 rounds of golf for
group 1. That is, as a result of having practiced on the driving range, group
1 “saved” about 1.7 rounds of golf practice.
These measures of amount of transfer are clearly confounded or flawed by
concepts discussed already in this chapter. One of the fundamental ideas is
that performance curves and their shapes are often arbitrarily defined by
choices the experimenter makes before the study (e.g., the size of the target;
see figure 8.4 and the related discussion). So, in terms of the percentage
transfer measurement, basing the measurement of transfer on the changes that
the no-prior-practice group produced is also arbitrary, producing an
arbitrary percentage transfer score. A similar argument can be made for the
savings score; as figure 8.7 shows, the initial shape of group 2’s
performance curve is also somewhat arbitrary. About the best we can say is
that these measures provide useful ways of describing transfer results in
relative terms, but they shouldn’t be taken too seriously.
Specific Transfer
The previous sections on measuring learning have perhaps left the
impression that the only way to measure the relative amount learned is by
performance on some delayed retention test. This is probably the most
important way to estimate learning, but other ways are possible, and some
are even preferable in some situations.
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The essential issue is what we want learners to be able to do after training.
In some cases, learners are trained to be proficient at a specific task with a
limited range of variations. For example, basketball players take foul shots
using a set shot from a distance of 15 ft (4.6 m) to the basket (i.e., from the
free-throw line). So, it seems perfectly reasonable to devote considerable
set-shot practice from the free-throw line because the set shot is a specific
type of basketball skill that is normally not performed at any other location
on the court. Many closed tasks share these characteristics.
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Because a basketball free throw is a closed skill that takes place at a set
point on the court, specific transfer from practice is easy to achieve and
measure.
Generalized Transfer
One critical aspect of many training settings is the extent to which the
practice transfers to different yet very similar settings in the real world.
This is sometimes termed near transfer, the learning goal being a task
relatively similar to the training task. A good example that contrasts nicely
with the free throw is the jump shot in basketball. The jump shot can be
taken from an infinite number of places on the court and under a variety of
game situations. Being able to perform in such varied and unpredictable
conditions is one mark of a highly skilled performer. Therefore, tests of
transfer in which performance is measured on some variant of the task that
is similar, yet different, from those in the practice conditions would be a
reasonable test of generalized transfer.
Sometimes instructors want to train learners to develop more general
capabilities for a wide variety of skills, only a few of which are actually
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experienced in practice. This is usually termed far transfer because the
eventual goal is quite different from that in the original practice setting. For
example, elementary school children are taught to throw, jump, and run; the
main concern is the extent to which these activities transfer to future
activities involving throwing, jumping, and running but occurring in very
different settings.
In all these situations, the evaluation of training effectiveness is not based
exclusively on how well the learners master the skills during actual
practice. Rather, if transfer to relatively different activities is the goal, the
most effective training program will be the one that produces the best
performance on some transfer test performed in the future—one that may
involve quite different skills from those actually practiced. Here, the
effectiveness of a training program is measured by the amount of transfer to
some different activity.
Summary
Motor learning is defined as a set of processes associated with practice. As
such, the emphasis is on the determinants of this capability, which supports
or underlies the performance. Hence, factors that affect performance only
temporarily need to be distinguished from the factors affecting this
underlying capability, which makes the use of “learning” curves somewhat
risky for evaluating learning.
However, temporary and learning effects can be separated through the use of
transfer or retention tests. In some experiments on learning, groups of
learners practice under different conditions in acquisition; after a delay, they
are tested on the same task but under conditions that are identical for all the
groups. This procedure focuses attention on the relative performance in the
retention tests as measures of learning.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
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http://www.HumanKinetics.com/MotorLearningAndPerformance

Interactive Learning
Activity 8.1: Using a figure from the text, interpret the findings of a
transfer design study to understand the effects of a mechanical aid on
learning.
Activity 8.2: Review the types of learning transfer by matching terms
with their definitions.
Activity 8.3: Check your understanding of the definition of motor
learning through a fill-in the-blanks exercise.
Principles-to-Application Exercise
Activity 8.4: The principles-to-application exercise for this chapter
prompts you to choose a skill and examine the effects of practice for a
person learning that skill. You will identify the possible temporary and
permanent effects of practice and consider how you could collect
evidence to distinguish temporary from permanent effects.
Check Your Understanding
1. Define motor learning and indicate why each of the following terms is
important to that definition.
Capability
Practice and experience
Performance
2. Distinguish between near transfer and far transfer and between positive
transfer and negative transfer. Give one example of each.
3. List and describe two limitations of using performance curves to
evaluate learning progress.
Apply Your Knowledge
1. List three essential features of a transfer design. How would these
features be included in an experiment to examine if the use of a pole to
aid in balance during a one-foot stance task is beneficial to learning to
perform the one-foot balance task without the pole?
2. Describe one practical setting where the proficiency level reached at
the end of a practice session may not reflect the actual performance
capability achieved under the conditions where the skill will
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http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30147

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30148

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30149

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30150

eventually be required. How could you assess the true level of skills
learned during practice?
Suggestions for Further Reading
Important cautions about the learning–performance distinction are provided
by Kantak and Winstein (2012) and Cahill, McGaugh, and Weinberger
(2001). An analysis of so called “learning curves” is given by Stratton and
coauthors (2007). More information on the measurement of learning can be
found in Schmidt and Lee (2011, chapter 11) and also on the retention and
transfer of learning (chapter 14). See the reference list for these additional
resources.
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Chapter 9
Skill Acquisition, Retention, and
Transfer
How Expertise Is Gained
Chapter Outline
Skill Acquisition
Skill Retention
Skill Transfer
Summary
Chapter Objectives
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Chapter 9 describes the processes that influence skill acquisition,
retention, and transfer. This chapter will help you to understand
basic principles of the skill acquisition process,
two conceptualizations of learning stages during skill
acquisition,
factors that influence the retention of skills after periods of no
practice, and
factors that influence the transfer of skills to new tasks or
performance situations.
Key Terms
autonomous stage
cognitive stage of learning
criterion task
degree of freedom
degrees of freedom problem
error-detection capability
fixation (associative or motor) stage
forgetting
gearshift analogy
generalizability
lead-up activities
part practice
physical fidelity
progressive part practice
psychological fidelity
repetition
set
simulator
specificity of learning
warm-up decrement
whole practice
Playing the guitar provides a good example of how practice leads to the
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development of motor expertise. Consider the beginning chords of a song
such as Link Wray’s classic, “Rumble.” The first three chords are D, D, and
E. For a right-handed guitarist, the D chord is achieved by pressing down
onto the third fret of the second string plus the second fret of the third string
with the left hand while strumming the second, third, fourth, and fifth strings
with the right hand. The D chord is played twice; then the fingers on the left
hand shift to make an E chord, which requires fingers on the left hand to
depress onto the first fret of the third string and the second fret of the fourth
and fifth strings while the right hand now strums all six strings.
The beginner guitarist is faced with a number of problems to be solved
simultaneously, such as knowing which fingers are placed on which frets,
having to avoid strings that should not be touched, remembering what
positions compose what chords and which strings the right hand should
strum, then moving the hand and fingers to create a whole new chord. And
this does not even consider the timing structure that must underlie these
chords. It is very impressive, indeed, that anyone can learn to play the guitar
given what we’ve just said; yet many do, and many do it very well.
Learning to drive a car provides another good example of the topics
presented in this chapter. Typically, the beginning driver goes through
changes in skill progression, called stages of motor learning. Certain
principles of learning apply to almost all motor learning, and these
principles result in the acquisition of a specific set of subskills that support
driving performance. But that is not the whole story about motor learning.
Time away from the task of driving has an impact on future performance
because the retention of skills is expected to be different for different types
of tasks; so, being able to classify driving as a member of one of several
classes of skills is important. And lastly, motor learning would be very
inefficient if we had to progress through the entire skill acquisition process
for each and every vehicle that we drive and every situation in which we
find ourselves. That is, we expect our driving skills to generalize (or
transfer) to different vehicles, situations, and environments; therefore,
information about the factors that are expected to affect transfer constitutes a
critical component of any discussion of the learning process.
Skill Acquisition
Quite simply, the single most important factor leading to the acquisition of
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motor skill is practice. However, “practice” is also probably one of the
most poorly understood and misused terms when applied to the concept of
learning. In this section we describe some principles of practice, how they
affect learning, and what occurs as the result of practice.
Basic Principles of Practice
It almost goes without saying that the most important variable for learning is
practice itself. There’s no easy way around it; and as a general rule, more
practice produces more learning. But the amount of practice time is not the
only concern here, and not all practice methods are equal in their impacts on
learning. In this section we describe the basic principles of effective
practice, and what might and (might not) lead to effective and efficient skill
acquisition.
Practice Is More Than Just Repetition
A frequently misused substitute for the term practice is “repetition,” and
many well-intentioned instructors and coaches confuse the two concepts. To
us, the term “repetition” invokes the idea of repeating a movement, and
again, and again, and so on. The concept brings to mind the idea that
repetitious movements somehow “groove” or “stamp in” a memory, with
more repetitions leading to a deeper groove or more durable stamp in
memory. The metaphor causes one to think (incorrectly, in our view) of
learning as a concept similar to muscle hypertrophy, which results from
repetitious exercise.
Consider the following quotes from two very influential theorists as
counterpoints to the traditional view of “practice as repetition.”
Bartlett: When I make the [tennis] stroke I do not . . . produce
something absolutely new, and I never repeat something old. (Bartlett,
1932, p. 202)
Bernstein: The process of practice towards the achievement of new
motor habits essentially consists in the gradual success of a search for
optimal motor solutions to the appropriate problems. Because of this,
practice, when properly undertaken, does not consist in [simply]
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repeating the . . . solution of a motor problem time after time, but
[rather] in the process of solving this problem again and again by
techniques which we changed and perfected from repetition to
repetition. It is already apparent here that, in many cases, “practice is a
particular type of repetition without repetition, and that motor training,
if this position is ignored, is merely mechanical repetition by rote, a
method which has been discredited in pedagogy for some time.”
(Bernstein, 1967, p. 134)
The conceptual model that we have developed throughout the book
represents the most important components of the human information-
processing system that are involved in movement control. These components
fluctuate due to temporary factors, improve with development, and regress
with advancing age. Importantly, though, the components of the processing
system become more effective and efficient with learning. In our view, the
most effective learning occurs when a repetition activates as many of the
individual components of the system as possible. In this way, a repetition is
successful to the degree that it engages the entire conceptual model
presented in figure 8.1.
Specificity of Practice
Although transfer is a hallmark of learning (discussed in more detail later in
this chapter, and in chapter 11), a consistent finding in the literature is that
motor learning is quite specific. In general, specificity of learning suggests
that what you learn depends largely on what you practice. Specificity effects
are wide-ranging. This example might make the idea more clear: If you want
your soccer team to perform well in the dark, in the rain, and in front of
many noisy fans, then you must practice in the dark, in the rain, and in front
of many noisy fans. Practicing in a particular environment or workspace
often leads to better performance mainly in (sometimes only in) that
workspace, compared to a different or altered workspace (this is perhaps
one of the bases of the so-called home-field advantage; Carron, Loughhead,
& Bray, 2005). Another important finding is that the sensory feedback (e.g.,
visual, auditory, tactile) resulting from performance during specific types or
locations of practice becomes part of the learned representation for skill,
such that later performance is more skillful when that same sensory
information is available, compared to situations in which one or more of
these feedback channels are altered (Proteau, 1992). Thus, while an
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important goal of practice is to facilitate transfer (i.e., performance
enhancement for unpracticed situations or contexts), it is important to
recognize that specificity of learning is the dominant characteristic.
Learning Versus Performance During Practice
Perhaps it is obvious that when learners acquire a new skill, they do so by
doing something different than they had done earlier. The processes leading
to learning require that the learner change something in the movement
patterning, hopefully so the performance becomes more effective. Yet, when
assisting learners during practice, many instructors encourage learners to
“do your best” on each practice attempt. This generates two conflicting
practice goals: performing as well as possible in practice versus learning as
much as possible in practice by attempting to change movement patterning.
The learner who attempts to perform as well as possible in practice tends to
be inhibited from modifying (“experimenting with”) movements from
attempt to attempt, which detracts from learning. The approach for
maximizing performance, repeating the most effective pattern discovered so
far, is not effective for learning in part because it discourages such
experimentation. One way to separate these conflicting practice goals is to
provide two fundamentally different activities during practice—practice
sessions and test sessions.
First, provide practice sessions in which you instruct the learners simply to
avoid repeating what they did earlier. Tell the learner to try different styles
of movement control to discover some more effective pattern of action. You
can guide the learning by suggesting specific ways to alter the movement,
helping the learner eliminate inappropriate patterns. The learner should
know that performance quality is not critical during this practice period, and
that the only goal is to discover some new way to execute the skill that will
be more effective in the long term.
Of course, the measure of the effectiveness of this learning progress is a test
of some kind. After several minutes in the practice session, the instructor
could announce a switch to a “test session,” in which the next five attempts
are treated as “a test.” In the test session the learner is to perform as well as
possible, using the best estimate gained so far of the movement pattern for
the most proficient performance. After the test session, the learner has some
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idea of his progress and can return to the practice mode to continue
searching for more effective movement patterning. Such tests could be
formally evaluated and graded, but they can also be effective if given only
for the student’s own information. Evaluating progress by asking learners to
compile their own test scores is an excellent method to help them assess
their own progress; it is both motivating and educational.
Focus on Application 9.1
Principles of Golf Practice
A frequent complaint of golfers who try to improve their skills
through driving-range practice is that their performance on driving
ranges is rarely matched on the golf course. In fact, the lament “But I
was hitting the ball so well during practice” seems to summarize
quite well the lack of progress that aggravates the “weekend
warrior.” The typical golfer rents a pail of balls at the local driving
range, goes to a small booth that has an artificial hitting mat, and
places a ball on the rubber tee. After a short warm-up, a golf club is
withdrawn from the bag, an intended target out in the range is
selected, the direction of the shot is determined, the stance is taken,
and the shot is made. Then, with very little hesitation, a new ball is
placed on the tee, and another shot is made. This process continues
for 5, 10, or 20 more shots, until finally the golfer decides to try a
different club. And on it goes. Let’s take a look at this typical
driving range practice behavior and compare it to the principles of
practice discussed in this chapter.
1. Repetitions. The golfer’s focus is on the swing, because that is
the message of almost all of the instructional videos and
infomercials that the golfer has seen on TV. If a successful shot
is made, the golfer’s immediate goal is now to repeat it.
Repetition is key, and very little time and energy are spent
either in preparation for the shot or in contemplating the result
afterward.
2. Practice design. Our golfer really does not have the motivation
to make a serious plan for the practice session. The only plan
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is to try to improve—usually with major emphasis on the
swing.
3. Specificity. Although this is not a fault of the golfer, consider
the very nature of the construction of most golf driving ranges.
The hitting areas use rugs of artificial turf; the ball sits in a
good, flat position; and there are no obstacles in front of the
golfer to go around, over, or under. In other words, the typical
driving range shot is taken under ideal conditions—conditions
quite unlike those that the golfer will face on the course. So, the
golfer learns to hit under nearly ideal conditions.
4. Learning and performance goals. Quite simply, the typical
golfer equates good performance in practice with successful
learning. And this illusion of progress, perhaps more than
anything else, is what frustrates the golfer most.
To be fair, though, we shouldn’t beat up our golfer too much. After
all, these “violations” of the principles of practice are not
uncommon in most types of sport skills, workplace training
environments, and rehabilitation settings. And in reality, it would
not take much effort to make golf range practice more effective for
learning. Here are a few ideas:
Sketch out a plan for the practice session before going to the
range. Establish clear goals and how you plan to achieve them.
Make each shot “count.” Try to create an image of each shot as
it might look at your favorite course. Go through your typical
preshot thinking routine before the ball is struck, and evaluate a
number of different components of the shot afterward, perhaps
in terms of a checklist (e.g., swing, ball contact, ball flight,
final location in relation to target).
Try to create different types of shots to be made, even if they
are just imaginary (e.g., a low shot into the wind or a high shot
over a tree).
Interject “tests” into practice, such as short games (chipping
and putting from three locations around a green), either by
yourself or in competition with a friend.
The bottom line here is that the skills needed to play the game of
golf are the same skills that need to be practiced on the driving
range. The frustrations that plague the typical golfer probably arise
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largely from the manner in which practice is undertaken.
Benefits of Practice
Obviously, a major goal of practice is effective performance, which can be
thought of as developing the capability to perform some skill on future
demand. However, there are several other benefits of practice that leave the
learner with capabilities not so directly related to actual task proficiency.
Actually, the term “motor learning” is a bit of a misnomer, as what results
from practice is much more than just “motor” learning. In this section we
briefly describe some of the benefits to be expected from practice.
Perceptual Skills
The game of chess provided an unlikely beginning for this important
research area. DeGroot (1946) and Chase and Simon (1973) showed chess
experts and nonexperts either a partially played game of chess or a board
that had a similar number of pieces placed randomly on the board (i.e., a
grouping of chess pieces that was unlikely to occur in an actual game). After
viewing the board for only about 5 s, the subjects were asked to recreate the
scene they had just viewed by placing the pieces on another board. As
expected, the experts were much better than the nonexperts at recreating the
board that had a “game structure.” But there was no difference between
experts and novices when they recreated the randomly-arranged board. This
study provided important evidence that the capability to remember briefly
presented information is specific to the skills of the observer—here, for the
chess experts, acquired over many years of practice.
The chess studies initiated a research paradigm that has since addressed the
perceptual advantage that is gained with practice and experience. Different
methods are used to assess this advantage. One example is showing video
clips of specific activities (as in sporting events) in which a critical portion
of the video (e.g., the pre–ball-release arm movements of a baseball
pitcher) is clipped from viewing, and subjects (usually experts and
nonexperts) are asked to predict something about the result of the occluded
action (e.g., something about the ball’s flight; see Williams, Ward, &
Smeeton, 2004, for a review). Another research strategy is to monitor the
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eye and head movements made by performers of differing skill to assess
whether the active search for information is changed by practice (e.g.,
Vickers, 2007). Results from this general research area tend to support the
original findings with chess players—that this expertise is quite specific to
the nature of the skill that has been practiced. The research also shows that
experts tend to seek out more specific and narrowly focused information in
a perceptual display, and to pick up that information much earlier in the
action than nonexperts (see Abernethy et al., 2012, for an excellent review).
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Expert chess players are able to better remember and recreate game-like
arrangements of pieces on a board, demonstrating the perceptual advantages
gained through practice.
Attention
Attention represents a major focus of chapter 3. We presented several
different concepts of attention, which can now be considered from a
learning perspective—the effect of practice on these concepts of attention.
We consider two of these in this section.
Reduced Capacity Demands
One of the fundamental concepts from chapter 3 was that most tasks demand
some attention for their performance, and performance suffers when the
overall demand exceeds the available attentional capacity (e.g., see the
discussion on distracted driving in Focus on Research 3.1). Thus, one
benefit of practice is the reduced attention that is demanded by tasks that
have been well learned. A wonderful study by Leavitt (1979) illustrates this
concept well. In his study, Leavitt compared young hockey players of
different ages, and different playing abilities within each age, in the
performance of a skating task (which was the “main task”), done either
without a stick and puck or when stick-handling a puck. The time required to
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skate over a fixed distance was measured. Ice skating without controlling a
puck simultaneously would appear to be a much less attention-demanding
task than when controlling a puck with a stick at the same time.
Leavitt’s (1979) results are illustrated in figure 9.1. Some very interesting
points are exemplified in this figure. First, note that skating times with the
stick and puck are, in general, longer than during skating without the stick
and puck. Second, the limitation in time due to skating with the puck
becomes progressively smaller as children become older (i.e., as they
advance from the Novice to Atom to Peewee to Bantam age groups). And,
lastly, even within each age group, more highly skilled players suffered less
decrement in time when skating with the puck than did the Novice players.
All of this evidence suggests that the decrement to skating performance that
occurs when players must stick-handle a puck simultaneously with skating is
reduced as skill improves.
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Figure 9.1 Skating times measured when subjects were skating alone (the
“no-puck” condition) and when they were skating and stick-handling (the
“puck” condition) simultaneously. (Novice = 7.9 years of age on average;
Atom = 10.1 years; Peewee = 11.3 years; Bantam = 14.1 years).
Data from Leavitt 1979.
Reduced Effector Competition
Another important concept of attention is the interference that can arise
when a task requires us to do two or more different things at the same time.
The classic example of patting your head and rubbing your stomach at the
same time illustrates the problem here (see figure 9.2). The issue shares
some similarities with the attention-demand concept, as discussed in the
previous section and in chapter 3, but it is unique in the sense that one has
no trouble rubbing (or patting) both the head and the stomach at the same
time. So, it is not a matter of doing two things at once, but a matter of doing
two different things at the same time.
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Figure 9.2 A typical coordination problem that can be solved with practice.
Interference arises when one effector has a movement goal or pattern that is
different from the goal (or movement pattern) of the other effector. The
research of Bender (1987), discussed in chapter 6, illustrates nicely the
problem and the benefits that result from practice. She found that producing
the English letter “V” with one hand and the Greek letter “γ” with the other
hand was nearly impossible when these actions were to be performed at the
same time. Perhaps this is due to the competition between underlying motor
programs (see figure 6.12). However, effector (or motor program)
competition was reduced some with considerable practice, perhaps due to
the development of a single motor program that was responsible for
controlling the two limbs as if they were a single limb (i.e., with a single
motor program; see also Schmidt et al., 1998). The difficulty seems to be
that the system is attempting to run two different programs at the same time
(see chapter 6 for more discussion), and practice moves the learner closer
to having a single motor program that governs both actions.
Motor Programs
A prominent theme in the motor learning literature suggests that many motor
skills are learned through developing motor programs, as discussed in detail
in chapter 5. How are these motor programs learned? The gearshift
analogy provides a useful way to answer this question. When first learning
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to shift the gears of a standard-transmission car, the beginning driver goes
through each of the seven steps illustrated in figure 9.3, the movement for
each of these steps being controlled by separate motor programs. As some
proficiency is gained, some of these steps are combined into larger motor
programs that are capable of controlling the movements for two of more of
the individual steps. At the highest level of skill (e.g., with a race-car
driver) all seven steps are controlled by a single motor program.
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Figure 9.3 The gearshift analogy, using the example of shifting a standard-
transmission car from second to third gear. Practice results in the
reorganization of seven individual motor programs into one program.
Reprinted by permission from Schmidt and Lee 2011; Adapted from MacKay 1976.
Whether or not a sequence of actions is controlled by one, two, or perhaps
more motor programs in sequence has been addressed by research designed
to identify units of action. Imagine that a kinematic analysis of a movement
allowed the measurement of the time of certain kinematic features (e.g., time
of peak acceleration, time of maximum velocity). Since these kinematic
features of movements are highly correlated in time over many individual
trial executions (within subjects), then we assume that these actions were
run off with a single motor program. A low correlation in time between any
pair of such landmarks is evidence of separate programs (Schneider &
Schmidt, 1995). This type of research can have many applications. For
example, using this rationale, researchers analyzed childproof butane
lighters and provided evidence that more than one motor program was
required to complete all of the steps of igniting the lighter (Schmidt et al.,
1996)—demonstrating that the safety feature was likely to be effective.
Error Detection
Error-detection capability represents another goal of practice. For
example, when a student is learning cardiopulmonary resuscitation (CPR)
skills, the instructor is usually present during practice. Therefore self-
detection of errors is not critical because the instructor is there to point them
out and suggest corrections. However, when the learner attempts to perform
this skill in an actual emergency, the instructor will not be available to
provide this corrective information. The learner who is able to detect and
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analyze her errors independently, and thus make corrections “in the
moment,” will be a far more skilled provider of CPR. This error-detection
and correction capability tends to make the learner self-sufficient, which is
one overall goal of practice. We will have much more to say in chapter 11
about how error-detection skills can be promoted (or discouraged) by the
manner in which augmented feedback is presented.
Tasks differ with respect to the saliency of different types of sensory
information, of course. The sound of the engine is particularly important to
the race car driver, and the sound of the guitar is obviously important to the
musician. Visual information is critical to the dentist, although when
provided by a mirror, that information requires special translation skills.
People who have lost their sight can become particularly adept at tactile
discrimination using movement (e.g., using Braille).
Stages of Learning
It is useful to consider learning as a series of relatively distinct stages (or
phases) that can be identified in the skill acquisition process. These stages
should not be confused with the information-processing stages discussed in
chapter 2. Rather, they are descriptors of the different levels of skill
development. Two important stages-of-learning contributions have been
made, one by Fitts and another by Bernstein, each from a very different
perspective (see Anson, Elliott, & Davids, 2005, for further discussion).
Fitts’ Stages
The stages suggested by Fitts (1964; Fitts & Posner, 1967) were
specifically designed to consider perceptual–motor learning, with emphasis
on both the perceptual and motor components involving skill acquisition.
This perspective places heavy emphasis on how the cognitive processes
invested in motor performance change as a function of practice.
Fitts’ Stage 1: Cognitive Stage
As the stage name implies (cognitive stage of learning), the learner’s first
problem is cognitive, largely verbal (or verbalizable); the dominant
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questions concern goal identification, performance evaluation, what to do
(and what not to do), when to do it, how to do it, and a host of other things.
As a result, verbal and cognitive abilities (discussed in chapter 7) dominate
at this stage. Figuring out what to look at in the environment (or listen to, or
feel, and so on) and generating an appropriate movement attempt are
critical.
Instructions, demonstrations, film clips, and other verbalizable information
are also particularly useful in this stage. One goal of instruction is to have
the learner transfer information from past learning to these initial skill
levels. For example, many skills have similar stance requirements, so
instructions that bring out already known stances should be useful for
teaching a new one (e.g., “adopt a stance like you would in skill x”). Often,
several previously learned movements can be sequenced together to
approximate the desired skill (e.g., to shift gears, push in the clutch, move
the gearshift down, and let out the clutch while gently pressing the
accelerator), and can provide a start for later learning. Gains in proficiency
in this stage are very rapid and large, indicating that more effective
strategies for performance are being discovered. It is not of much concern
that performance at this stage is halting, jerky, uncertain, and poorly timed to
the external environment; this is merely the starting point for later
proficiency gains.
Some learners engage in a great deal of self-talk, verbally guiding
themselves through actions. However, this activity demands considerable
attention and can interfere with the processing of other sensory events that
may be going on at the same time. Verbal activity is effective for this initial
stage, though, facilitating a rough approximation of the skill, and will likely
drop out later.
Fitts’ Stage 2: Fixation Stage
The performer next enters the fixation stage (sometimes called the
associative stage or motor stage). Most of the cognitive problems dealing
with the environmental cues that need to be attended to and the actions that
need to be made have been solved. So, now the learner’s focus shifts to
organizing more effective movement patterns to produce the action. In skills
requiring quick movements, such as a tennis stroke, the learner begins to
“build” a motor program to accomplish the movement requirements. In
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slower movements, such as balancing in gymnastics, the learner constructs
ways to use movement-produced feedback.
Several factors change markedly during the fixation stage, associated with
more effective movement patterns. Performance improves steadily. Some
inconsistency from trial to trial is seen as the learner attempts new solutions
to movement problems. Inconsistency gradually decreases, though; the
movements involving closed skills begin to be more stereotypic, and those
involving open skills become more adaptable to the changing environment
(Gentile, 1972). Enhanced movement efficiency reduces energy costs, and
self-talk becomes less important for performance. Performers discover
environmental regularities to serve as effective cues for timing. Anticipation
develops rapidly, making movements smoother and less rushed. In addition,
learners begin to monitor their own feedback and detect their errors. This
stage generally lasts much longer than the cognitive stage.
Fitts’ Stage 3: Autonomous Stage
After considerable practice, the learner gradually enters the autonomous
stage. This is the stage usually associated with the attainment of expert
performance—perceptual anticipation (chapter 2) is high, which speeds the
processing of environmental information. The system generally programs
longer movement sequences; this means that fewer programs need to be
organized and initiated during a given interval of time, which decreases the
load on attention-demanding movement initiation processes.
The decreased attention demanded by both perceptual and motor processes
frees the individual to perform simultaneous higher-order cognitive
activities, such as making higher-level decisions about strategies in sport,
expressing emotion and affect in music and dance, and dealing with stress
and chaos in emergency care activities. Self-talk about the actual muscular
performance is almost absent, and performance often seems to suffer if self-
analysis is attempted. However, self-talk could continue in terms of higher-
order strategic aspects. Self-confidence increases and the capability to
detect and correct one’s own errors becomes more fine-tuned.
It is important to remember that performance improvements in the
autonomous stage are slow, because the learner is already very capable
when this stage begins. However, learning is far from over, as shown in
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many studies (see Focus on Research 9.1, for example).
Focus on Research 9.1
Learning Never Ends
The shape of the classic skill acquisition profile, sometimes
erroneously termed a “learning curve,” is illustrated by rapid gains
early in practice, improvements slowing down as some proficiency
is gained, then a gradual slowing to an eventual point where no
further improvements in skill are detected. So, would it be correct
to say that learning has ended as this point? Two classic studies in
the motor learning literature suggest that the answer is no.
Bryan and Harter (1897, 1899) studied the perceptual–motor skill of
telegraphy—involving the language of sending and receiving Morse
code. They studied telegraphers of varying levels of experience and
discovered many important findings. For example, the perceptual
skill of receiving Morse code became faster and more efficient with
practice. However, this was achieved not only through quantitative
improvements (speed of letter detection), but also by means of
qualitative change—as skill developed, telegraphers progressed by
perceiving individual letters at first, then groupings of letters, then
whole words, and then common phrases or groupings of words. The
timing of dots and dashes is a critical component of sending Morse
code, and Bryan and Harter found that the consistency in movement
timing continued to improve over many years of experience (see Lee
& Swinnen, 1993, for more analysis). In sum, there was no evidence
that improvements stopped being made in either perceptual or motor
skill. Rather, performance became more efficient and less variable
with continued practice.
Crossman (1959) presented a very different type of analysis of a
skill. He tracked the performance of cigar rollers who used a
machine to combine leaves of tobacco into a finished product, a
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cigar. Crossman found that improvements in performance time
leveled off only after seven years of experience (or 10 million
cigars!) had been accumulated. However, the plateau in
performance was not due to a limitation in human performance.
Rather, performance leveled off because the limits of the cycle time
of the machine had been reached. Presumably the cigar rollers
would have continued to improve if they had not reached the cycle
limit of the machine.
The illusion that learning has ended is almost always caused by the
limits reached in the sensitivity of the measurement tool in revealing
further changes. New measurement tools continue to be developed
and to reveal the process of motor learning in more detail. For
example, recent studies of motor learning have revealed markedly
different effects of transcranial magnetic stimulation (TMS) on
performance, depending on whether it was applied early (after 5
sessions) or late (after 16 sessions) in practice (Platz et al., 2012a,
2012b). We suspect that future studies using these and other
measures of brain plasticity will provide clearer evidence that
learning never stops.
Exploring Further
1. Consider a skill such as archery, which can be measured in
terms of performance outcome (such as accuracy and
consistency) and movement proficiency (e.g., steadiness). How
might these different measures change at different rates as a
function of practice?
2. Think of another motor task and describe how expertise might
continue to evolve with continued practice. What measurement
systems would be required to observe these continued
changes?
Bernstein’s Stages
In contrast to Fitts’ emphasis on the information-processing aspects of
perceptual and motor components of skill, Bernstein identified stages of
learning from a combined motor control and biomechanical perspective.
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Bernstein’s Stage 1: Reduce Degrees of Freedom
The initial problem facing the learner is what to do with all of the possible
degrees of freedom of movement that are available for the body. A single
degree of freedom, in Bernstein’s view, refers to just one (out of all of the
ways) in which the various muscles and joints are free to move. Bernstein
considered that the solution to the so-called degrees of freedom problem
(i.e., how can the system control all of the degrees of freedom) was to
reduce the movement of nonessential or redundant body parts in the initial
stage of learning—in essence, by freezing these degrees of freedom. In
some ways, this solution achieved the same goal as Fitts’ first stage of
learning—since the number of degrees of freedom that need to be controlled
is reduced, there are fewer motions of the body that require conscious
control, which allows attention to be devoted to the few degrees of freedom
that provide maximal control of the rudimentary aspects of the action.
Bernstein’s Stage 2: Release Degrees of Freedom
As control of a minimum number of degrees of freedom in stage 1 begins to
result in some initial successes, the typical learner attempts to improve
performance by releasing some of the degrees of freedom that had initially
been “frozen.” This release of degrees of freedom would seem to be
particularly useful in tasks that require power or speed, as the degrees of
freedom that have been released could allow for faster and greater
accumulation of forces.
Bernstein’s Stage 3: Exploit Passive Dynamics
In Bernstein’s final stage, the performer learns to exploit the passive
dynamics of the body—essentially, the energy and motion that come “for
free” with the help of physics (such as gravity, spring-like characteristics of
muscle, and momentum). Thus, in Bernstein’s final stage, the movement
becomes maximally skilled in terms of effectiveness (achieving the end
result with maximum assuredness) and efficiency (minimum outlay of
energy).
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As youth hockey players increase their skill level, the tasks of maintaining
balance and handling the puck require less attention, allowing them to focus
on other aspects of the game.
Focus on Application 9.2
Fitts and Bernstein Learn to Play Ice Hockey
One way to conceptualize the different stages of learning
perspectives proposed by Fitts and by Bernstein is with an example.
Ice hockey involves two main tasks: skating (whole-body
movements using two boots fitted with sharp blades that cut into the
ice) and stick-handling (using a hockey stick to pass, shoot, and
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manipulate a flat rubber disk called the puck). What follows is an
analysis regarding how the stages of learning might proceed
according to Fitts and Bernstein.
Fitts’ Stage 1
From Fitts’ perspective, the process of maintaining balance is a
primary concern, requiring massive amounts of attentional resources
just to stay upright. Holding the stick is highly verbalizable, and
doing so correctly is an important consideration. For right-handed
players, the left hand holds the stick near the top of the shaft and the
right hand holds it partway down the shaft. Shooting, passing, and
manipulating the puck require considerable conscious resources
since each of these activities perturb balance and pose a threat to the
learner’s posture.
Bernstein’s Stage 1
For Bernstein, the same problem of staying upright and striking the
puck is solved by reducing body motion. A steady base is critical;
therefore movements that would destabilize that base, such as taking
a long stride during the act of shooting or passing a puck, are largely
avoided or reduced in magnitude. The motions of the body during
skating are rather rigid, again to avoid destabilizing balance. The
stick is used as a “crutch” during this stage to help maintain balance.
Fitts’ Stage 2
Rudimentary skill has been achieved by the start of this stage—the
learner has acquired the basic skills to skate, shoot, pass, and
manipulate the puck, although these require considerable cognitive
resources to coordinate at the same time. Attention to skating and
stick handling has been reduced, however, allowing the learner to
attend to other perceptual attributes of the situation, for example
locating members of the opposing team and anticipating the
movements of teammates.
Bernstein’s Stage 2
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For Bernstein, the learner’s skating skills have improved
dramatically due to the release of the body’s degrees of freedom.
Rather than appearing to “walk” on skates, the learner makes a
sideward push with one skate and glides forward on the other skate.
Considerably more trunk rotation is used to move, resulting in much
faster skating speed. Control of the puck has also improved through
more involvement of the wrist, forearm, and shoulder muscles. The
result is greater force production and accuracy in shooting and
passing the puck, as well as more effective stick-handling control.
Fitts’ Stage 3
In Fitts’ autonomous stage, very little attention is given to the
processes involved in skating and puck control, or even the
simultaneous coordination of these skills. Now the learner’s
cognitive involvement is invested in higher-order activities of the
game—detecting patterns of game flow by members of both teams,
planning strategic plays whereby the learner goes to locations on the
ice that could provide an offensive or defensive advantage, or taking
advantage of perceived weaknesses.
Bernstein’s Stage 3
Bernstein’s final stage exploits the energy that comes with the fast
and dynamic play of the game. Players learn to stop, turn,
accelerate, and decelerate with precision and use the passive
dynamics of their own body but also the energy obtained from on-
ice objects, both animate (other players) and inanimate (e.g., the
rink’s wall, or “boards”). Modern equipment (skates and sticks) are
manufactured with composite materials, which are designed to be
fully exploited by only the most highly skilled players.
Summary
This analysis represents just one example of how Fitts’ and Bernstein’s
stages of learning might characterize the ways in which skill development
proceeds. As you can readily tell from this comparison, the Fitts and
Bernstein perspectives should not be seen as competing with each other.
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Rather, they represent two different theoretical approaches conceptualizing
somewhat different sets of processes that change with practice.
Limitations of Fitts’ and Bernstein’s Stages
One thing that is important to keep in mind about both the Fitts and the
Bernstein perspectives is that neither was meant to describe learning as a
series of discrete, nonlinear, and unidirectional stages. The progression
from one stage to another is not categorical, such as leaving one room of a
house and entering another. In many ways, these stages were meant to be
considered as generic descriptions of performance capabilities and
tendencies at any one time in the learning process, with the proviso that
these capabilities change as learning progresses.
There are a number of other aspects of these two schemes that need to be
considered. For example, Fitts considered performance change to be
regressive as well as progressive, in the sense that performance, under
conditions of high arousal or after a long layoff from practice, was expected
to produce performance tendencies characteristic of a previous stage (Fitts
et al., 1959).
Task differences also play an important role in the stage views of both Fitts
and Bernstein. For instance, “automaticity” in Fitts’ final stage might be
achieved for some tasks but never achieved for other tasks. As one example,
responding “automatically” to a specific stimulus with a specific response
can be learned with many hours of devoted practice (e.g., Schneider &
Shiffrin, 1977), although it might be impossible to ever develop
automaticity for a novel, complex, serial, or continuous movement task.
Similarly, the nature of the task might limit the application of Bernstein’s
stages (Newell & Vaillancourt, 2001). We can think of two common
examples of skills whose learning would seem to contradict Bernstein’s
stage 2 characterization. One of these involves doing a handstand on the still
rings in men’s gymnastics. The learner seems to begin learning this task
using nearly all of the available degrees of freedom (e.g., maintaining
balance with hip and trunk movements, using the arms). When the learner is
clearly past the initial stage, rather than releasing degrees of freedom, the
learner seems to freeze them. All of the wild hip and arm movements seem
to drop out, leaving behind control of balance by the wrists only: this would
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seem to be just the opposite order from Bernstein’s views, where the
learner seems to gain proficiency by freezing, then freeing, degrees of
freedom. Another example concerns the skill of learning to windsurf. At
first, the learner uses wild hip, knee, and arm movements to maintain
balance on the board, much as the gymnast does on the still rings at this
stage. Then, when clearly past the first stage, the learner seems to freeze the
hip, and knee movements so that the body is still and rigid, and control is
due to very small movements of the sail with small elbow and wrist
movements, again with freezing proceeding freeing. Konczak, vander
Velden, and Jaeger (2009) found that, similar to these gymnast and
windsurfer examples, expert violinists developed their skill by learning to
reduce rather than release motions of their bowing arm.
In sum, we believe that the stage ideas of both Fitts and Bernstein retain
considerable merit, and their longevity as descriptive analyses of the
learning process attests to a continued prominence in the motor learning
literature. However, one must not lose sight of the fact that these are rather
generalized descriptions, not firm theories about the learning process.
Skill Retention
This section concerns the “fate” of motor skills after a period of time during
which no further practice is undertaken, during which time forgetting may
occur. This interval of time is often termed the “retention interval.” As we
will discuss, the absence of practice often is detrimental to skilled
performance.
Forgetting
One of the truisms of life is that some skills seem never to be forgotten
whereas others are lost rather quickly. The folklore example of the former is
riding a bicycle, a skill that we seem to retain for very long periods of time
during which we experience no intervening practice. In contrast, some
memories are forgotten quite quickly, such as the number sequence of your
first telephone number.
Consider the following two studies that are representative of this contrast.
In the first study, conducted many years ago by Neumann and Ammons
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(1957), experimenters asked subjects to learn to match the locations of a
series of eight paired switches and lights. This task seemed to have a
relatively heavy verbal memory component to it, in that subjects had to learn
and remember which switch went with which light. Practice continued until
subjects performed two errorless trials, which, as illustrated in figure 9.4,
required about 63 trials, on average. At that point the subjects were split
into five subgroups, each defined by the length of time after which the
retention test would be performed—retention intervals of 1 min, 20 min,
two days, seven weeks, or a full year. The results illustrated in figure 9.4
are very clear. Retention performance was dramatically affected by the
length of the retention interval, with one year of no practice both producing
the least skilled performance on the first retention trial and returning this
group essentially to the level at which they started on the first day. This
decrement required the largest number of trials (as compared to the other
groups) to regain the criterion of two errorless trials. It is of interest to note
here that the rate of improvement after one year of no practice was
somewhat larger than it was in original practice, suggesting that perhaps not
all of the skill had been lost during this retention interval.
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Figure 9.4 Forgetting of a discrete task (learning light switch combinations)
occurred rapidly, and more additional practice trials were needed to
reacquire the original criterion as the retention interval increased.
Reprinted by permission from Neumann and Ammons 1957.
Now, contrast results from Neumann and Ammons study (figure 9.4) with
the findings reported by Fleishman and Parker (1962), presented in figure
9.5. The task required production of complex tracking movements involving
movement of the hands in the X (left-right) and Y (forward-backward)
dimensions and movement of the feet in the X-dimension, using an aircraft-
type stick and rudder controls. Retention tests were performed after nine
months or one or two years. As illustrated in figure 9.5, the retention loss
was remarkably small, even after two years of no practice. It is clear that
the type of task influences retention performance.
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Figure 9.5 Results from Fleishman and Parker (1962). Motor skill tracking
performance was retained well for periods of up to two years following
original practice.
Reprinted by permission from Fleishman and Parker 1962.
What does the difference in the results of these two studies mean? We argue
that long-term retention depends largely on the nature of the task—discrete
tasks (especially those with a relatively large cognitive component such as
the one used in Neumann and Ammons’ study) are forgotten relatively
quickly. On the other hand, continuous tasks, exemplified by the task in the
Fleishman–Parker study, are retained very well over long periods of no
practice. Of course, the amount of original practice will have much to say
about the relative amount of retention for these tasks (e.g., Ammons et al.,
1958). But, in general, continuous tasks, like riding a bicycle, are retained
for much longer periods of time than are discrete tasks.
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Some skills are retained, even with no practice, over long periods of time.
(Contrary to what many people believe, this is not a photo of the second
author.)
Warm-Up Decrement
The initial depression in motor activity at the very start of a performance
represents what many researchers believe to be a different kind of retention
deficit (Adams, 1961). Many examples exist. A musician may have trouble
getting into the proper rhythm or mood to support optimal performance; a
hockey goalie who is substituted late in the game may not be mentally
prepared for the speed of play. In these cases the learner typically suffers a
relatively large performance decrement and is temporarily prevented from
performing at his maximum potential. This disturbance to performance is
eliminated quickly once a few trials of practice are experienced—
essentially as with the 20 min retention-interval condition in Neumann and
Ammons’ study (figure 9.4). Still, the facts that these decrements to
performance are relatively large, and that they appear reliably in so many
different tasks and under so many different circumstances, have earned this
research area its very own term: warm-up decrement.
Note that the term “warm-up” is not used here in the same sense as
“warming up” physiologically to run a race, for example. Rather, warm-up
decrement is considered a psychological factor that is brought on by the
passage of time away from a task and is eliminated when the performer
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begins again to perform a few trials. Many consider warm-up decrement the
result of a loss of the “set” (a kind of tuning, or adjustment process not
related to memory for the task itself) that facilitates performance before the
retention break, and that, having been lost over the retention interval, now
prevents a return to maximum performance potential for a short period of
time. These decrements, being relatively large, have a large role in tasks for
which the performer must respond as well as possible on the very first
attempt after a layoff of a few minutes (e.g., free-throw shots in basketball).
The term “set,” when referring to warm-up decrement, has very specific
meaning (Adams, 1961). According to researchers, set is a collection of
psychological activities, states, or adjustment and processes (for example,
the target of attentional focus, one’s perceptual focus, and postural
adjustments). These processes are appropriate for, and support performance
while an activity is ongoing but are “lost” when a different set is adopted to
support the activities undertaken during rest (e.g., a set of adjustments
appropriate for resting).
One explanation for the occurrence of warm-up decrement is that it simply
represents a type of motor forgetting. That explanation has not received
much support from researchers, however, because so-called set-reinstating
activities that do not influence memory for the task, undertaken near the end
of the retention interval, have been shown to reduce or eliminate warm-up
decrement. In contrast, activities (such as maximum-grip-force tests) that are
deliberately designed to interfere with set for a task (such as delicate
positioning of a lever on a trackway) produce increases in warm-up
decrement (Nacson & Schmidt, 1971). These findings seem to argue against
the idea that warm-up decrement is simply a form of memory loss
(forgetting) in the main task.
Reinstatement of set before undertaking performance can eliminate much of
the warm-up decrement that accrues over a retention interval, even a brief
one. This finding likely helps to explain some of the benefit that occurs due
to the preshot routine seen in many types of sporting events (e.g., golf shots,
basketball free throws) when an athlete performs a highly individualized set
of behaviors just before the “real” action. These may involve bouncing the
basketball a certain way or following a set of procedures before hitting a
golf ball. But most high-level athletes do these activities in a similar way
each time. Successful performance has much to do with these preshot
routines, and research suggests that overcoming the negative effects of
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warm-up decrement is part of the reason for this success (Boutcher &
Crews, 1987).
Skill Transfer
Transfer, which is sometimes called generalization, is an important goal of
practice. It refers to the idea that learning acquired during practice of a
given task can be applied to, or transferred to, other task situations. An
instructor cannot be satisfied if the students can perform only those task
variations they have specifically practiced. The instructor wants them to be
able to generalize specific learning to the many novel variations they will
face in the future (see Focus on Application 9.3). The concern is how to
organize practice to maximize generalization.
Focus on Application 9.3
Teaching for Transfer of Learning
The general idea of “teaching for transfer” involves not only
maximizing transfer from earlier learning but also selecting those
methods and ways of organizing practice that maximize transfer and
generalizability. This focus section considers several ways in which
instructors can enhance generalization through practice organization
and feedback.
Point Out Similarities (or Differences) Among Skills
Recall from earlier in the chapter that Fitts’ first stage of learning is
highly cognitive and verbal. The instructor can use this knowledge
to advantage by pointing out to the learner that a particular skill
being practiced for the first time is similar to another one learned
earlier. For example, a surgical student learning to stitch an open
wound can be reminded that these actions are similar to sewing
together two pieces of cloth or two peach skins.
Pointing out fundamental differences can be useful too. An example
is explaining the difference between a flop shot and a chip shot in
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golf—the former is like trying to score an underhanded shot through
a basketball hoop (high arc, little roll) while the latter is like
bowling a ball toward a target (low arc, considerable roll).
Borrowing details of an action pattern from earlier learning gives
the student an advantage in understanding how to perform a new
skill, particularly in early learning.
Use Verbal Cues to Emphasize Transfer
Similarities can also be emphasized by various teaching cues. In
gymnastics it is helpful to use consistent labels for skills. For
example, it is a good idea to emphasize the fact that kips on the
horizontal bar, on the rings, and on the parallel bars are really the
“same” skill and to refer to them all as “kips.” Also, many skills
have similar mechanical principles, such as shifting momentum
when beginning to walk or run or needing a wider base of support
for maximum balance when walking on ice. Such cues are used
commonly by physical therapists in rehabilitation, for example.
Emphasize Transfer to Future Skills
It is tempting to think of practice as contributing only to the skill that
the learner is attempting at the moment. It works the other way, too.
In the practice of a given skill, ask the learner how to apply a
particular strategy or concept to this new setting. Some methods,
such as variable practice (discussed in chapter 10), have this
characteristic as a goal—today’s specific techniques being at least
partly directed to future generalizability. A physical therapist may
ask the patient to get up out of a chair using as many different types
and styles of chairs or actions as possible. The goal here is for the
learner to acquire fundamental strategies that will transfer to new,
not previously encountered chairs in the future.
What Skills Will Transfer?
Recall from chapter 8 that transfer is defined as the gain or loss in the
capability to perform one task as a result of practice or experience on
another task. Transfer is task positive if it enhances performance in the other
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skill, negative if it degrades it, and zero if it has no effect at all. The issues
surrounding transfer, and particularly maximizing transfer by adjusting
teaching methods and styles, are far-ranging and need to be discussed
briefly here.
Transfer and Similarity
An old idea in psychology and motor learning is that transfer of learning
between two tasks increases as the “similarity” between them increases.
One idea was that of identical elements (Thorndike & Woodworth, 1901),
according to which learning certain elements in one situation transferred to
another skill because the second skill used the same elements. As simple as
this idea might sound, there have always been problems with it. One is that
the concepts of “similarity” and “identical elements” are never explicitly
defined. For example, is throwing a baseball more similar to passing a
football than shooting a basketball? What exactly are the elements that are
involved? This particular view of “similarity” has not been taken very
seriously as a result. However, other views of similarity and transfer have
been supported by research.
Fundamental Movement Patterning
Many have suggested that the so-called overarm pattern underlies throwing
a baseball, serving in tennis, spiking a volleyball, and many other actions
requiring forceful overarm movements to strike or throw an object. All
these involve rotation of the hips and shoulders and ballistic actions of the
shoulder–arm–wrist, ending finally with wrist–hand action to accomplish
the particular goal. An analogous idea common among gymnasts is that
certain fundamental actions (e.g., the sharp hip extension in a kip) can be
applied to many apparatus events. In both these examples, if practice is
given at one variant of the class of movements sharing the same general
pattern, then the learner should be able to transfer the learning to any other
variant using this same pattern. Of course, practicing a kipping action would
not transfer to an overarm action or vice versa, because these skills use very
different patterns, belonging as they must to separate movement classes.
Perceptual Elements
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“Similarity” is also evident in the numerous perceptual elements underlying
many tasks. For example, learning to intercept flying balls of various kinds
(baseballs, footballs, tennis balls, and so on) depends on learning the
common features of ball flight, which are based on the principles of physics.
In a similar way, police trainees must be attuned to the perceptual cues that
alert them to a dangerous situation. Learning to react appropriately to such
cues in one situation facilitates transfer to other situations in which the
perceptual elements are similar.
Strategic and Conceptual Similarities
Similar strategies, rules, guidelines, or concepts are present in many
different activities. For example, driving behaviors, signs, traffic lights, and
general “rules of the road” are common within a restricted population or
community, which facilitates driving performance when you travel to parts
of the country that are new to you. However, one would expect much less
transfer, or perhaps even some negative transfer, when the rules of the road
differ dramatically, as when tourists drive on the opposite side of the road
in a foreign country (e.g., Americans driving in Australia).
Overall, the concept of “similarity” among skills involves several classes
of common features:
Common movement patterning
Common perceptual elements
Common strategic or conceptual elements
Motor Transfer as Learning Progresses
The transfer principles just discussed apply best when the learner is just
beginning to learn a skill. An important extension of Henry’s specificity
principle, discussed in previous chapters, that the amount of transfer from
earlier learning should drop markedly when the learner becomes more
highly skilled in the to-be-transferred-to skill. This decrease in transfer
occurs because, with continued practice and increased capability, a skill
becomes more specific (see chapter 7 for a review) and shares less with
other skills of the same movement type (Henry, 1968). In early practice an
overarm throw and a tennis serve do seem similar, and relating them might
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help the novice get the idea critical to initial attempts. However, a tennis
serve and an overarm throw are not the same thing, and at higher levels of
proficiency the two skills become more distinct. What, then, are the
principles of transfer for later stages of learning?
Motor Transfer Is Small
Between two reasonably well-learned tasks that merely appear somewhat
similar, there is usually very little transfer. The transfer that does appear is
usually low-positive, the skills generally facilitating each other to some
small extent. But the amount of transfer is generally so low that transfer
ceases to be a major goal of practice; this is in contrast to the situation in the
earliest practice stages, where transfer was a major goal. Therefore,
teaching a particular skill A (which is not of major interest), simply because
you would like it to transfer to skill B (which is of major interest), is not
very effective, especially when one considers the time spent on skill A that
could have been spent on skill B instead. Transfer is fine when received
“for free” in early practice, but it usually requires too much time in later
practice.
The principle just mentioned also applies to using various “lead-up
activities.” These actions are usually not of interest in themselves but are
considered only as means to another goal—the transfer to another skill. For
example, learning to suture wounds by starting with grapes is a cost-
effective lead-up activity to working with more realistic simulators or
patients. In general, however, learning such preliminary activities tends to
transfer to the degree that they are effectively “similar” to the goal
conditions, which we will discuss in more detail shortly.
No Transfer of Basic Abilities
A common misconception is that fundamental abilities (see chapter 7) can
be trained through various drills or other activities. The thinking is that,
with some stronger ability, the learner will see gains in performance for
tasks having this underlying ability. For example, athletes are often given
various “quickening” exercises, with the hope that these exercises will train
some fundamental ability to be quick, allowing quicker responses in their
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particular sports. Coaches and physical therapists often use various
balancing drills with the goal of increasing general balancing ability; eye
movement exercises are used with the goal of improving general visual
abilities; and there are many other examples. Such attempts to train
fundamental abilities may sound fine, but usually they simply do not work
(e.g., Abernethy & Wood, 2001; Lindeburg, 1949). Resources (time, money)
would be better spent practicing the eventual goal skills.
There are two correct ways to think of these principles. First, there is no
general ability to be quick, to balance, or to use vision, as discussed in
chapter 7 on individual differences. Rather, quickness, balance, and vision
in various tasks are each based on many diverse abilities, so there is no
single quickness ability, for example, even if it could be trained. Second,
even if there were such general abilities, these are, by definition, essentially
genetically-determined and are not subject to modification through practice.
Therefore, attempts to modify an ability with a nonspecific drill are usually
ineffective. A learner may acquire additional skill at the drill (which is,
after all, a skill in itself), but this learning does not transfer to the main
ability of interest.
Transfer of Part Practice to Whole Performance
Some skills are enormously complex, such as playing a musical instrument
and performing a gymnast’s routine. Clearly, in such situations the instructor
cannot present all aspects of the skill at once for practice because the
student would be overwhelmed and would likely grasp almost none of it. A
frequent approach is to divide the task into meaningful units that can be
isolated for separate part practice. The goal is to integrate these practiced
units into the whole skill for later performance. This is not as simple as it
may sound because there are several factors that make integrating the
learned units back into the whole skill somewhat difficult.
The question is how to create subunits of skills and how they can be
practiced for maximum transfer to the whole skill. It is a simple matter to
divide skills into parts. You could separate a gymnastics routine into the
component stunts; you could divide the left and right hands of piano practice
into separate components for practice. And each subpart could be divided
even further. But the real question is whether these parts, practiced in
isolation, will be effective for learning the whole skill, which is the overall
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goal. Thus, part practice is based on the transfer-of-learning principles
defined earlier: Will the subunit transfer to the whole task that contains it?
How much (if any) time should be spent on part practice, and would this
time be more effectively spent practicing the whole task?
At first glance the answers to these questions seem obvious. Because the
part of the task practiced in isolation seems the same as that part in the
whole task, the transfer from the part to the whole task would seem to be
almost perfect. This may be so in certain cases, but there are many other
situations in which transfer is far from perfect. These differences in part-
practice effectiveness depend on the nature of the skill.
Serial Skills of Long Duration
In many serial skills, the learner’s problem is to organize a set of activities
into the proper order, as with the gymnast who assembles a routine of stunts.
Practicing the specific subtasks is usually effective in transferring to whole
sequences. Part transfer works best in serial tasks of very long duration and
in cases in which the actions (or errors) of one part do not influence
markedly the actions of the next part. That is, part practice is most effective
for skills in which the parts are performed relatively independently. The
learner can devote more practice time to the troublesome parts without
practicing the easier elements, making practice time more efficient.
However, in many serial skills in sport, performance on one part frequently
determines the movement that must be made on the next part. If the ski racer
comes out of a turn too low and fast, this affects the approach for the next
turn. Small positioning errors on the beam in one move determine how the
gymnast must perform the next one. If a part-to-part interaction is large, as it
might be if the sequence is run off quickly, modifying a given action as a
function of performance on a previous action is an important component of
the skill. However, these interactions between parts of the whole skill
cannot be practiced and learned in isolated part practice; whole practice is
necessary. The gymnast might be able to do all of the individual stunts in her
routine, but she still might not be able to perform an effective routine in a
meet because she never learned to modify each component movement based
on the previous one.
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A gymnast putting together a routine (here, Richard Schmidt, the first author,
a long time ago) must become skilled at performing the routine as a whole,
since each movement must be modified in response to the previous
movement.
Discrete Skills of Short Duration
Any skill is, in some sense, serial because certain pieces of it come before
other pieces, such as hitting a baseball, which contains step, hip turn, and
swing elements. At some point, though, these individual parts, when viewed
separately, cease to be parts of the whole skill. Dividing a golf swing into
smaller and smaller arbitrary parts destroys a critical aspect that allows the
parts to be characterized as a component of a swing; that is, the division
seems to disrupt the essential features of the action. Practice at these
subparts could be ineffective, even detrimental, to learning the whole task
(see Lersten, 1968).
Several experiments suggest that practicing parts of a discrete task in
isolation transfers little if at all to the whole task (e.g., Lersten, 1968;
Schmidt & Young, 1987), especially if the task is rapid and ballistic. This is
probably related to the fact that the components in rapid tasks usually
interact strongly, which means less effective transfer. In fact, transfer from
the part to the whole can even be negative in certain cases, so practicing the
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part in isolation could be worse for the whole task than not practicing at all!
This evidence suggests that when very rapid skills are broken down into
arbitrary parts, these parts become changed from the “same” parts in the
whole task so that the part practice contributes very little to the whole. In
tasks like hitting a golf ball, for example, practicing the backswing
separately from the downswing changes the dynamics of the action at the top
of the backswing, which is dominated by actively lengthening muscles
whose spring-like properties allow the downswing to be smooth and
powerful. Therefore, practicing the backswing in isolation, which
eliminates the role of these spring-like muscle properties, is quite different
from performing the same backswing in the context of the whole skill.
Motor Programs and the Specificity Principle
According to the motor program concept introduced earlier, quick actions
are controlled essentially open loop, with the decisions about the action’s
structure programmed in advance. Performing only a part of this action in
part practice, particularly if the part has different dynamics (e.g., in the golf
swing) when performed in isolation, requires using a different program, or
one that is responsible for only the part in isolation. Practicing such a part
program contributes to performance of the part in isolation, but it will
probably not contribute to production of the whole movement, which could
be based on a different motor program. Thus, in part practice the learner
develops two separate movement programs—one for the part and one for
the whole task. This is consistent with Henry’s (1968) specificity view of
movement learning, in which isolating a part and changing it slightly to
practice it separately shifts the underlying abilities to the point that it is no
longer related to the original part in context of the whole skill.
Progressive Part Practice
Even with very rapid actions, though, some part practice might be helpful,
particularly if the action’s elements are many in number and provide initial
difficulty for the learner to sequence them properly. Very early part practice
might be beneficial in this case. However, to minimize the problems of
learning actions that do not transfer to the whole, many instructors use
progressive part practice. In this method the parts of a complex skill are
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presented separately, but the parts are integrated into larger and larger parts,
and finally into the whole, as soon as they are acquired.
The principles of part practice can be easily summarized:
For very slow, serial tasks with no component interaction, part
practice on the difficult elements is very efficient.
For very brief, programmed actions, practice on the parts in isolation
is seldom useful and can even be detrimental to learning.
The more the components of a task interact with each other, the less the
effectiveness of part practice.
Simulation and Transfer
Transfer principles are commonly used in the area of simulation. A
simulator is a practice device designed to mimic features of a real-world
task. Simulators are often very elaborate, sophisticated, and expensive, such
as devices to train pilots to fly aircraft (figure 9.6). But simulators need not
be elaborate at all, such as wireless video game consoles (see Focus on
Research 9.2). Simulators can be an important part of an instructional
program, especially when the skill is expensive or dangerous (e.g., learning
to fly a jetliner), where facilities are limited (e.g., cycling on a treadmill
instead of in a velodrome), or where real practice is not feasible (e.g., using
artificial patients rather than real humans for surgery practice).
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Figure 9.6 An aircraft cockpit simulator.
Focus on Research 9.2
Game Systems for Virtual Training
One of the more recent advances in transfer-of-training research
employs a common commercial product—gaming consoles, such as
the popular Nintendo Wii system (figure 9.7). The advantages in
using these systems are many, such as the relatively low price
(specially created hardware and software research tools typically
cost many tens of thousands of dollars, while game systems are
available for a few hundred or less), the sophistication of the
computing technology, and the intrinsic motivation to practice and
learn that these gaming systems elicit (Levac, Rivard, & Missiuna,
2012).
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Figure 9.7 Video gaming systems engage people of all ages in
performing activities and learning new motor skills.
In many ways, these gaming systems are nothing more or less than
low-cost commercial simulators, although they were created for
entertainment value rather than with a specific, transfer goal in
mind. However, researchers have begun to use these highly
motivating gaming systems to enhance specific end goals, such as
physical rehabilitation for neurologically-challenged children
(Levac et al., 2010) and adults who have had a stroke (Hijmans et
al., 2011), as well as cognitive intervention for individuals with
Alzheimer’s disease (Fenney & Lee, 2010).
There is no doubt about the entertainment value of these gaming
systems. However, their functional value, measured in terms of
transfer of training, still remains to be assessed. To do so, we would
use the same methods of evaluation as those applied to
noncommercial simulators and training devices.
Exploring Further
1. Describe how a specific popular interactive game might be
used in a poststroke rehabilitation setting.
2. Using the rationale presented in figure 9.8 (and described in
the text), explain how you would assess the value of the gaming
intervention described for question 1.
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Evaluating Simulator Effectiveness
Of course, a simulator must provide positive results in order to justify its
use. Therefore, the amount of transfer resulting from the time spent in a
simulator is an important consideration in determining its effectiveness and
efficiency. Consider figure 9.8, showing the hypothetical performance
curves on a novel motor learning task for two groups of subjects. The
simulator group begins practice on the criterion task after having 3 h of
practice on a simulator task, designed to provide positive transfer to the
criterion task. The no-simulator group receives no prior practice on the
simulator.
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Figure 9.8 Hypothetical performance curves of two groups of learners on a
novel motor learning task. The simulator group practiced a simulation task
for 3 h before initial practice on the criterion task, whereas the no-simulator
group did not.
Figure 9.8 illustrates performance of both groups on the criterion task. Note
that the point on the axis corresponding to 0 h of practice refers to the first
trial on the criterion task for both groups (this point occurs after 3 h of
practice on the simulator task for the simulator group). From figure 9.8 we
can see that there is considerable positive transfer from the simulator to the
criterion task, seen as the gain in probability of success from .30 to .50 (the
difference labeled “A” in figure 9.8). Now look at the difference labeled
“B” in figure 9.8. This difference suggests that the simulator group started at
a level (.50) that took the no-simulator group 1.5 h of practice on the
criterion task to achieve. So, in some respects, the simulator experience
“saved” about 1.5 h of practice on the criterion task.
However, there is another way to look at this result. Remember that the
simulator group had already spent 3 h of practice on the simulator. Since the
simulator group spent 3 h of practice on the simulator but the no-simulator
group “caught up” in 1.5 h, the simulation actually cost 1.5 h of real
(sometimes very expensive) simulator time. Viewed in this way, simulator
was not effective at all in reducing the time of training.
Time is not the only relevant factor here, though. The effectiveness of a
simulator sometimes must also be judged in relation to the relative financial
costs of simulator practice and criterion task practice, the availability of
resources and facilities, safety, and so on. Relative to practice cost in a
flight simulator, practice cost in an actual jetliner would be staggering, and
there are obvious concerns for the safety of people, equipment, and so on.
Thus, the evaluation of simulators in an instructional setting can be
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complicated, and it must take into account a number of important factors in
making decisions about their use and effectiveness.
Physical Versus Psychological Fidelity
Remember that the overall goal of simulation is for the learning in the
simulator to transfer to the criterion task. Scientists who conduct research in
this area refer to the “quality” of the simulation in terms of fidelity—the
degree to which the simulator mimics, or is “faithful to” the criterion task.
But two different types of fidelity constructs have emerged in the literature.
Physical fidelity refers to the degree to which the physical or surface
features of the simulation and criterion tasks themselves are identical. In
contrast, psychological fidelity refers the degree to which the behaviors and
processes produced in the simulator replicate those required by the criterion
task. Although these seem like similar constructs, in fact they are quite
different and have the potential to result in quite different effects on transfer
(e.g., Kozlowzki & DeShon, 2004).
Because transfer is expected to increase with task similarity, this idea has
naturally led to the notion that physical fidelity should be as high as
possible. Aircraft-cockpit simulators (figure 9.6, for example) replicate the
cockpit of a real aircraft very closely (although doing so is often very
expensive). Another example is cardiorespiratory resuscitation (CPR)
mannequins, which are designed to be as anatomically correct as possible,
for the purpose of training lifesaving skills. Physical fidelity refers to the
degree to which the simulator replicates the physical features of the
criterion task—possessing as much of the look, sound, and feel of the
criterion task as possible.
Psychological fidelity is less concerned with the physical similarity
between the simulator and criterion tasks and more concerned with the
target skills and behaviors required to perform the criterion task. In the case
of CPR mannequins, for example, simulator training might emphasize the
perceptual and decision-making processes that are presented in an
emergency situation, under high levels of stress, and perhaps under
environmental challenges (e.g., extreme heat or cold). Psychological fidelity
is concerned with training the skills that will be required of the end user in
the criterion task.
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Physical and psychological fidelity should be seen as complementary, not
competing goals, according to Kozlowzki and DeShon (2004). Still,
situations can arise in which too much faith is placed in the physical fidelity,
without enough attention devoted to the psychological processes. Returning
to our CPR example, some mannequins provide very exacting physical
fidelity—the sights, sounds, and proprioceptive feedback that would appear
to promote excellent perceptual and motor skill transfer. Yet just using the
mannequin without considering how to structure the practice conditions and
how to provide augmented feedback would be a major mistake, as the
behaviors practiced during training would ultimately be expected to affect
transfer to real emergency situations.
These issues of training motor skill behaviors are the major focus of the next
two chapters. As we will see, how practice is structured and how feedback
is augmented during practice determine the quality of motor learning that
results from practice.
Summary
Motor learning is a fundamental necessity that we often take for granted, yet
it is required for just about every facet of our daily existence. As people
practice, they generally pass through stages of learning that describe the
current state of their skill proficiency. Although some debate exists
concerning how to best characterize these stages, practice results in some
basic principles regarding how new motor skills are acquired and a specific
set of benefits that result.
But periods of no practice are also a fact of life, and the retention of learned
skills after a lengthy time away from them represents a critical area of
research. Retention of skills is affected greatly by their classification;
continuous skills are generally retained much more completely, and for
longer periods of time, than discrete skills. Warm-up decrement refers to a
specific type of retention deficit due to the loss of an activity set. Being able
to perform a learned activity in a new situation concerns the issue of skill
transfer. Simulators of various kinds can efficiently mimic important
elements of a skill when practicing the actual skill would be too costly,
dangerous, or impractical.
Web Study Guide Activities
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The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 9.1: Indicate whether a given understanding of a stage of
learning fits with Fitts’ or Bernstein’s model of skill acquisition.
Activity 9.2: Distinguish the goals of practice and test sessions by
indicating whether each in a series of statements applies to practice or
testing.
Activity 9.3: Review the terminology of skill acquisition, retention,
and transfer through a matching exercise.
Principles-to-Application Exercise
Activity 9.4: The principles-to-application exercise for this chapter
prompts you to choose a movement skill, identify the goal of practice
for this skill, and explore additional ways that practice might affect
performance of this skill.
Check Your Understanding
1. Contrast Fitts’ and Bernstein’s stages of learning. Name each stage and
provide a brief description. Name one limitation of each of these
perspectives.
2. Define warm-up decrement and explain how its effects can be reduced.
3. Distinguish between performance and learning during practice. How
can they have conflicting goals, and how might this be overcome?
Apply Your Knowledge
1. List four benefits of practice discussed in this chapter and provide an
example of how each might be illustrated by a karate student and a
truck driver.
2. Your neighbor tells you that he will be learning to lead climb at a local
rock-climbing gym. He tells about how lead climbing can be difficult
because there are many parts to placing the safety gear properly while
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http://www.HumanKinetics.com/MotorLearningAndPerformance

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30153

http://courses.humankinetics.com/shell.cfm?siteCourseID=655&pageid=30154

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climbing and the decisions made can influence the next movement.
From what he tells you about the lessons, it seems like the instructor
will be using progressive part practice. Why might the instructor have
chosen this method? What alternatives would the instructor likely have
considered, and why were these not chosen?
Suggestions for Further Reading
Specificity of learning, which remains an enduring topic in motor skills,
was reviewed from different perspectives by Marteniuk (1974) and later by
Proteau (1992). The development of expertise was the topic of a series of
chapters in a volume edited by Starkes and Allard (1993). Differing views
on stages of learning were explored by Anson, Elliott, and Davids (2005).
And a range of topics on transfer of learning was reviewed by various
authors in the book edited by Cormier and Hagman (1987). See the
reference list for these additional resources.
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Chapter 10
Organizing and Scheduling
Practice
How the Structure of Practice
Influences Learning
Chapter Outline
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Off-Task Practice Considerations
Organizing Practice and Rest
Variable Versus Constant Practice
Blocked Versus Random Practice
Summary
Chapter Objectives
Chapter 10 describes the influence of the ways in which practice is
structured and various conditions under which practice is
conducted. This chapter will help you to understand
key factors that occur while undertaking physical practice,
basic concepts regarding the nature of practice,
practice schedule organizations and their impact on
performance and learning, and
the role of practice variability in motor learning.
Key Terms
blocked practice
constant practice
demonstration
distributed practice
elaboration hypothesis
forgetting hypothesis
goal setting
massed practice
mental practice
modeling
observational learning
random (interleaved) practice
schema theory
self-regulation
variable practice
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It comes as little surprise that increasing practice time, or increasing the
efficiency of a given amount of practice time, is a major goal of instructors
interested in practice effectiveness. Practice time is not the only factor,
however; the quality of practice must also be considered. Thus, it is
important to structure or organize a given amount of practice to maximize its
effectiveness.
Imagine you are in charge of designing a plan for teaching a group of
learners a particular set of skills. They may be prospective chiropractors
learning different manipulation techniques, or a high school woodworking
class, or perhaps a physical education class learning a set of tumbling
exercises. Or perhaps there is no teacher involved at all, and you, the
learner, are wondering how best to structure your music practice. How
would you organize your time? How would you schedule physical practice
and rest? In what order will you practice various skills, how much variation
in skills would you introduce into your practice, and how much practice
will you allow on one task variation before moving to the next? Questions
like these affect how you would plan instruction, and this chapter presents
the principles that help you solve these problems concerning how and when
to practice.
Practice, or various kinds of experience, at particular skills is a broad
concept, difficult to specify precisely. Practice can occur at many different
times and places, under varying conditions, and it can be either almost
unintentional or highly guided and structured. In experiments, many features
of practice settings can be varied systematically, and these factors have
been found to make practice more or less effective; many of these are under
the direct control of the instructor. Of course, being armed as you are with
principles of movement performance and learning will facilitate such
decisions, equipping you to make wise choices about structuring practice to
produce the most effective outcomes—usually the maximization of learning.
Off-Task Practice Considerations
Several important aspects of the practice setting occur very early in the
process. In fact, many of these can occur in the absence of any physical
practice at all (although they are generally more effective if they are
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interspersed with physical practice, as we discuss later). These
considerations include concerns regarding motivation for learning,
instructions, demonstrations, and mental practice (and imagery).
Motivation
Instructors often have the impression that the learner’s motivation is not a
problem—that a student would obviously want to learn a particular skill.
However, students do not always share their instructors’ enthusiasm for
learning. An unmotivated learner is not likely to practice, and the result can
be little or no learning. The motivated student devotes greater effort to the
task, with more serious practice and longer practice periods, leading to
more effective learning. How can instructors influence this motivation to
learn?
Intrinsic motivation for learning concerns the learner’s internalized drive
—here, a drive to learn a skill. Considerable research has been conducted
to understand how intrinsic motivation affects the learner in a wide variety
of situations and skills, and has resulted in important advances in theory
(e.g., Deci & Ryan, 2000) and application (e.g., Weinberg & Gould, 2011).
For our purposes, however, we consider specifically the tools and
techniques that may influence a learner’s intrinsic motivation.
Deci and Ryan (2000) suggest that an individual’s intrinsic motivation is
largely determined by three basic needs: autonomy (control of one’s own
destiny), competence (skill mastery), and relatedness (being accepted
within a social context). Of course, the relative weighting of each of these
basic needs differs in every individual. Therefore, as an instructor,
becoming familiar with the individual, and understanding how the
acquisition of a motor skill fits into his needs, goes a long way in
determining how best to respond in a learning context. The following
sections discuss how motor learning may be affected by specific factors that
influence motivation.
Goal Setting
An important motivational method is goal setting, whereby learners are
encouraged to adopt specific performance goals. This method has had
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numerous applications, particularly in industry, and it has strong
implications for learning in sport and physical education (Locke & Latham,
1985). In one experiment, Boyce (1992), either set specific goals or
instructed participants to do so, or simply told participants to “do your
best.” Performance over a three-week practice period in which these goal-
setting methods were applied is presented in figure 10.1, along with the
results of a pretest (done before the goal setting was applied) and in a
retention test, where the goal-setting instructions were no longer in force.
The findings clearly showed that adopting a specific goal improved
performance compared to the “do-your-best” group. Moreover, this effect
was maintained in a retention test. This is one of the few studies to
demonstrate effects of goal-setting instructions on both performance (i.e.,
during practice) and learning (i.e., on a test of retention—see chapter 8 for a
discussion of the use of retention tests to distinguish between performance
and learning).
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Figure 10.1 Results of the Boyce (1992) study in which learners practiced a
shooting task after different goal-setting assignments.
Adapted by permission from Boyce 1992.
These results suggest that instructors encourage their learners to set realistic
goals, ones that can be reasonably achieved with practice and effort. The
learner can become discouraged by not even approaching goal levels that
are too high. Yet goals that are too easily met can result in boredom and
reduced motivation. Being encouraged to commit oneself to a specific,
“challenging” (but not impossible) goal is strongly motivating and has
positive benefits on performance and learning.
Augmented Feedback
Although augmented feedback—information that is provided to the learner
from an external source—is the focus of the entire next chapter, we
recognize here how it serves an important goal as a motivator. Lewthwaite
and Wulf (2012) have recently reviewed a rapidly growing body of
evidence suggesting that positive augmented feedback can provide a boost
to motor learning, even if the feedback is not entirely true.
For example, in one study by Lewthwaite and Wulf (2010, see figure 10.2),
using a balance task, subjects in one group (red symbols in figure 10.2)
were told that their performance was 20% more accurate than the average
performance of others who had participated in the experiment (termed
“false-positive-normative feedback”). The performance of this group was
compared to that of another group (blue symbols) given false-negative-
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normative feedback—they were told that their performance was 20% less
accurate than average. A third (control) group (green symbols) was
provided only their results, with no mention of the subjects’ normative
standing. The results of these feedback conditions are shown in figure 10.2,
which illustrates clear benefits for the false-positive-normative feedback
group throughout practice and in retention. Interestingly, there were no
(significant) differences between the false-negative group and the control
group, suggesting that the normative feedback provided a boost to learning
when positive, but did not degrade learning reliably when it was negative.
These and other studies of the effects of feedback on motivation are
discussed in detail by Lewthwaite and Wulf (2012) and are revisited in the
next chapter.
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Figure 10.2 Results of the Lewthwaite and Wulf (2010) study, using a
balance task. One group received false-positive feedback about their
performance (red), another received false-negative feedback (blue), and one
received only true feedback (green). RMSE = root-mean-square error (see
chapter 1).
Reprinted by permission from Lewthwaite and Wulf 2010
Self-Regulation of Practice
Providing some control over the learning environment is another factor
thought to influence motivation and enhance learning. Researchers call this
“self-regulation”; it refers to giving learners “ownership” over some of the
components of practice. In studies of this type, learners are typically told
that they can control how much practice to undertake, when augmented
feedback will be provided, or how to organize the practice schedule
(reviewed by Sanli et al., 2012). An important component of these studies is
the inclusion of (yoked) control groups that provide the same schedule of
feedback delivery as the self-selected group. However, these yoked
conditions are determined entirely in advance and are not under the control
of the learner. These studies have revealed more learning under self-
regulated feedback conditions, leading some to speculate that giving
learners control over their learning environment provides an extra incentive
to learn. This seems to satisfy the need for autonomy, as suggested in Deci
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and Ryan’s (2000) self-determination theory (see Lewthwaite & Wulf,
2012).
Instructions
Giving instructions is a feature of nearly every teaching setting. Instructions
are usually spoken (although they can be written or demonstrated), and they
provide information about the very first aspects of the skill. Tips on where
and how to stand, how to hold the apparatus or other implement, what to
look at, and what to do are frequently parts of typical instructions.
Information about what is likely to happen also helps, such as a statement
like “If you make this (chiropractic) manipulation correctly, you should feel
this happen.” Considering the difficulty students have with no instructions at
all, these procedures are critical for raising skill level in very early
practice. Simple, direct statements that start people off on the right track can
be effective in reducing early confusion in the learning process. Effective
instructors often even begin a demonstration by simply saying “Do this.”
Instructions can be overdone, though. One problem is that words are a
relatively crude, imprecise way of describing the subtle aspects of
movements, so verbal descriptions are probably best suited only for the
most elementary features. Try describing how to tie a shoelace in words,
and the problem will be obvious. Along similar lines, many advocate
explaining skills in terms of their biomechanical, or physical, bases, using
concepts such as transfer of momentum and action–reaction forces. Some of
this may be useful, but it assumes that learners understand the physical
principles well enough to apply them to the new skills.
Two examples from motor learning research in which instructions have been
determined to play a particularly strong role are discussed next. One of
these concerns a largely verbal form of instruction (directing attentional
focus); the other is mostly nonverbal (demonstrations and modeling).
Directing Attentional Focus
Earlier, in chapter 3, we discussed the effects on performance of directing a
performer’s attentional focus through verbal instructions. For most
performers (perhaps with the exception of beginners), instructing them to
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pay attention to the intended result of an action (an “external focus”)
produces more skilled performance than an instruction to pay attention to
aspects of the movement itself (an “internal focus”). This basic result has
been replicated for many different sport activities, such as golf, baseball,
basketball, and volleyball as well as other activities such as jumping and
balancing (see Lohse, Wulf, & Lewthwaite, 2012, for a review), and this
performance effect has also been extended to learners acquiring a new skill.
For example, groups of learners in a study by Wulf and colleagues (2003)
practiced a balancing task while standing on a “teeter-totter” type of
balance board (called a “stabilometer”). The balancing task was performed
while also holding a cylindrical tube in their hands. A control group (blue
trace in figure 10.3) of subjects was given no instructions about where to
direct their attentional focus while balancing. The other groups were
instructed to try to balance while keeping a specific focus—either to keep
their hands held horizontal (green trace, an internal focus) or to keep the
tube horizontal (red trace, an external focus). The results of these
instructions, in figure 10.3, show the reduction in balance error over two
days of practice and in tests of retention (where no attentional-focus
instructions were given) and transfer (where none of the subjects held the
tube). The tube/no-tube finding is particularly remarkable in that holding the
hands horizontal is almost exactly the same instruction as holding the tube
horizontal (because the hands hold the tube).
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Figure 10.3 Effects of attentional focus in practice over two days (trials 1-
14) and in retention and transfer tests. RMSE = root-mean-square error (see
chapter 1).
Reprinted by permission from Wulf et al. 2003.
The findings illustrated in figure 10.3 could be interpreted from two
perspectives: (1) that external attentional focus instructions facilitated
performance (during the practice trials) and learning (as measured in the
retention and transfer tests), or (2) that internal attentional focus instructions
degraded performance and learning (at least in retention). Or, perhaps the
findings indicate some combination of these facilitation and degradation
effects.
Demonstrations and Modeling
Good companions to prepractice instructions are various visual aids, such
as still pictures, video clips, and live demonstrations by an instructor or by
the learners themselves (sometimes called modeling). A clear advantage of
transmitting information this way is seen, probably because modeling is not
limited by words. This procedure comes under the general heading of
observational learning, in which the learner gains information by watching
another’s performance.
The modeling process has been studied by researchers quite intensely over
the past two decades. The result is a complex set of moderating variables
that influence the observational learning process. The decision about how to
maximize the effectiveness of a model seems to depend on a number of
factors, which are summarized by Ste-Marie et al., 2012. How
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observational learning works without active movement on the part of the
learner is a question that has raised plenty of debate. But there is little doubt
that a considerable amount of learning, particularly early in practice, comes
from studying and imitating others’ actions. Capitalizing on this in
instructional settings is a good procedure.
Mental Practice
One useful addition to the collection of activities in a practice session is to
ask the learner to rehearse skills to be learned mentally, without performing
actual, overt physical practice. In such mental practice the learner thinks
about the skills being learned, rehearses each of the steps sequentially, and
imagines doing the actions that would result in achieving the goal.
Can this method actually contribute to learning? For many years, scientists
and educators in the motor learning field had very much doubted that motor
learning could be accomplished through mental practice. The understanding
of practice and learning at the time held that overt physical action was
essential for learning. Most researchers thought that mental practice was
producing gains in the cognitive–conceptual aspects of the task, and it was
difficult to understand how any learning could occur without movement,
active practice, and feedback from the movement to signal errors.
However, evidence from various experiments has demonstrated
convincingly that mental practice procedures actually generate motor
learning. Although mental practice does not result in as much learning as the
same amount of physical practice, it does result in far more improvement
than in no-practice control groups (see Feltz & Landers, 1983, for a
review). Figure 10.4, from Hird and coauthors (1991), provides results
from two separate tasks, the pegboard and pursuit rotor tasks. The fact that
mental practice generates learning in the pursuit rotor (for example), which
does not seem to have much cognitive learning involved beyond the first
few trials, suggests strongly that the learning of motor control must be
involved with mental practice.
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Figure 10.4 Effects of physical (blue) and mental practice (green)
compared to a control, no-practice condition (red) on groups learning a
pegboard-insertion task (left) and a pursuit-rotor task (right).
Data from Hird et al. 1991.
How Does Mental Practice Work?
There are several views regarding how mental practice generates new task
learning. One idea is that mental practice facilitates the learning of “what to
do” (Heuer, 1985). For example, a tennis player could decide what shot to
take; a baseball player could think how to grip the bat; and a skier could
rehearse the sequence of turns in the ski run. These cognitive elements are
thought to be present only in the very early stages of learning (the cognitive
stage discussed earlier); thus, mental practice effects were predicted to
apply only to early learning as well. Although learning cognitive elements is
undoubtedly a major factor in mental practice, evidence such as that
illustrated in figure 10.4 (and in Focus on Application 10.1) suggests that
there is more to mental practice than just this. Beyond these early stages of
practice, both the pegboard-insertion task and the pursuit-rotor task involve
considerable motor control learning, as these tasks seem largely devoid of
cognitive or conceptual components. Rawlings and coauthors (1972) also
studied mental practice with the rotary-pursuit task, and their results were
very similar to those of Hird and colleagues. Clearly, mental practice is not
just cognitive or symbolic learning.
Another, older notion is that during mental practice, the motor system
produces minute contractions of the participating musculature, with these
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contractions being far smaller in amplitude than those necessary to produce
action. On this view, the “movement” is carried out in the central nervous
system, providing “practice” even without overt body movement. Although
EMGs (electromyograph recordings of the muscles’ electrical signals) do
show some evidence of weak activities during mental practice, the
patterning of these EMGs does not resemble that of the actual movements
very closely, making it difficult to understand how these electrical activities
alone could be the basis for enhanced learning. This idea that mental
practice produces minute muscular contractions has not generated much
research support.
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Mental practice does contribute to learning, though the exact way it does
this is still unclear. One way may be allowing the learner to practice
decision making, such as a tennis player’s choosing what shot to take.
Focus on Application 10.1
Mental Practice in Stroke Rehabilitation
The application of mental practice as a method to improve motor
skills has been a part of sport for years. In some sports the facilities
are restricted to seasons of the year, therefore mental practice would
be a perfect fit for practicing sport in the off-seasons when facilities
are not available. When research on mental practice, such as that
shown earlier in this chapter, began to reveal positive effects on
motor learning, instructors and therapists began to use these methods
in their teaching and therapies, respectively—notably, in stroke
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rehabilitation they were justified in using these methods.
Stroke is a medical condition that results in damage to the brain.
Often the damage is to one side (hemisphere) of the brain, resulting
in motor control impairments to the opposite side of the body. The
goal of rehabilitation is to regain function by repairing the brain
through goal-directed movements. Continued activations, from
active physical practice, can lead to partial or full restoration and
compensation.
But, there are limitations to the amount and frequency of
rehabilitation treatments involving a therapist. Active movement
outside therapy times may be encouraged, but unless specified and
monitored, may not always be wise (e.g., for various reasons).
Fortunately, recent research has shown that mental practice (and
imagery) generates neural activations of the brain that are similar to
actual movement (Garrison, Winstein, & Aziz-Zadeh, 2010). Since
mental practice cannot replace physical rehabilitation as an
effective therapeutic technique, the combination of physical and
mental practice is more effective than either form alone (Cha et al.,
2012; Dickstein & Deutsch, 2007; Nilsen, Gillen, & Gordon, 2010).
There appears to be little doubt that mental practice serves as an
effective addition to the occupational and physical therapists’
arsenal of rehabilitation tools.
When and How to Use Mental Practice
The learner needs to be instructed carefully in the methods of mental
practice. It is not enough simply to suggest that the learner go somewhere
and “practice mentally”; systematic procedures are necessary. Weinberg and
Gould (2011) provide additional tips for maximizing the use of imagery and
mental practice, such as performing imagery and mental-practice activities
in as many different settings as possible. Because mental practice and
imagery require no apparatus; large groups of learners can practice at the
same time. The clever instructor will find ways to interleave the two
practice modes to provide maximal gains, for example by urging mental
practice during the rest phase between trials of a fatiguing task or to break
up a long string of repetitious physical practice trials.
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Organizing Practice and Rest
Certainly, scheduling practice is a major concern in designing a program of
instruction. This includes how many days per week skills should be
practiced, whether to provide layoff days, how much practice to give on
each day, and how much rest to provide during the practice period so fatigue
does not become a problem. Some of these questions have been studied in
the laboratory and in applied settings, revealing interesting and useful
implications for skills learning.
There are countless ways to organize practice, of course, but how these
variations affect learning and trade off with each other is complicated.
Several common features of practice sessions have been well-studied, such
as, the two research streams in this section: (1) research studying the effect
of rest among periods of practice; and (2) research on mental practice and
observation given during the intervals between periods of practice.
How Often to Practice
One of the first decisions concerns how often the learners will practice. On
the one hand, a major goal of an instructor is usually to facilitate maximal
learning before the first opportunity to perform the skills in a “real”
situation. Most training schedules involve a limited period of time in which
certain skills are practiced (e.g., a fixed number of weeks); one idea would
be to provide as much practice as possible per week, concentrating it to
maximize the practice time.
However, as shown by Baddeley and Longman (1978) with keyboard skills,
there is likely some upper limit to the amount of practice per day that is
effective for learning. In this study, postal workers were retrained for a total
of 80 h of practice time (60 h only for one group). The practice was varied
in terms of the amount of practice time per session (1 or 2 h) and the number
of sessions per day (one or two), so among the three groups that received 80
total hours of training, two groups completed the training in 40 days (in
either two, 1-h sessions or one, 2-h session per day) and one group
completed it in 20 days (with two, 2-h sessions per day). The group that
received 60 total hours of training completed the training in 60 days (one, 1-
h session per day). The data in figure 10.5 present the results for the last
part of the training period for all four groups, plus the results for three
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retention tests conducted months later. (Note that one peculiarity of this
study was that the group having the least concentrated practice [1 h, once
per day] received less total practice time than the other three groups.)
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Figure 10.5 Results of the Baddeley and Longman (1978) study of retraining
postal workers on keyboard tasks under different distributed-practice
conditions.
Reprinted by permission from Schmidt and Lee 2011; Data from Baddeley and Longman 1978.
A consistent finding in figure 10.5 throughout practice and in retention was
the relatively poor performance of the most concentrated practice group—
the individuals who practiced for 4 h a day (2-h sessions, twice per day, red
trace). Somewhat similar results, with concentrating practice into one
session leading to less skill in a retention test than distributing practice
across sessions, were obtained in a study of learning a golf skill (Dail &
Christina, 2004).
Another interesting finding in the Baddeley–Longman study was that the
practice schedule that produced the least learning (4 h per day, red trace), as
measured in the retention tests was the most popular among the trainees.
This illustrates an important consideration: Learners do not always know
which procedures work most effectively in terms of the overall goal of
learning. (We will see more evidence of this less-than-optimal
understanding of the learning process later in this chapter.)
The trainees’ dissatisfaction with the short amounts of training in the 1 h/day
condition highlights an important concern regarding the issue of practice
efficiency versus practice effectiveness. The Baddeley and Longman study
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clearly demonstrated both: Although practicing 4 h/day was the least
effective schedule in terms of learning, it was the most efficient in terms of
total practice time (i.e., the least amount of time spent in the practice
environment, working and resting). The decision to trade off effectiveness
versus efficiency when determining how to distribute work and rest periods
across days is not a simple one, and must take into account other factors,
such as the likely motivational deficits that might occur with extended
periods of practice and the fatigue-producing effects of the task.
Work and Rest Periods During a Practice Session
Unlike the questions concerning practice scheduling over a week, issues
concerning organizing practice and rest during a single practice session
have been studied a great deal in the laboratory. For the purposes here, we
can define two classes of practice distribution, based on the relative
amounts of practice and rest provided, called massed and distributed
practice in the literature.
Massed practice provides relatively little rest between trials. For example,
if a task has practice trials 30 s long, a massed-practice schedule might call
for rest periods of only 5 s or perhaps no rest at all (so-called continuous
practice). On the other hand, distributed practice calls for much more rest,
perhaps with a rest period between trials that is as long as a trial itself (30 s
in this example). There is no fixed dividing line between massed and
distributed practice, but massed practice generally has reduced rest, as
compared to distributed practice.
Researchers interested in massed and distributed practice (see Lee &
Genovese, 1988, for a review) have generally been concerned with the
effects of physical and mental fatigue-like states on learning effectiveness.
For a given number of practice trials, decreasing the amount of rest between
trials reduces the time available for dissipation of fatigue, degrading
performance on the next practice trial and perhaps interfering with learning.
Many experimenters have used a fixed number of practice trials in an
acquisition session, varied the amount of rest between these trials, and then
measured learning on a retention test. These work and rest schedules have
different effects on performance and learning for discrete and continuous
tasks.
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Discrete Tasks
A few of the distribution-of-practice experiments have used relatively rapid
discrete tasks. Generally, when the task involves performance trials that are
only a few tenths of a second, as in a throw or a kick, it is very difficult to
make the rest periods short enough to affect performance. In the laboratory,
even when the rest periods were made as short as 300 ms, seemingly far
shorter than for any real-world practice session, the result has been either
no decrement in performance or learning or perhaps even slight advantages
for massed conditions (see Carron, 1967; Lee & Genovese, 1989). It may be
best to conclude that, for discrete tasks, there is no evidence that reducing
the rest time through massed practice affects learning.
Continuous Tasks
By far, most massed- and distributed-practice research has involved
continuous skills analogous to real-world tasks such as swimming or typing.
In these tasks, fatigue-like states have much more opportunity to build up
within a trial, so decreasing the rest between trials has larger effects. This
can be seen in a study by Bourne and Archer (1956), in which groups of
subjects performed 30 s trials on a pursuit-rotor task that were separated by
differing periods of rest between trials—either 0, 15, 30, 45, or 60 s. The
results of the performance in the practice trials and in a retention test are
illustrated in figure 10.6. Three general conclusions can be drawn from this
figure; these typify the results seen generally with distribution-of-practice
experiments using continuous tasks (Lee & Genovese, 1988):
1. Longer rest periods generally lead to more skilled performance during
practice (i.e., distribution of practice has a performance effect).
2. When measuring learning, the size of the differences between groups is
generally reduced as measured after a retention interval.
3. The positive effect of longer rest intervals on performance remains
large on a retention test (i.e., distribution of practice has a learning
effect).
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Figure 10.6 Results of the Bourne and Archer (1956) study examining the
effects of rest intervals of differing length, inserted between 30 s periods of
practice on a pursuit rotor task.
Reprinted by permission from Bourne and Archer 1956.
Implications of Practice Distribution Effects
The effects of rest between trials have considerable importance when
viewed from the standpoint of practice effectiveness versus practice
efficiency. Clearly, longer rest periods have positive effects on both
performance and learning. However, these effects come with a “cost,”
because essentially the rest periods are “lost time.” In economic terms, the
cost of introducing rest periods into a training application (e.g., in an
airplane cockpit simulator) might outweigh the benefits to learning.
Fortunately, there are alternatives to “resting” that can make the time
between physical practice trials both effective and efficient from a learning
standpoint. Two of these—mental practice and observation—were
discussed earlier in this chapter. We consider these alternatives again in the
next sections as a means to improve practice effectiveness and efficiency
(Ong & Hodges, 2012).
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Inserting Mental Practice and Observation
From the perspective of practice distribution, inserting periods of mental
practice or observation during the “rest” between practice trials makes
sense from a practice efficiency viewpoint. Rather than seeing rest as “lost
time,” one can use these intervals productively to enhance motor learning at
the same time the learners are recovering from fatigue. In effect, this strategy
would make distributed practice both effective and efficient compared to
massed practice. In this sense, both intervals of physical practice and
mental practice or demonstrations would contribute positive effects on
learning.
A second perspective concerns how rest during periods of physical practice
can encourage the self-evaluation of performance outcomes, which should
contribute to learning. One of the principles that we discuss in the next
chapter concerns how using augmented feedback allows learners to become
self-sufficient in their capability to assess their own performance and to
learn to help themselves through error corrections. Interspersing periods of
mental practice, demonstrations or observations, or both between periods of
physical practice encourages learners to try to assess and better understand
what makes their own performance effective at times and ineffective at other
times.
Finally, mixing physical practice with periods of demonstration or
observation and mental practice would likely have positive effects on
motivation as well. These periods provide time for reflective thinking about
the positive things that occurred during physical practice and about how the
negative aspects might be improved upon in the next opportunity to perform.
Variable Versus Constant Practice
Ultimately, the goal of practice is to prepare a learner to perform to the
highest possible level of skill when it counts—such as applying
cardiopulmonary resuscitation (CPR) skills in an emergency. The task in
these situations should be highly familiar to the learner because the action,
as it was practiced, closely resembles the criterion task. However, these
situations are few; by far, the more common situation occurs when the task
changes somewhat between practice and the performance of the task in the
criterion situation. Practice, in these cases, must prepare the learner to be
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highly adaptable to task requirements such that learners can perform in a
way they have never performed before. How does one prepare for these
different criterion tasks, and, more importantly, what are the features of
practice that enable one to perform in such novel situations with skill and
dexterity?
Review: Motor Programs and Parameters
Recall from chapter 5 that the skill of throwing, for example, represents a
collection, or class, of movements. For example, in American football, skill
in passing includes the skill to produce many different throwing distances,
with arched or flat trajectories, and to stationary or moving targets, and also
involves many other potential variations. Even with these variations, there
is something fundamental, consistent, and characteristic about a football
pass, such as the particular grip on the ball, the step and the follow-through,
the arm action, and the wrist movement that produces a spiral. These
features are called invariances (chapter 5). One can determine that an action
is a member of a particular class of actions because it has the same
invariant features as the other members of the class. Also, these features
differ between classes, as there is no way to change a putting stroke into a
football pass, for example. Members of a class have these characteristics:
Common movement sequencing exists among the elements.
Common temporal, or rhythmical, organization exists.
The same action can often be carried out with different effectors (e.g.,
limbs).
The same action can differ in surface features (e.g., speed) on two
different occasions, which is specified by different movement
parameters.
Return now to the conceptual model of human performance developed
earlier, for the example in figure 8.1. Action patterns are governed by
generalized motor programs (GMPs), each with an almost invariant
temporal organization. Once learned, a GMP for football passing can be
applied to many specific throwing situations by specifying movement
parameters in the movement programming stage, which define how the
movement is produced. The learner evaluates the environment, decides what
kind of pass is required in this particular case, and then specifies the proper
parameters to the program (those that are likely to achieve the movement
goals as assessed). The parts of the conceptual model involved in this
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process are shown in figure 8.1. The questions are, How are the proper
parameters selected, and how does the performer learn to generalize to all
of the throwing distances in the class?
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Passing a football accurately requires the player to throw with different
speeds and trajectories under variable conditions. How can a learner
practice to maximize the ability to perform this skill?
Schema Theory
One conceptualization to answer these questions is schema theory
(Schmidt, 1975), in which the learner acquires a set of rules, called the
schemas, that relate the surface features of throwing (e.g., distances, speeds)
to the parameter values necessary to produce those actions. Figure 10.7
illustrates how this could work for the distance dimension in the football
pass as an example. On the horizontal axis are all the possible distances the
football has been thrown in the past, with a maximum of 40 m for this
learner. Whenever a ball is thrown, the learner records briefly the distance
the ball went as well as the parameter that was used for the GMP for that
throw. Over time, with many such throws recorded, the learner then
abstracts (or generalizes) the relationship between the past throwing
distances and the task parameters that were used for the GMP (Schmidt,
1975). Figure 10.7 shows the abstraction. To avoid the storage problem the
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learner stores these values just long enough to update the schema after each
throw, and then these are discarded or forgotten. According to schema
theory, this process is responsible for motor learning associated with
learning to parameterize the GMPs—a common problem for the player using
the same GMP over and over again.
An important question for researchers is how the schema is learned. The
learning process, according the schema theory, is also illustrated in figure
10.7. Suppose the learner begins the learning process by generating
parameter A, which leads to a throwing distance of 29 m. On subsequent
attempts, the learner chooses parameter B, which leads to a throw of
approximately 18 m. Then, he issues parameter C, which leads to a throw of
about 34 m, and so on. With each throw, the learner associates (abstracts or
generalizes) the GMP parameter value used with the resulting distance
thrown (seen as the collection of individual (blue) data points in figure
10.7). In the future when the person wants to make a throw of X1 m, he
chooses the distance of X1 on the X-axis, where he uses the vertical red line
to access the schema (blue line), and this results in using parameter value of
Y1. In the same way, desiring a throwing distance of X2 m, he chooses a
parameter value of Y2.
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Figure 10.7 The schema relates parameter values to outcome distances. To
produce a throw of 25 m (X1) or 45 m (X2), the learner relies on the schema
to generate parameter values equal to Y1 and Y2, respectively.
This process generates a movement with parameter values based on the
learner’s past experience in using this program. Most important, this process
allows the learner to make a movement that he has never made previously.
Suppose that this learner has never produced a 45 m pass before (X2 in
figure 10.7). No problem: The learner simply provides the best estimate of
the required parameter values from the schema, and then runs off the GMP
with this parameter value (Y2 in figure 10.7), thereby producing a novel
action that has not been performed before.
Variable Practice Enhances Schema Learning
Considerable evidence suggests that variable practice should be
particularly effective in cases like this. A basic research paradigm contrasts
two groups of learners: One is a constant-practice group, practicing only a
single member of a class of tasks; the second is a variable-practice group,
practicing several members of the class of tasks (for the football passing
example, this would mean practicing varying football passing distances).
The two groups have the same amount of practice, but they differ in the
amount of practice variability they receive.
The constant group typically outperforms the variable group during the
acquisition phase. Typically a learner can produce instances of a single
version of a movement more effectively than multiple versions, particularly
if these versions are interleaved. However, when subjects in both groups
are switched to a novel version of the task on a transfer test, the group that
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received variable practice performs at least as well as the constant group,
and frequently they do so much more skillfully (e.g., McCracken &
Stelmach, 1977). This evidence has been interpreted to mean that learners
acquire schemas when they practice and that variable practice enhances
their development, allowing more effective novel-task performance in the
future. In other words, variable practice enhances generalizability,
allowing the performer to apply past learning to actions not specifically
experienced before in practice.
This generalization can be seen in figure 10.8 (from Catalano & Kleiner,
1984); groups practiced a coincident-timing task (predicting the arrival of a
moving light simulating a moving ball by making a hand response) with
target velocities of only one speed (5, 7, 9, or 11 mph [8, 11, 14.5, or 18
kph]) under constant-practice conditions; or another group practiced all
speeds (5, 7, 9, and 11 mph) under variable-practice conditions, with the
different target velocities presented in a random order over trials. Transfer
tests were then given on task versions that were not experienced previously
by either group (at 1, 3, 13, and 15 mph [1.6, 4.8, 21, and 24 kph]). In figure
10.8 three of the four transfer velocity tests, variable practice led to much
smaller errors than did constant practice; hence variable practice produced
generalization. Of course, many skills require us to produce variations that
have never been produced before and practice is one means of maximizing
the capability to move effectively in this way.
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Figure 10.8 Mean absolute timing errors in four coincident-timing transfer
target-speeds following practice in either a constant (gray) or variable
(blue) practice condition.
Data from Catalano and Kleiner 1984.
A Caveat to Variable Practice
Schema theory predicts that variable practice is best suited for performance
that applies novel movement parameters to just a single version of the GMP,
as in free-throw shooting a basketball. However, would variable practice
also be optimal for learning in situations in which a criterion where only a
single version of the GMP is needed? Recent evidence presented in Focus
on Research 10.1 raises some doubts.
Focus on Research 10.1
Especial Skills: An Exception to Variable Practice?
Schema theory, and indeed just common sense, suggests that if
someone is faced with learning to produce a class of actions, that
practice ought to be structured to be variable, taking into account the
unlimited variations to be experienced in the criterion version of the
skill. But how would practice be structured if only one variation of
the criterion task would ever be experienced? In other words, are
skills that are to be performed in only one way represented
differently in memory than a class of skills that can be performed in
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infinite ways?
The latter question was addressed in a series of experiments
involving basketball shooting skills (Keetch, Lee, & Schmidt, 2008;
Keetch et al., 2005). Two types of shots are commonly used in
basketball—jump shots (which, as the name implies, involve the
player leaping into the air before releasing the ball), and set shots
(in which the player remains in contact with the ground during the
shot). Jump shots are usually taken anywhere on the court, but set
shots are typically taken only at the foul (or free-throw) line.
Keetch and colleagues predicted that if variable (jump shot)
practice results in the development of a schema for a class of
actions, then performance at one location should be highly related to
performance at all other locations, including shots at the free-throw
line. In contrast, constant practice (of the set-shot, practiced only at
the free-throw line) might result in a specific advantage for
performance at that one particular location. And this prediction
should be particularly strong for highly-experienced players, who
have taken thousands of set-shot practice shots in the development
of their expertise.
Data from college basketball players shooting set shots and jump
shots from five locations (including one from 15 ft [4.57 m]—the
foul line) are presented in figure 10.9. Jump shot accuracy
decreased almost linearly as the player moved farther from the
basket. In contrast, even though the set-shot decreased in accuracy
as the distance increased generally, performance at the 15 ft mark
was markedly more accurate than expected based on the
performances at the other shot locations.
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Figure 10.9 Performance of set and jump shots from five different
locations. Set-shot performance shows an advantage at the foul line,
whereas no specific advantage is seen for jump shots.
Reprinted by permission from Keetch, Schmidt, Lee, and Young 2005.
These findings have some interesting implications for learning, as
they seem to suggest that practice be structured according to the
criterion demands of the task—how the skills will be performed in
the “test” situation. If flexibility in producing a variation of a class
of skills is required, then it makes sense to continue with variable
practice. However, if only one version of the task will ever be
performed, then concentrating practice from the one location
appears to have practical merit (Breslin et al., 2012).
Exploring Further
1. Name another skill, usually performed from only one specific
location, that might show an effect similar to the set shot in
basketball.
2. For the task named in question 1, describe an experimental
methodology that would assess whether or not performance
would show a specific advantage from that location.
Blocked Versus Random Practice
In many, if not most, real-world settings, the learner’s goal is to acquire
more than a single skill or task in a limited practice period, sometimes even
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in a single practice period. Physicians practice different skills related to
surgery (such as suturing and knot-tying skills), musicians practice multiple
songs at a time, tennis players practice serving and volleying as well as the
more usual ground strokes during a single session, and so on. An important
question confronting the learner or instructor is how to sequence the
practice at these various tasks during the practice session so as to maximize
learning. Two variations have powerful effects on learning: blocked and
random practice.
Suppose that your student has three tasks (tasks A, B, and C) to learn in a
practice session and that these tasks are fundamentally different, such as
tennis serves, volleys, and ground strokes. That is, tasks are chosen such
that one cannot argue that any of them are in the same class or use the same
GMP. A commonsense method of scheduling such tasks would be to practice
all trials of one task before shifting to the second, then to finish practice on
the second before switching to the third. This is called blocked practice, in
which all the trials of a given task (for that day) are completed before
moving on to the next task. Blocked practice is typical of some drills in
which a skill is repeated over and over, with minimal interruption by other
activities. This kind of practice seems to make sense in that it allows the
learners to concentrate on one particular task at a time and refine and
correct it.
Another practice scheduling variation is called random (interleaved)
practice; where the order of task presentation is mixed, or interleaved,
across the practice period. Learners rotate among the three sample tasks so
that, in the more extreme cases, they never (or rarely) practice the same task
on two consecutive attempts. And from a common-sense perspective, the
random method, with its high level of trial-to-trial variability, its high level
of contextual interference would not seem optimal for learning.
The Shea and Morgan Experiment
John Shea and Robyn Morgan (1979) conducted a groundbreaking
experiment that revolutionized the way scientists think of the processes
involved in practice. Following some of the original ideas of William Battig
(1966), Shea and Morgan had subjects practice three different tasks (A, B
and C) that involved responding to a stimulus light with a correct series of
rapid movements of the hand and arm, with each task having a different
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predetermined sequence. One group of subjects practiced the tasks in a
blocked order, completing all task A practice before moving to task B,
which they completed before moving to task C. A second group practiced in
a random order; no more than two consecutive trials could occur for any one
task. The two groups had the same amount of practice on tasks A, B, and C
and had the same amount of total practice—they differed only in the order in
which the tasks were presented.
The results are presented in figure 10.10. The goal was to respond to the
stimulus and complete the movements as quickly as possible, so lower total
times indicate more-skilled performance. Notice that, during acquisition, the
blocked condition was far more effective for performance (with shorter
times) than the random condition. But recall that differences during
acquisition cannot be interpreted as differences in learning; rather, delayed
retention (or transfer) tests are needed to evaluate learning (these concepts
were presented in chapter 8).
Shea and Morgan tested for learning by conducting retention tests after 10
min and 10 days; these tests were conducted under either randomized or
blocked conditions, which produced four subgroups. The following
abbreviations indicate the condition in acquisition and the condition in
retention, respectively: R-B, R-R, B-R, and B-B. The first character in the
pair indicates the condition during acquisition (random or R and blocked or
B), and the second member of the pair indicates the performance conditions
in retention.
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Figure 10.10 Performance on speeded movement tasks under random- and
blocked-practice conditions during acquisition, and in random-ordered and
blocked-ordered retention tests.
Adapted by permission from Shea and Morgan 1979.
When the retention tests were under random conditions, the group that had
random practice in acquisition (R-R, solid blue) greatly outperformed the
group with blocked conditions in acquisition (B-R, solid red). When the
retention tests were under blocked conditions, again the random condition in
acquisition (R-B) outperformed those who had blocked conditions in
acquisition (B-B), but these differences were much smaller than for the
random retention tests. Clearly, the random conditions in acquisition were
always more effective for retention, but this benefit was clearly dependent
on the nature of the retention test.
An issue regarding variable practice was alluded to earlier. A very
important factor concerns how variable practice is scheduled; this issue
now becomes better understood due to the results of Shea and Morgan
(1979). Studies in which variable practice was scheduled in a trial-by-trial
random order showed rather large advantages compared to constant practice
(e.g., Catalano & Kleiner, 1984; also Pigott & Shapiro, 1984). The Shea
and Morgan findings suggest that scheduling how variable practice is
ordered influences its effectiveness.
Why Random Practice Is So Effective
The Shea and Morgan findings surprised many scientists in the field by
showing that, even though random conditions result in much less skilled
performance than blocked conditions in acquisition, random-practice
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conditions produce more learning. The findings were a large surprise
because most conventional viewpoints would suggest that learning should
be maximized by those conditions that make learners most proficient during
practice—there was no motor learning theorizing that could explain this
opposite result. As a result, some interesting new hypotheses were offered
to explain the findings.
Shea and Zimny (1983) argued that changing the task on every random-
practice trial made the tasks more distinct from each other and more
meaningful, resulting in more elaborate memory representations. As
revealed in subject interviews after the experiment, random-practice
subjects tended to relate the task structure to already learned materials
(creating “meaningfulness”), such as discovering that task B had essentially
the shape of an upside-down “Z.” Also, they would make distinctions
between tasks, such as “Task A is essentially like task C, except that the first
part is reversed” (creating “distinctiveness”). The blocked-practice
subjects, on the other hand, tended not to make such statements. Instead they
talked of running off the performances more or less automatically, without
thinking much about it, and blocked practice did not induce the kind of
comparative and contrastive efforts in practice that were experienced during
random practice. According to this elaboration hypothesis, increased
meaningfulness and distinctiveness produce more durable memories for the
tasks, and thus increased performance capabilities in tests of retention and
transfer.
An alternative hypothesis explains the beneficial effects of random practice
somewhat differently. Lee and Magill (1983) suggested that when the
learner shifts from task A to task B, the “solution” that was generated (in
short-term memory; see chapter 2) for performing task B causes the solution
previously generated for task A to be forgotten. When task A is encountered
again a few trials later, the learner must generate the solution anew;
therefore, performance in practice is relatively poor. Yet this solution-
generation process is assumed to be beneficial for learning (see also Cuddy
& Jacoby, 1982). In blocked practice, on the other hand, the performer
remembers the solution generated on a given trial and simply applies it to
the next trial, which minimizes the number of times the learner must generate
new solutions. Therefore, performance during practice in a blocked
schedule is very effective because the solution, once generated, is
remembered for a series of trials. Yet learning is poor because the learner is
not required to generate a “new” solution to the task on every trial. In this
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way, the key focus of the forgetting hypothesis is the fact that new solutions
are required frequently in random practice, but not in blocked practice; thus,
the development of the solution for the task is the key feature that facilitates
learning. Interestingly, the forgetting hypothesis suggests the somewhat
ironic and counterintuitive idea that “forgetting facilitates learning.”
A number of investigations have evaluated and supported both of the
hypotheses. For example, in a study by Wright (1991), members of a
blocked-practice group were encouraged to make explicit comparisons of
the task just practiced with one of the other tasks to be learned—essentially
inducing this group to “mentally” practice the tasks with meaningful and
distinctive processing. This special blocked-practice group outperformed
the other practice groups that had a similar intervention but without the
benefits of the explicit comparative and contrastive processing. The results
supported the elaboration hypothesis predictions because of the insertion of
these specific mental processing activities.
A key prediction of the forgetting hypothesis was that random practice
forces more extensive planning operations on each trial compared to
blocked practice. A study by Lee and colleagues (1997) attempted to reduce
the need for these planning operations by presenting a powerful “model”
just before each practice trial. This model was designed so that it would
inform subjects how to perform the next trial, and because the model
provided extremely strong memory guidance for the upcoming trial, the
model was hypothesized to prevent the construction process (because the
model provided the solution for the next trial). In the experiment, the
presence of the model was combined with random practice. The model,
eliminating as it did the subject’s requirement to reconstruct the “solution”
for the next trial, would interfere with performance in acquisition more or
less as blocked practice does. As figure 10.10 shows, the model obliterated
the usual benefits of random practice.
In the experiment, the random and blocked conditions are contrasted with
this special “random + model” condition. Clearly, the model was beneficial
for performance during acquisition (when the model was present), as seen
on the left side of figure 10.11 where the “random + model” group was far
more skilled than the group that had only random practice. However, in the
retention tests, where the model was withdrawn, the random + model group
regressed considerably, to the point that this condition led to the most error
in the delayed retention test. Providing the powerful model before each
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practice trial, while it was beneficial for performance when it was present,
was disastrous for learning. The model obliterated the beneficial
advantages of random practice. These findings support strongly the
forgetting hypothesis for the random- versus blocked-practice effect, and
they show that random practice is not necessarily the “magic bullet” for
effective motor learning.
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Figure 10.11 Providing a powerful guiding model reduced planning
operations in a random + model practice group obliterated the usual
random-practice benefit for learning (from Lee et al., 1997).
Reprinted by permission from Lee et al. 1997.
A number of studies have provided evidence supporting the elaboration
hypothesis, and a number have supported the forgetting hypothesis; but no
clear “winner” has yet emerged. As a result, it is probably a better idea to
consider these hypotheses as complementary, rather than competing,
explanations of the random- versus blocked-practice effects. The beneficial
effects of random practice over blocked practice appear to be due to
several factors:
Random practice forces the learner to become more actively engaged
in the learning process by preventing simple repetitions of actions.
Random practice gives the learner more meaningful and
distinguishable memories of the various tasks, increasing memory
strength and decreasing confusion among tasks.
Random practice causes the learner to forget the short-term solutions
(from working memory) to the movement problem after each task
change.
Forgetting the short-term solution forces the learner to generate the
solution again on the task’s next trial, which is beneficial to learning.
Further Research
Shea and Morgan’s findings have been very influential. Hundreds of studies
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have been conducted on the random and blocked practice since their
research was published, and many discussion articles have been written
about how to conduct practice in everyday activities (e.g., Schmidt & Lee,
2012). The next sections summarize some of the research that has emerged
in the years since the publication of this landmark study.
Contextual-Interference Effects in Nonlaboratory Tasks
As might be expected, the Shea and Morgan (1979) study motivated a large
number of researchers to examine random- and blocked-practice schedules
in non-laboratory tasks. Goode and Magill (1986) found similar random-
blocked effects in participants who were learning three different types of
badminton serves. Hall, Domingues, and Cavazos (1994) produced an
analogous effect using a group of college baseball players who engaged in
extra batting practice, hitting different types of pitches thrown in random or
blocked order. And Ste-Marie and colleagues (2004) found similar
beneficial effects for random practice in schoolchildren who were learning
handwriting skills. The Shea and Morgan laboratory findings appear to
extend to the acquisition of real-world tasks too.
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This walking garden allows rehabilitation patients to practice walking with
crutches over many surfaces, allowing for variable and random practice.
Random-Practice Limitations
The beneficial effects of random practice are not universal, however, and
some studies have resulted in no learning differences. Lee (unpublished
data, Louisiana State University, 1981), using the pursuit-rotor task, failed
to produce the expected random-practice benefits. The pursuit rotor,
however, as a form of tracking task, does not require very much advance
preparation between trials. Perhaps this can be taken as evidence that the
random-practice benefits might only occur in tasks for which considerable
pretrial preparation is needed.
Guadagnoli and Lee (2004) reviewed the varieties of evidence and
suggested that random practice is likely to be least effective when the task
demands are so high to begin with that performers have a very difficult time
producing even a single trial of the behavior. This could occur when
individuals are practicing a very “difficult” task, or when the learners
themselves are in some way not “appropriate” for the task to be learned. A
good example might be attempting to teach very young learners an “adult”
task that demands too much. In such cases, random practice would make the
practice environment too challenging and perhaps counterproductive to
effective learning.
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Alternatives to Blocked and Random Practice
Blocked and random practice represent “extreme” ends of the practice-
schedule continuum—random practice involves very little (or no) repetition
of the same task from one practice trial to the next, and blocked practice
involves almost no interleaving of practice on other tasks. These scheduling
extremes might be responsible for the rather dramatic shifts seen in
performance and retention, with blocked practice facilitating performance in
practice, but having a detrimental effect on retention (and vice versa for
random practice). Thus, one need for seeking an alternative to a random-
versus blocked-practice schedule concerns that fact that neither “optimizes”
both performance and learning; that is, neither is a practice structure that
facilitates learning without the degrading effects seen during the practice
period.
There is another reason for seeking alternatives to blocked and random
practice. Simon and Bjork (2001) asked their subjects to make predictions
about their performance just before a retention test. The results, presented in
figure 10.12, revealed an illusion about learning. Subjects with practice
under random- or blocked-acquisition conditions were asked to predict how
they would do on a retention test. The blocked practice “fooled” the
learners into thinking that they had learned much more than they really had,
whereas random practice led the learners to believe that they had learned
less than they had. Thus, giving learners a more accurate sense of how
learning is proceeding might be another reason to seek alternatives to
blocked and random practice.
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Figure 10.12 Before performance in a retention test (shown), individuals
who had practiced in a blocked order (blue) predicted that they would be
more skilled (the “predicted” bars) than those who practiced in a random
order (gray), (the “predicted” bars). In reality, the retention performance of
the blocked group was less skillful than that of the random group (“actual”
bars).
Data from Simon and Bjork 2001.
Hybrid Schedules
Some researchers have found that “moderate” levels of random practice,
with the practice schedule including interleaved short strings of blocked
practice for example, are beneficial for performance and learning. For
example, Landin and Hebert (1997) had novices practice a basketball
shooting task from different locations on the court according to either a
blocked order, a serial order (a quasi-random condition, but more structured
than purely random practice), or a “moderate” order in which practice
rotated from task to task after “mini-blocks” of three attempts from the same
distance were performed. As can be seen in figure 10.13, this “moderate”
practice format was successful in reducing the performance deficit normally
seen during purely random practice and facilitated learning as measured in
both a blocked- and a random-retention test (see also Pigott & Shapiro,
1984).
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Figure 10.13 A hybrid (moderate) practice schedule, which included small
blocks of blocked practice, facilitated basketball performance and learning
compared to purely blocked and serial practice schedules.
Adapted by permission from Landin and Hebert 1997.
Practice Contingencies
Although the hybrid approach to practice would appear to represent the best
of both worlds—it remains insensitive to individual differences. For
example, although three blocked trials might be optimal for one person, five
blocked trials or no task repetitions might be optimal for two other people.
A type of schedule that is more sensitive to these individual differences is a
“contingency” schedule, whereby the “difficulty” of the task (Choi et al.,
2008) and the decision to repeat the same task or switch to an easier or
more difficult task (e.g., Simon, Lee, & Cullen, 2008) depend on the
performance success of the individual. These contingencies are just
beginning to be examined by researchers and represent an exciting new
approach to the topic.
Summary
Physical practice is just one way to “rehearse” a task. Several methods not
involving physical rehearsals have also been shown to enhance learning.
Observation (of a human model) provides objective information that
learners can use to organize their thinking about the task. Mental practice
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and imagery reflect important methods to undertake this organization of
thoughts.
Rest periods during physical practice (relative to no-rest conditions)
produce gains in learning, especially so for continuous tasks. However, long
rest periods have the disadvantage of making practice less time efficient.
Research suggests that these rest periods can be used more efficiently if
combined with periods of observation, mental practice, or both.
Variable practice involves intentional variations of a given task. Compared
to constant practice, in which only a single variant is practiced, varied
practice facilitates retention and generalizability to a novel situation whose
specific variant has not received prior practice. Variable practice is thought
to operate by generating stronger schemas, which define the relationship
between parameters for a GMP and the movement’s outcome.
Large learning gains can be made through effective practice organization
and scheduling. An important concept is random practice, in which trials of
several tasks are interleaved during acquisition. Relative to blocked
practice, in which trials of a single given task are presented repeatedly,
random practice produces far more skilled performance at retention (i.e.,
more learning). Random practice operates by preventing the learner from
repeating the same movement output on successive trials and by interleaving
experience gained from performing different activities on adjacent trials.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
chapter.
Interactive Learning
Activity 10.1: Better conceptualize the possible ways of organizing
practice by identifying the pattern represented by each of five graphical
representations and the type of practice illustrated.
Activity 10.2: Explore the ways that random practice leads to better
learning than blocked practice by matching descriptions to either the
elaboration hypothesis or the forgetting hypothesis.
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Activity 10.3: Answer a series of questions on how instructors can
influence learners’ levels of motivation.
Principles-to-Application Exercise
Activity 10.4: The principles-to-application exercise for this chapter
prompts you to choose an activity that involves several motor skills,
then identify a specific learner and design a practice session that
would include blocked practice and random practice and why these
types of practices are used for each skill.
Check Your Understanding
1. Explain how rest and cognitive activities between periods of practice
influence motor learning.
2. Explain why including variable and random practice when teaching a
person to play volleyball can be beneficial to learning. Are there any
volleyball skills for which this type of practice would not be
beneficial? Why or why not?
3. Discuss the differences between internal focus and external focus
instructions. Give an example of each for someone learning to play the
piano. Which of your examples would be more beneficial to learning
for an intermediate student?
Apply Your Knowledge
1. Discuss three motivational tools or techniques that a physical therapist
could use to help ensure a client is motivated during a recovery
program. What factors would you consider in order to integrate
observational learning or mental practice into the client’s schedule?
2. You have volunteered to coach your nephew’s soccer team for the
summer. Two of the skills that you would like to work on with your
team this season are penalty kicks and dribbling the ball down the
field. How might you organize work–rest periods during practice for
each of these skills? How might you include an amount of task
variability appropriate to each skill? Are there any characteristics of
your players that you would need to take into consideration when
organizing practice? Why?
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Suggestions for Further Reading
The review by Ste-Marie and colleagues (2012) provides a solid
framework for considering various factors related to observational learning.
A meta-analysis (statistical review) of the mental practice literature is
provided by Feltz and Landers (1983). A distribution-of-practice meta-
analysis and review was published by Lee and Genovese (1988). For more
on variable practice and schema development, see Schmidt (1975) and
Schmidt and Lee (2011; chapter 13). The variability-of-practice literature is
reviewed in Shapiro and Schmidt (1982). And numerous reviews of the
contextual-interference literature exist, including those by Magill and Hall
(1990), Merbah and Meulemans (2011), and Lee (2012). See the reference
list for these additional resources.
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Chapter 11
Augmented Feedback
How Giving Feedback Influences
Learning
Chapter Outline
Feedback Classifications
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Functions of Augmented Feedback
How Much Feedback to Give
When to Give Feedback
Summary
Chapter Objectives
Chapter 11 describes the influence of augmented feedback on motor
performance and learning. This chapter will help you to understand
the types of augmented feedback and their role in the
conceptual model,
how augmented feedback functions to influence performance
and learning,
the various properties of augmented feedback, and
the influence of the various ways in which augmented feedback
can be delivered.
Key Terms
absolute frequency of feedback
augmented feedback
average feedback
bandwidth feedback
concurrent feedback
faded feedback
feedback
feedback delay interval
guidance
guidance hypothesis
inherent feedback
instantaneous feedback
intrinsic feedback
knowledge of performance (KP)
knowledge of results (KR)
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post-feedback delay
precision of feedback
relative frequency of feedback
summary feedback
trials delay of feedback
This chapter can be considered an extension of chapters 9 and 10 because it
also concerns the organization of practice. Here, though, the focus is on how
an instructor organizes and delivers feedback—information about
performance or errors that the learner can use for making future corrections.
Here we discuss some principles of how feedback influences learning,
examining questions about feedback frequency, feedback timing, and the
most effective kinds of feedback for learning.
Without a doubt, one of the most important learning processes concerns the
use of feedback about actions attempted in practice. As discussed in chapter
4, feedback may be a natural consequence of the movement, such as seeing a
hammered nail become flush with a block of wood or hearing the sound of a
key that has been depressed on a keyboard. Feedback can also be given in
various “artificial” forms that are not so obvious to the learner, such as the
performer’s score in a driver’s license examination or a comment about a
swimmer’s “whip kick” in performing the breaststroke. Of course, verbal
feedback is under the instructor’s direct control; thus, it makes up a large
part of practice organization.
Feedback Classifications
The term “feedback” originally emerged from the analysis of closed-loop
control systems (see chapter 4), referring to information about the difference
between a performance and some desired goal-state. In closed-loop system
terminology, feedback is considered to be information about error. In human
performance systems, however, the term feedback takes on a more general
meaning: information about the movement and movement outcomes, not just
errors.
It is helpful to form a clear feedback classification system because many of
the different kinds of feedback follow somewhat different principles. One
system appears in figure 11.1, where the global category of all information
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available to the learner is divided into several subclasses. First, of course,
there is a great deal of sensory information “out there,” most of which is not
related to the movement the person is learning. But of the information that is
related to the movement, it is useful to categorize it as either available
before the movement or as a result of the movement. Information before
action is critical for movement planning (discussed in chapters 4 and 5) and
largely comes from the last practice attempt. This information influences the
processes involved in anticipation, decision making, parameter selection,
and so on. However, it is the information provided during the movement or
after the movement that is commonly considered feedback. Feedback may be
further divided into two main categories: inherent (or intrinsic) and
augmented feedback (see figure 11.1).
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Figure 11.1 A feedback classification scheme.
Inherent Feedback
Sometimes also called “intrinsic feedback,” inherent feedback is
information provided as a natural consequence of making an action. When
you take a swing at a tennis ball, you feel your hips, shoulders, and arms
moving; you see the racket travel; you see, hear, and feel the ball’s contact;
and you see and hear where the ball travels. All these types of information
are inherent to performing the task, and you can perceive them more or less
directly, without special methods or devices. Other kinds of inherent
information might be the sounds or smells made by a race car engine, or
seeing the progress of a saw blade as it cuts through a piece of wood. This
general class of feedback has been discussed throughout the text during the
development of the conceptual model of human performance.
Augmented Feedback
Now it is time to finalize the conceptual model of human performance by
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adding augmented feedback, shown as the blue line in figure 11.2.
Sometimes called “extrinsic feedback,” augmented feedback consists of
information from the measured performance outcome that is fed back to the
learner by some artificial means, such as an instructor’s voice or a video
played on a computer or cell phone. Thus, augmented feedback is
information supplied to the learner that is over and above that contained in
inherent feedback. As the name suggests, augmented feedback serves to
supplement the naturally available (inherent) information. Most importantly,
this feedback is information over which the instructor has control; thus, it
can be given or not given, given at different times, and given in different
forms to influence learning. Scientists in motor learning tend to use the term
“feedback” as shorthand for augmented feedback, and we use the term this
way as well.
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Figure 11.2 Conceptual model with the addition of augmented feedback.
Knowledge of Results
Knowledge of results (KR) is a particularly important category of
augmented feedback shown in figure 11.2 with the new blue line. KR is
augmented, usually verbal (or at least verbalizable) information about the
success of an action with respect to the environmental goal. In many daily
activities, KR is redundant with the inherent information. Telling someone
he missed the nail with the hammer and telling a basketball player she
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missed the free throw are examples of KR (the verbal information) that
duplicates the information the performer received anyway.
However, KR is not always redundant with inherent feedback. Surgical
residents, springboard divers, and dancers must wait for the assessment
scores to know the success of their performance. In riflery and archery it is
not always possible to see where the projectile hit the target area, so
augmented KR information must be received from a coach or a scoring
device. In these cases, KR is critically important for performance and
learning because, in tasks in which the inherent feedback is absent or
incomplete, learners cannot know about the outcomes of their actions
without some form of KR.
KR is frequently used in research, where specific aspects regarding how
information is given to learners can be controlled. Researchers use this
general method to examine how feedback processes influence learning.
Earlier research was often conducted with very simple tasks, such as
blindfolded limb-positioning tasks, in situations in which the learners could
not use inherent feedback. These experiments generally showed that, without
any KR, there was no learning at all (e.g., Thorndike, 1927; Trowbridge &
Cason, 1932). On the other hand, providing KR about errors allowed rapid
improvement across practice, such that performance improvements
remained in retention tests even when KR was withdrawn. These results
suggest that when learners cannot detect their own performance errors
through inherent feedback, no learning occurs at all unless some form of
feedback is provided. This is one of the reasons that feedback is considered
the single most important variable for learning except for practice itself
(Bilodeau, 1966).
This is not to say learning cannot occur without KR, however. You have
probably learned many real-world tasks with no KR (as defined here)
provided by an instructor, as in practicing free throws by yourself. You
received goal achievement information (i.e., whether or not the ball went in
the basket) through inherent feedback, and this feedback was the basis for
your learning. Thus, the principle is as follows: Some information relative
to goal achievement must be received, either through inherent sources or
augmented sources, for any learning to occur.
Knowledge of Performance
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Knowledge of performance (KP), sometimes referred to as “kinematic
feedback,” is augmented information about the movement pattern the learner
has just made. It is frequently used by instructors in real-world settings. For
example, you often hear coaches say things like “That pass was too slow” in
ice hockey, “Your tuck was not tight enough” in a springboard dive, or
“Your backswing was too short” in golf. Each of these forms of KP tells the
learner something about kinematics (the movement or movement patterns).
Note that KP information, unlike KR, does not necessarily tell about
movement success in terms of meeting the environmental goal. Rather,
kinematic feedback tells about the nature of the movement pattern that the
learner actually produced. Some of the main similarities and differences
between KR and KP are summarized in table 11.1.
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For a diver, augmented feedback could include a score provided by a judge
or a coach’s assessment of the diver’s movement pattern.
Functions of Augmented Feedback
After a movement attempt, a music instructor says to the learner, “The
rhythm was pretty good, but try to slow everything down a little next time.”
Think of all the meanings that such a simple statement could have to the
learner. First of all, the feedback could have a motivating, energizing, or
discouraging function: It could make the learner slightly more enthusiastic
about the activity and encourage her to try harder. Second, there is of course
the information about the rhythm and absolute timing of the movements,
which can be combined with other information (e.g., how the movement felt)
to generate new knowledge. Third, feedback helps to direct the learner’s
attention either toward the production of the movement (an internal focus of
attention) or toward the end product or effect of the movement in the
environment (an external focus). Finally, feedback can also produce a kind
of dependency, such that performance is enhanced when feedback is present
because of its influence on the following trial but causes performance to
deteriorate when it is later withdrawn.
Generally in real-world settings, augmented feedback operates in four
interdependent ways simultaneously; these functions are often very difficult
to separate. To summarize, augmented feedback does these things:
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Produces motivation, or energizes the learner to increase effort
Provides information about errors as a basis for corrections
Directs the learner’s attention toward the movement or the movement
goal
Creates a dependency, leading to problems at feedback withdrawal
Focus on Research 11.1
Revising Ideas About How Feedback Works
Research traditions that were established in the animal-learning
literature early in the 20th century strongly influenced the thinking
about how feedback must work in motor learning. In one example, a
food reward was given if a hungry animal pressed a lever within 5 s
of hearing a signal. Over trials, the animal learned to press the lever
quite reliably when the sound occurred. Your new puppy quickly
learns to sit on command if you give a biscuit or a friendly pat when
the puppy performs the action. In this case, the food reward is
serving as feedback for correctly responding to the stimulus (to sit
on command).
Scientists realized that the nature and timing of the feedback had a
marked influence on learning the desired response. These findings
were captured and summarized by Thorndike’s (1927) Law of
Effect, in which reinforcement (or feedback) played a prominent
role—associations (or “bonds”) between the stimulus and the
“correct” response were presumably strengthened when they were
reinforced by (or were accompanied by) feedback. Factors that
increased the immediacy or frequency of such feedback
presentations presumably strengthened these bonds, further
increasing learning. To view this another way, if feedback were
withheld after a particular trial and the learner could not know the
outcome from inherent feedback, then there could be no increment of
bond strengthening for that trial, rendering that practice trial
essentially useless for enhancing learning. These basic notions gave
rise to the general idea that effective feedback is presented as
immediately and as frequently as possible, which quickly became a
standard belief in the movement skills literature.
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Over the next few decades, scientists saw little in the literature that
would contradict this basic viewpoint about feedback for skill
learning. Gradually, based on Thorndike’s ideas, a set of feedback
generalizations emerged, suggesting that:
any variation of feedback during practice that makes the
information more immediate, more precise, more frequent,
more informationally rich, or generally more useful would be
beneficial for learning.
Such a view made good common sense—it just seems logical that
giving more information to the learner should benefit learning—and
this view became widely adopted as a result. The principle has
strong implications for the structure of practice, encouraging just
about anything that would provide more information to the learner.
As you will see throughout this chapter, however, the above
generalization is probably wrong in several ways. One of the major
difficulties with this principle emerged from research on the relative
frequency of feedback—a key variable that defines how frequently
feedback is scheduled in a learning session. Other variables, such as
feedback delay, feedback summaries, and bandwidth feedback,
failed to operate in ways predicted by Thorndike’s views.
In the end, these early theoretical beginnings were important
because they led to more research, new ideas, and a better
understanding of feedback processes in motor learning. These more
modern interpretations of the research constitute most of the
theorizing presented in this chapter. An important review of the
feedback literature by Salmoni, Schmidt, and Walter (1984)
provides much more on the historical context of this work.
Exploring Further
1. Why does the typical experiment in animal conditioning
provide an “extinction” period?
2. In what ways does augmented feedback in motor learning work
similarly to the provision of feedback in animal conditioning
studies? In what ways do the principles differ?
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Motivational Properties
The music instructor tells the struggling piano student, “Keep it up, you’re
doing fine.” This casual comment motivates the student to keep going a little
longer in practice. Certainly, one important function of feedback is to
motivate the learner, for example, in helping a tired learner to bring more
effort to bear on the task. In addition, early research revealed that, if
performance was deteriorating (in so-called vigilance tasks), performers
showed an immediate increase in proficiency, as if the feedback was acting
as a kind of “stimulant” to energize them again (Arps, 1920; Crawley,
1926). In addition, learners who are given feedback say they like the task
more, they try harder at it, and they are willing to practice longer. In short,
unless it is overdone, learners seem to like feedback. Even when an
instructor has another primary reason for giving feedback (e.g., to correct an
error), this extra motivational benefit is achieved “for free.”
You can capitalize on this feedback feature in practice. Think of ways to
give learners feedback relatively frequently. This is not as simple as it
sounds, though, because in large classes, one does not have much time to
devote to a single learner. In any case, try to avoid long periods in which
you give no feedback, because motivation can sag and practice can either be
very inefficient or cease altogether. Keeping learners informed of their
progress usually translates into their bringing more effort to the task, which
can only benefit them in terms of increased learning.
The effects of feedback as a motivating tool just discussed are primarily
indirect in their influence. For example, KR encourages the learner to keep
practicing, and the results of this additional practice are what influences
learning. However, recent research suggests that motivational feedback can
also have a direct effect on learning. Consider the study by Chiviacowsky
and Wulf (2007), for example. Learners in this study practiced a beanbag-
tossing task in which vision of the end-result accuracy was obstructed,
making feedback from the experimenter (KR) critical for improving
performance. Subjects in the “KR good” group were provided with
feedback about performance on the most skillful three performances out of
the previous six trials, over repeated blocks of practice. In contrast,
subjects in the “KR poor” group received information about their three least
skillful performances over these same practice periods. Learning, as
illustrated by the retention performance in figure 11.3, was facilitated by the
“good” feedback.
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Figure 11.3 Providing learners with feedback about their three most skillful
trials after each block of six trials (the “KR good” group, blue) resulted in
stronger retention performance than providing learners with feedback about
their three least skillful trials during practice (the “KR poor” group, red).
Reprinted by permission from Chiviacowsky and Wulf 2007.
The findings of Chiviacowsky and Wulf (2007) underscore the beneficial
effects of motivation on learning as discussed in chapter 10, and suggest that
motivating feedback can also have a direct effect on learning. These and
other recent findings on this new development in the feedback literature are
discussed by Lewthwaite and Wulf (2012).
Informational Properties
Probably the most important component of feedback for motor learning is
the information it provides about patterns of action. This feedback about
errors, giving direction for modifying future performance, is the focus that
makes the instructor so important for motor learning. Whereas a machine
can give out jelly beans for effective performance, a skilled instructor can
know the proper patterns of action for which feedback should be provided.
Because of the importance of this component of feedback, most of the
remainder of the chapter deals with the principles of its operation for
learning.
Consider now the example mentioned earlier, in which the music instructor
tells the student that the rhythm was fine but the tempo could be slower
overall. This information defines clearly the basis for making corrections on
the next attempt, bringing the performance closer to the values that
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characterize the “most-skilled” performance. There is no doubt that giving
information guides the learner toward the movement goal. Continued use of
this feedback keeps errors to a minimum and ensures that errors are
corrected quickly, thus holding the movement pattern very near to the goal.
Recognizing that augmented feedback is mainly informational raises many
important questions for the instructor. For example, what kind of information
can the learner use most effectively (e.g., information about limb position,
limb timing, coordination), in what form can it be presented (e.g., verbally,
in video replays, graphically), when can it be presented (immediately after
an action, delayed somewhat), and how often can it be presented (on every
trial, only some trials)? These questions are addressed in detail later in this
chapter.
Attentional-Focusing Properties
An important discussion in two previous chapters addressed the role of
attentional focus on performance (chapter 3) and learning (chapter 10). In
many situations, it is likely to be the case that performance and learning are
enhanced when the learner’s attention is directed to the product of
movement (or the movement’s goal achievement)—typically referred to as
an external focus of attention. In contrast, an attentional focus that is directed
toward the movement itself (an internal focus) often leads to poor
performance and learning.
Now consider the roles of KR and KP in this discussion. By its very nature,
KR provides information about success of performance relative to the
movement goal. Put differently, KR directs the learner to think about
externally-directed information. The informational content of KP, on the
other hand, is about the nature of the movement that was produced, such as
the spatial or temporal form of the action. Thus, the information content of
KP directs the learner’s attention to process movement-related information
—an internally-focused process.
The attentional-focusing properties of KR and KP set up the learner for a
potential conflict in practice goals. Since KP is acknowledged to be the
preferred form of feedback for making changes in the kinematics and
kinetics of the action itself (e.g., Newell & Walter, 1981), how can it be
used without the detrimental impact of an internally-focused attention?
Fortunately, researchers have studied ways of scheduling the provision of
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feedback so that the most useful information content can be delivered
without the potentially detrimental effects that might occur when (an
internal) attentional focus is created by that feedback. We discuss some of
that literature in the sections that follow.
Dependency-Producing Properties
When feedback that contains information for error correction is given
frequently, it tends to guide behavior toward the goal movement. In a sense,
this process operates in very much the same way guidance procedures do,
as we discuss later in this chapter. Physical guidance acts very powerfully
to reduce errors, sometimes preventing them completely. This is fine as long
as the guidance is present, but the learner can also become dependent on the
guidance, allowing performance to deteriorate markedly when the guidance
is removed and the learner attempts to perform without it (Salmoni,
Schmidt, & Walter, 1984).
Just as with physical guidance, augmented feedback tends to hold the
movement at the goal, allowing the learner to correct errors quickly and
thereby maintain the movement’s correct form or outcome. The problem led
to the guidance hypothesis (Salmoni, Schmidt, & Walter, 1984), which
holds that the learner can become dependent on such feedback, so that he
uses this augmented source of information instead of internally generated
processes to keep the movement on target. If the instructor’s feedback is then
removed on a retention test, the performance could suffer markedly if the
learner has not developed the capability to produce the movement
independently. Various ways have been developed to structure feedback to
minimize dependency-producing effects, as discussed in the sections that
follow.
How Much Feedback to Give
An instructor could give feedback about countless features of the action
after every performance attempt. Thus, overloading the learner with too
much information is a potential problem. Information-processing and
memory capabilities of the learner—particularly a child—are limited, so it
is doubtful that the youthful learner can take in and retain very much
information during multiple feedback presentations. It is also doubtful that
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the learner can be very effective in correcting the next action in more than
one way, particularly with feedback about motor patterning. Feedback
(perhaps leading to many other comments and thoughts) such as the kind
processed by the learner in figure 11.4 would be very difficult to translate
into an effective correction.
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Figure 11.4 Providing more feedback than can be processed effectively for
movement correction is probably detrimental to performance and learning.
Adapted by permission. ©Bob Scavetta. Any adaptation or reproduction of “1.5 Seconds of Thought”
is forbidden without the written permission of the copyright holder.
In general, too much information is generally not useful. For example, video
replay is only moderately effective as a feedback tool, despite its
widespread appeal (see Focus on Research 11.2) probably because there’s
too much information in a video replay. A good rule of thumb is to decide
what error is most fundamental and focus the feedback on that.
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Technology has made providing video feedback to athletes easier than ever,
but it is more effective when accompanied by cues to help the learner focus
on the relevant details.
Focus on Research 11.2
Augmented Feedback From Video Replays
Knowledge of performance (KP) has a long history in motor skills
research. Early pioneers in feedback methods recorded force–time
tracings in sprint starts on strip-chart paper (Howell, 1956). The
recordings were then displayed to the learner as KP feedback with
the correct tracing superimposed over the learner’s tracing (see
Tiffin & Rogers, 1943, for a very early study using similar methods
with industrial tasks).
Films were also popular, particularly involving professional and
collegiate sport teams; these were used as feedback so that a player
could analyze mistakes and determine more effective actions to use
next time. As learning tools, though, films were limited because the
time required for developing film was usually quite long,
troublesome, and costly. Also, many events intervened between a
given action and the feedback from it, making it difficult for the
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learners to remember what they did to produce particular errors and
how to avoid making those errors the next time.
Videotape solved many of the problems with film: Feedback about
whole performances could be viewed after only a few seconds of
tape rewind, and these replays would capture the details of the
movement very well. And, of course, digital imagery has taken this
kind of feedback to a whole new level. Crisp, clear, high-definition
recordings of performances can be captured with nothing more than
a cell phone and can then be distributed to others around the world
in seconds. Providing “live” feedback to the user has never been
easier, cheaper, or more informative.
But, an important question remains: Is presenting video replays an
effective method of providing augmented feedback? Early on,
Rothstein and Arnold (1976) reviewed the evidence on videotape
replays and, surprisingly, found that this feedback was not always
useful for learning. An explanation might be that videotape replays
provide too much information, so the learner becomes confused
about what to extract as feedback. This leads to the suggestion that
cuing, in which the instructor directs the learner to examine some
particular feature of the movement as feedback, could be an
effective technique.
Kernodle and Carlton (1992) provided evidence to support
Rothstein and Arnold’s suggestions. Subjects in each of four groups
practiced a throwing task (using a very lightweight foam ball,
organized so that the learner could not detect his accuracy on his
own) with their nondominant limb and were given retention tests for
learning immediately before five separate practice sessions. One
group received only KR about the distance of the throw. The other
three groups all received videotape replays of their performances—
one group with no additional feedback, another group with cues to
direct attention to certain parts of the video, and a third group with
supplemental cues indicating what changes to make on the next
attempt. The results, illustrated in figure 11.5 were quite clear.
Providing video feedback KP without additional information was no
panacea—learning was no more effective than when only KR was
provided. However, providing video feedback with the addition of
attention-directing cues, or even better, with cues about what errors
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to correct, was indeed effective for learning.
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Figure 11.5 Providing real-time augmented feedback facilitates
learning only if supplemented with additional cuing, as indicated by
five retention tests conducted before practice over several days.
Reprinted by permission from Schmidt and Lee 2011; Data from Kernodle and Carlton 1992.
Exploring Further
1. Describe how you might conduct an experiment, similar to Kernodle
and Carlton’s (1992), using a task from physical rehabilitation, such as
correcting an asymmetric walking gait.
2. Most of the video replay research has been conducted using older
technology (videotape feedback). Would you expect newer video
technology to produce similar or different results than those from
Kernodle and Carlton’s (1992) study?
Precision of Feedback
Feedback about movement errors can be expressed in terms of either the
direction of the error, the magnitude of the error, or both, and with varying
levels of precision. The following are some of the principles involved.
Qualitative information about the direction of the learner’s error (early vs.
late, high vs. low, left vs. right, and so on) is critical to bring the movement
into line with the goal. In addition, it is generally helpful to report some
quantitative magnitude of the errors as part of the feedback, such as “Your
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movement was 2 cm to the left of the target.” Precision of feedback is
based on the level of accuracy with which the feedback describes the
movement or outcome. You can imagine feedback that only roughly
approximates the movement feature, as in learning to do partial weight
bearing on crutches. Feedback such as “You put a little too much weight
through your left leg that time” would be considered less precise (and
produce less effective learning) than “you put 4.3 pounds too much weight
through your left leg that time” (Trowbridge & Cason, 1932).
The level of feedback precision to provide seems to depend on the learner’s
skill. Early in practice, the learner’s errors are so large that precise
information about the exact size of the errors does not matter, simply
because the learner does not have the movement-control precision to match
the precision of correction specified by the feedback. By the same argument,
movement control will be much more precise at higher levels of skill, and
more precise feedback can be used effectively as a consequence. Of course,
the effective instructor will be well-schooled in the proper form of the
action and in how to detect errors in order for the feedback to describe the
important movement aspects well.
Absolute and Relative Frequency of Feedback
The feedback literature has defined two general descriptors for feedback
frequency. Absolute frequency of feedback refers to the total number of
feedback presentations given to a learner across a set of trials in practice. If
there are 400 trials and the instructor gives feedback on 100 of them, then
the absolute frequency is 100—simply the total number of feedback
presentations. Relative frequency of feedback, on the other hand, refers to
the percentage of trials receiving feedback. In this example, the relative
frequency of feedback is 25% (100 feedback trials out of a total of 400
trials).
Consider this situation: A learner practices a task such as rifle shooting to a
distant target, where errors cannot be detected without augmented feedback.
Because the instructor is busy giving feedback to other students, information
about the performances can be given only occasionally. We have discussed
the learning benefits of the trials that actually receive feedback, but what
about the no-feedback trials in between? Are they useful for learning? Do
these so-called blank (no-feedback) trials have any function for learning at
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all, or are they simply a waste of time? Earlier, instructors suspected that
no-feedback trials were generally ineffective, which led to the development
of a number of artificial (and expensive) methods to give feedback about
performance when the instructor was occupied. What are the issues here?
Such questions about so-called “blank trials” can be answered by examining
the effect of relative frequency of feedback for learning. Research has
shown that blank trials can be actually beneficial for learning, even though
subjects receive no feedback on them and cannot detect their errors for
themselves (Winstein & Schmidt, 1990). This can be seen in figure 11.6.
Using a limb patterning task, the 100% group received feedback after every
trial (100% relative frequency), and the 50% group received feedback after
only half of the trials (50% relative frequency), with the same total number
of trials. The groups improved at about the same rate in acquisition.
However, in the tests of learning performed without any feedback, there was
a strong effect for the 50% group to have learned more than the 100% group,
even though half of the 50% group’s trials involved no feedback. This
finding challenges Thorndike’s perspective that trials without feedback
should produce no learning (see Focus on Research 11.1).
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Figure 11.6 Reducing relative frequency of feedback from 100% to 50%
during acquisition has beneficial effects on learning. RMSE = root-mean-
squared error (see chapter 1).
Reprinted by permission from Winstein and Schmidt 1990.
Faded Feedback
A key feature in the Winstein and Schmidt (1990) study was that reduced
feedback frequency was achieved using faded feedback. In this method, the
learner is given feedback at high relative frequencies (essentially 100%) in
early practice, which has the effect of guiding the learner strongly toward
the movement goal. The instructor then gradually reduces the relative
frequency of feedback as skill develops in order to prevent the learner from
developing a dependency on this feedback. With advanced skill, the
performance does not deteriorate very much when feedback is totally
withdrawn for a few trials. If performance does begin to drop off, the
instructor can give feedback again for a trial or two to bring behavior back
to the target, then withdraw the feedback again. The instructor can adjust
feedback scheduling to the proficiency level and improvement rate of each
learner separately, thus tailoring feedback to individual differences in
capabilities. The ultimate goal is to generate the capability for the learner to
produce the action on her own, without a dependency on feedback. Even
though feedback is critical for developing the movement into a skilled
pattern, it appears that it must be eventually removed to accomplish
permanent skill learning.
Bandwidth Feedback
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A method that combines both qualitative and quantitative types of
information, discussed earlier, as well as the faded method of reducing the
relative proportion of feedback, is known as bandwidth feedback
(Sherwood, 1988). In this method, the decision to provide a learner with
feedback is based on a preset degree of acceptability of performance. For
example, if a resident is suturing a wound after a surgery, the instructing
physician might say “good job” if the stitches were performed satisfactorily
or the time to perform the surgery was acceptable. If either the accuracy or
time was not acceptable, however, the physician might provide precise
feedback about the nature of the errors made and what aspects of the
performance need to be improved.
There are two general rules in using the bandwidth method (Sherwood,
1988). First, no feedback is given if some measure of performance falls
within an acceptable level (or “band”) of correctness. However, for the
bandwidth method to work properly, the learner must be told ahead of time
to interpret the absence of feedback to mean that performance was
essentially “correct” (Lee & Carnahan, 1990). Second, precise feedback
indicating the amount and direction of the error is given if and when
performance falls outside the range of acceptability. In this way, the
bandwidth method combines both qualitative and quantitative forms of
feedback.
You may have guessed by now that a key issue in using the bandwidth
method is deciding what level of error tolerance is appropriate for a
learner. A simple illustration of this concept is presented in figure 11.7. The
darker orange band in the figure illustrates a narrow bandwidth (−1 to +1
mm). In this case, the learner would receive no feedback on trials 5, 8, 9,
and 10 (but would have been instructed that receiving no feedback would
mean that those movements were essentially correct) and given precise
feedback on the other trials. The orange band (−2 to +2 mm) illustrates a
larger bandwidth that could be used instead. Here, the learner would not be
given feedback on trials 2, 3, 4, 5, 8, 9, and 10 and given precise error
feedback only on trials 1, 6, and 7.
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Figure 11.7 The bandwidth-feedback method. Establishing a preset level of
tolerance (the two orange bands) determines what type of feedback is given
to the learner. Two potential bands of correctness are shown here – a
narrow band (the darker orange) and a wider band (which includes both the
lighter and darker orange). If performance falls outside of the tolerance
band chosen, then and only then is error feedback given. Performance that
falls within the band chosen results in no feedback, which the learner has
been told beforehand means that performance was “correct.”
Obviously, the size of the bandwidth will have a determining influence on
how frequently the learner is given precise error feedback versus feedback
that performance was “correct.” And indeed, the bandwidth size itself has
an important effect on learning. For example, Sherwood (1988) found that a
larger bandwidth (10% of the target goal) produced more learning than
smaller bandwidths (5% or 1%).
The bandwidth method is consistent with a number of well-established
principles for learning. First, the method produces reduced faded-feedback
frequency as a by-product (which Winstein & Schmidt [1990] found to be an
important feedback variable in itself). When the learner is just beginning,
the movements often tend to be outside the tolerance level, leading to
frequent feedback from the instructor. As skill improves, more performances
fall within the band, leading to less frequent error feedback on the error
trials that are outside the band (which, as we will discuss later, has an
important positive influence on learning in itself). Second, with
improvements in performance (resulting in more performances inside the
bandwidth), the decreased frequency of error feedback results in increased
frequency of rewarding feedback. And as we have discussed twice
previously, rewarding learners with motivating feedback has a strong
learning function. Finally, withholding information on a set of trials that fall
within the bandwidth fosters more stable, consistent actions. Eliminating
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these small trial-to-trial corrections has a stabilizing influence on
performance, because the learner is not encouraged to change the action on
every trial.
Summary Feedback
Another way to avoid the detrimental effects of every-trial feedback is to
give feedback summaries. In this method, feedback is withheld for a series
of trials—say, following a series of 5 to 20 performance attempts—after
which feedback for the entire series is summarized for the learner, perhaps
by providing a graph of all of the previous performance attempts in that
block of trials. On the surface, summary feedback would seem to be
particularly ineffective for learning. The informational content of the
feedback would be seriously degraded because the learner wouldn’t be able
to associate the feedback with any particular practice attempt, and thus
would have no basis for making trial-to-trial corrections to improve
performance.
Even so, research has shown that summary feedback can be particularly
effective for learning (e.g., Lavery, 1962; Schmidt et al., 1989). Generally,
even though summary feedback is less effective than every-trial feedback
for performance during practice, when feedback was withdrawn in retention
tests, subjects who had received summary feedback performed more
skillfully than subjects who had received every-trial feedback. Therefore,
summary feedback is more effective for learning than every-trial feedback.
How can summary feedback be so effective? For us, the better question is
why is every-trial feedback so ineffective for learning?
How Many Trials to Summarize?
How many trials should you include in summary feedback? Can there be too
many summarized trials? Evidence suggests that there is an optimal number
of trials to include in summary-feedback reports, with either too few or too
many trials decreasing learning. Why? With every-trial feedback (a one-trial
summary), the learner is guided strongly to the goal; but this also maximizes
the dependency-producing effects. On the other hand, if the feedback
summarizes a very large number of trials (say 100), the dependency-
producing effects are greatly reduced, but the learner also benefits less from
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the informational properties of the feedback that guide him to the goal. This
rationale suggests the existence of an optimal number of summary-feedback
trials in which the benefits from being guided to the goal are balanced just
right with costs of the dependency-producing properties.
This prediction was confirmed in experiments by Schmidt, Lange, and
Young (1990), who studied different numbers of summary feedback trials on
a laboratory task resembling batting in baseball. As seen in figure 11.8,
there was an optimal relationship between summary length and learning,
with the 5-trial summary feedback length being optimal. Again, even though
the 1-trial condition (every-trial feedback) was best for performance during
earlier practice, when feedback was being presented, the 5-trial condition
was optimal for learning.
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Figure 11.8 Performance score for various numbers of trials included in a
summary-feedback presentation for acquisition (left) and immediate and
delayed retention (right).
Reprinted by permission from Schmidt, Lange, and Young 1990.
How Does Summary Feedback Work?
What are the processes behind the benefits of summary feedback? The
following are three ways summary feedback (relative to every-trial could
function to aid learning:
1. Summary feedback might prevent the dependency-producing effects of
frequent feedback because it causes the learner to perform
independently for several trials before finally receiving feedback.
Then the learner can make corrections to the general movement pattern
produced in the earlier trials.
2. Summary feedback might produce more stable movements because
feedback is withdrawn for several trials, giving the learner no basis
for a change in the movement from trial to trial. Frequent feedback, on
the other hand, more or less encourages the learner to change the
movement on every trial, which prevents the movement from achieving
the stability needed for subsequent performance.
3. Summary feedback appears to encourage learners to analyze their
inherent movement-produced feedback (kinesthetic, visual, and so on)
to learn to detect their own errors (this concept was discussed in
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chapter 9). Frequent feedback tells learners about errors, eliminating
the need to process inherent feedback, so the learners do not need to
process information about their errors, as KR provides this for them.
Average Feedback
In a variant of summary feedback called average feedback, the learners
wait for a series of trials before receiving feedback information about their
scores (as with summary feedback), but now receive only the average score
on those trials instead of a trial-by-trial (e.g., graphical) summary. For
example, the golf instructor might watch the learner make 10 swings before
commenting, “Your backswing was about 6 inches too short on those last 10
shots.” Results from studies by Young and Schmidt (1992) and Yao,
Fischman, and Wang (1994; illustrated in figure 11.9) showed that average
feedback and feedback summaries were far more effective for learning (i.e.,
retention) than every-trial feedback (red). And average feedback appeared
to be slightly more effective for learning than summary feedback.
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Figure 11.9 Average feedback produced benefits similar to summary
feedback.
Reprinted by permission from Yao, Fischman, and Wang 1994.
Average feedback and summary feedback might operate in the same general
way—by blocking the detrimental, dependency-producing effects of every-
trial feedback. Average feedback also allows the instructor to formulate a
more complete idea of what the learner’s error tendency happens to be. On
any one attempt, just about anything can occur by chance alone (because
performances vary greatly from trial to trial). However, by watching the
performer do several trials, the instructor can “filter out” this within-subject
variability (i.e., by averaging) to detect the error that a learner typically
(i.e., on average) tends to make. Thus, average feedback gives the learner
more reliable information about what to change, and how much to change it,
on the next few practice attempts.
Learner-Determined Feedback Schedules
A different approach to the study of scheduling feedback gives the learner
control over when to receive feedback, instead of having this determined by
the instructor. In many ways this makes good common sense—the learner is
often in the best situation to know when feedback would be most beneficial.
In a study conducted by Janelle and coworkers (1997), subjects practiced
throwing with their nondominant limb, with two groups receiving KP about
the quality of the throwing motion. In one group, each subject received
feedback whenever the learner requested it. For each subject in the other
group, the feedback schedule was matched to (i.e., yoked to) that of a
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member of the self-determined group, thereby ensuring that the amount of
feedback and its scheduling were identical for the two groups—only the
determination of feedback delivery was different (i.e., learner- vs.
experimenter-determined). A third, control group received no KP feedback.
Consider the results of Janelle and colleagues’ (1997) study, shown in figure
11.10, and focus first on the bottom part of the figure. The symbols here
represent the frequency with which learners in the self-determined group
requested feedback in each of the 20 blocks of practice trials over the two
days in the experiment. Note that the learners tended to request feedback
relatively infrequently (on 11% of the trials, overall, orange trace, bottom
half of figure 11.10), and also that they tended to fade (or “wean”
themselves off) feedback as practice continued (ranging from 21% on the
first block of practice to 7% on the last block).
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Figure 11.10 Self-determined feedback (purple) compared to a yoked
control condition (green) and a no-feedback control condition (blue) for
optimizing learning.
Adapted from Janelle et al. 1997.
Now consider these feedback manipulations on performance and learning,
represented in the top half of figure 11.10. First, compare the experimenter-
determined (yoked) group (green) and the control group (blue). Clearly,
giving KP feedback, which was also faded over trials for this yoked group,
was beneficial for performance and learning. Now compare the
experimenter (green) and learner-determined (purple) groups. Giving the
decision to the learner to determine when feedback would be delivered
provided a boost to performance and learning that was above and beyond
that generated by fading feedback. Why might this be so?
One implication from this study is that learners likely need (or at least
request) feedback far less frequently than instructors tend to provide it. But
there is another aspect of learner-determined feedback that should be
mentioned. Research conducted by Chiviacowsky and Wulf (2002) revealed
that learners tend to request feedback more frequently following trials that
they perceived they performed well, as compared to trials that they thought
they performed poorly. Thus, as discussed previously in this chapter (see
also figure 11.3), there may be an important motivational component driving
the request for feedback, which in turn has a beneficial effect on learning
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when that feedback is delivered.
When to Give Feedback
Assuming that it is desirable to provide feedback about a particular
performance, an important question that remains is this: To maximize
learning, when would it be given? We often hear that “immediate feedback”
is desirable (see the subsequent section on this), leading to the idea that an
instructor would strive to give feedback as quickly as possible after a
performance to maximize learning (again, review Focus on Research 11.1).
What is the role, if any, of the timing of the information that we give?
Feedback timing can be described in terms of three intervals, shown in
figure 11.11. When delivered during the ongoing movement, it is typically
called concurrent feedback. (Note that physical guidance falls within this
definition too, since it consists of augmented information, though not verbal
information, that helps to signal errors and is provided during an ongoing
movement.) The interval of time after the completion of movement until
feedback is presented is called the feedback delay interval. And the
interval after the provision of feedback until the next movement starts is the
postfeedback delay interval.
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Figure 11.11 Terms used to describe the various times at which feedback
can be delivered.
Feedback During the Movement
One of the most powerful ways to deliver feedback is to provide it while
the movement is ongoing. The information can be used to regulate ongoing
(particularly long-duration) actions by giving a basis for correcting errors
and “pushing” the movement closer to the action goals. Two methods are
typically used to provide ongoing information: (1) concurrent feedback, in
which augmented information about the movement error (or the correct
movement) is provided by verbal, visual, or auditory means; and (2)
physical guidance, in which haptic or kinesthetic information is signaled to
the learner by means of a physically restricted guidance device or a person
(e.g., therapist) who physically restricts the movement. Despite their
apparent differences, the two methods influence similar processes that
govern motor learning.
Concurrent Feedback
A classic experiment, providing considerable insight into some of the
processes involved in concurrent feedback, was conducted by Annett
(1959). He asked subjects to learn to produce a given amount of pressure
against a hand-operated lever. During the movement, one group of subjects
received concurrent visual feedback on a display showing the amount of
pressure they exerted in relation to the goal pressure whereas another group
did not. As expected, the concurrent feedback facilitated performance
greatly during practice. However, on a retention test with the feedback
removed, this group performed very poorly, with some subjects pressing so
hard that they damaged the apparatus! Subjects who had learned the task
with this concurrent feedback were unable to perform without it. Similar
results, showing enhanced performance but poor retention, were shown in
Schmidt and Wulf (1997).
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Physical Guidance Techniques
Concurrent feedback, such as that provided in the studies just cited,
provides information that helps the learner to avoid making errors, to
correct errors quickly, or both. Guidance techniques often work in a more
direct way—to prevent the learner from making errors by physical means
(e.g., see Focus on Application 11.1).
Physical guidance techniques represent a large class of methods in which
the learner is “forced to” produce the correct movement patterning.
Guidance devices have several goals; the main one is to reduce or eliminate
errors and ensure that the proper pattern is carried out. This is particularly
important when the movement is dangerous, as in gymnastics, where harmful
falls can be prevented by various spotting methods, or in swimming, where
fearful beginners can use flotation devices. Guidance is also useful for
training with expensive equipment, where mistakes can be costly as well as
dangerous, as with learning to drive a car or fly an airplane.
Guidance methods vary widely across settings. Some forms of guidance are
very loose, giving the learner only slight aids to performance. An example
is the instructor who provides very slight hand pressure to guide the learner
or talks the learner through the action. Other forms of guidance are far more
powerful and invasive. An instructor can constrain the learner’s movements
physically, as when the physical therapist “forces” the patient’s movements
into the proper path, preventing a serious fall. Physical-guidance devices
are particularly popular in sports such as golf, where the aids constrain the
movement pattern physically in several ways, with the hope that subjects
will learn it.
Each method provides the learner with some kind of temporary aid during
practice. The desire, of course, is that learning, as measured by performance
in the future without the aid, will be enhanced. But the research suggests that
while learning might be facilitated by small amounts of guidance, the
negative impact on learning accrues quickly (Hodges & Campagnaro,
2012).
A study by Armstrong (1970) provides an important statement about the
effects of guidance and an empirical comparison of concurrent feedback and
terminal feedback as well. Over three days of practice, subjects learned to
move a lever with elbow extension and flexion movements (see figure 5.8)
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in order to produce a specific, timed kinematic sequence, or movement
pattern (see also Focus on Research 5.3). As illustrated in figure 11.12,
most of the movement error was prevented in a guidance group that received
a physically restricted patterning produced by light haptic (i.e., touch)
restrictions to errorful actions. Movement error was not prevented entirely
but was eliminated quickly in a concurrent-feedback group, whose subjects
were able to see the ongoing movement’s kinematic trace on a computer
screen, overlaid on the target template. And error was eliminated gradually
over practice in a group that received KR feedback after the completion of a
trial. Figure 11.12 shows that, even after three days of practice, subjects in
the terminal-feedback group did not achieve the level of performance of the
other groups. Clearly, this guidance procedure did its job, assuring that the
learners remained on target.
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Figure 11.12 Effects of guidance (red), concurrent feedback (blue), and
terminal feedback (green) on performance and on a no-feedback
retention/transfer test of learning.
Reprinted by permission from Schmidt and Lee 2011; Adapted from Armstrong 1970.
But now consider the respective performances of these groups in a retention
test, in which all groups were transferred to a test condition without the
benefit of any augmented guidance or augmented feedback (far right side of
figure 11.12). Several things are of interest to note here. First, the terminal-
feedback group, which had been the most errorful of the three groups during
practice, clearly showed the most learning as measured in these retention
trials. Second, in the absence of augmented feedback, the terminal feedback
group maintained the level of performance that had been achieved at the end
of the practice trials. And finally, the performance of the physical-guidance
group was most errorful of all in retention, showing that guidance during
practice was particularly horrible for learning. Note also that both the
guidance group and the concurrent-feedback group deteriorated remarkably
over the retention interval. In fact, the performance of both of these groups
deteriorated almost to the level of performance displayed by the terminal-
feedback group on their very first block of practice, suggesting that guidance
and concurrent feedback were almost completely ineffective for learning.
As mentioned previously, however, not all physical guidance is detrimental
to learning. Guidance certainly plays an important role in dangerous or
frightening situations; here it would be undesirable to continue practice in
the absence of guidance. And guidance may also serve a useful function in
the beginning stages of learning a skill; here, the communication of
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information to a learner is particularly difficult unless the learner is also led
through the motions. Recent research also suggests that reducing or
withdrawing guidance as the learner develops skill at the task is a fruitful
learning procedure (see also Focus on Application 11.2).
Focus on Application 11.1
Physical Guidance in Stroke Rehabilitation
The effects of acute stroke are often devastating, and many
individuals who experience a stroke never fully regain the motor
capability that was lost due to the brain damage. But many
individuals do indeed recover. Some of this recovery is
spontaneous, as the brain heals itself following the trauma. And
some of the recovery can also be attributed to intense therapeutic
interventions involving movement.
Physical guidance is a frequently used technique in rehabilitation
and is typically based on two basic fundamental assumptions: (1)
that learning is a process of repetition and (2) that repeating an
optimal or “correct” movement pattern results in more learning than
repeating a movement that is suboptimal, incorrect, or errorful.
Physical guidance techniques are designed with both of these
assumptions in mind. Both of these assumptions have questionable
validity, though, that we’ll cover in a later section.
Patients who have had a stroke often tire easily because of extreme
weakness. One advantage of upper limb guidance techniques, for
example, is that they can be used to move the limb for the patient
(“passive” movement) or to support the weight of the limb at least
partially so that active movement can be done with minimized effort.
An advantage, therefore, is that much less fatigue occurs during a
therapy session with a guidance device, and more repetitions can be
performed as a result, satisfying the first principle. By this view,
then, more practice should lead to more complete rehabilitation
outcomes (see Barnett et al., 1973).
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The second principle is more contentious. The view of optimizing
learning as a process of repeating a “correct” or desirable
movement pattern is basically an extension of Thorndike’s Law of
Effect (see Focus on Research 11.1). This view characterizes
learning as a process of strengthening the association between a
goal (e.g., to move in a certain way, “correctly”) and a response
(e.g., actually moving correctly). For Thorndike, augmented
feedback, in the form of “reward,” was the agent that served to
increase the repetition of this association. So, in theory, physically
restricting the response so that only the correct movement can be
performed should optimize the Law of Effect.
As we discuss in this chapter, however, the evidence from studies
with healthy adults suggests that physical guidance is an ineffective
method of practice, for several important reasons. Moreover, the use
of physical guidance as a therapy intervention in stroke
rehabilitation has come under increasing criticism (Mehrholz et al.,
2008; Timmermans et al., 2009). The result is that new techniques
are now being devised, with the aim of maximizing the positive
benefits conferred by guidance devices that provide assistance only
when needed—allowing the patient to make and experience some
errors in movement, but not ones that would lead to further injury
(Banala et al., 2009).
Focus on Application 11.2
Physical Guidance in Learning to Swim
In the text, we perhaps leave the impression that all physical
guidance is detrimental for learning. Actually, there are a few
situations in which physical guidance turns out to be very useful.
Here’s just one of them.
When one of us (RAS) was a graduate student at the University of
Illinois, he was assigned to teach a course in beginning swimming.
This course should have been called something like “Teaching the
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Persistent Nonswimmer to Swim,” as many of these students could
not swim a stroke, and many were truly terrified of the water.
After a week or so of learning to become familiar with water (e.g.,
in the shallow end of the pool, blowing bubbles, learning to open
one’s eyes underwater, counting fingers of a partner underwater), the
next task was for the students to learn the elementary backstroke as a
lifesaving stroke in case they were to fall into the water somewhere.
This process involves the usual techniques (in the shallow end of
the pool): learning to float on the back, gliding on the back after a
push-off from the side of the pool, then adding a “frog kick” and
eventually adding an arm stroke.
Then came the terrifying part: swimming with the elementary
backstroke from the shallow end to the deep end of the pool.
Naturally, most of the students were quite apprehensive about this
task, so we developed various methods to alleviate their fear. One
of these involved the use of long (15 ft or 4.57 m) wooden poles,
about an inch (2.5 cm) in diameter. On the first attempt the instructor
would walk along the poolside adjacent to the swimmer in the water
just touching the pole against the swimmer’s far-side hip. This did
two things: First, it provided a measure of assurance for the
swimmer, since all he had to do in an emergency was to grasp the
pole, and the instructor could pull him to the pool edge. Second,
though, this method did not interfere with the swimmer’s own
strokes allowing him to gain confidence and to learn the stroke.
This method was enormously successful. In fact, as a “final exam,”
the students had to swim a mile without touching the sides or ends of
the pool. By this time most of them had been “weaned” from the
assistance of the wooden pole, and they were quite capable of
swimming relatively long distances (albeit very slowly). Over the
course of about 10 years in which this course was taught, the
average percentage of students who completed the 1 mi (about 2.5
km) swim was over 70%! The faculty proudly presented to each
successful swimmer a fancy certificate with a gold star on it
(suitable for framing), saying essentially, “I swam a mile at the
University of Illinois.” The look of pride on these students’ faces
was impossible to describe.
These were the two major keys to the success of this procedure:
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Alleviating nearly completely the learner’s fear so that the
student could learn the stroke
Doing so in a very noninvasive way so that the guidance did
not interfere with the actions the student was trying to produce
Common Processes in Concurrent Feedback and Physical
Guidance
The evidence discussed in the previous sections points to an important
principle. Guidance and concurrent feedback, almost by definition, are
effective for performance when present during practice. After all, these
supplements are designed to help the performer make the correct action, to
prevent errors, to aid in confidence, and so on, so there is little surprise that
performance benefits from guidance. But the real test of guidance
effectiveness is how well subjects do when the intervention is removed, and
here is where these procedures often fail. When the ongoing information
source is removed for retention tests, performance usually falls to the level
of, or sometimes below, that of learners who had no guidance at all. That is,
guidance is not a very effective variable for learning if it is not used wisely.
How can these principles of guidance be understood? Probably the best
interpretation is that, during practice where guidance is present, the learner
relies too strongly on its powerful performance-enhancing properties, which
actually changes the task in several ways. Physical guidance can modify the
“feel” of the task. Decision-making processes change when the instructor or
the guidance tells the learner what to do. Also, the learner does not have the
opportunity to experience errors, or to correct errors, during the guided
movement or on the next movement. The learner will have failed to acquire
the capability necessary to perform in a retention test or in a competition
when the guidance is not present.
Notice that this interpretation is really a statement of the specificity view
discussed in previous chapters. If guided practice changes the task
requirements markedly (as it does), the task is not really the same task it
was under the unguided conditions. If these modifications are large (as in
very strong physical guidance procedures), then practice on the guided
version can be thought of as involving practice on a different task rather than
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practice on the unguided version. Under the specificity view, practice on the
guided version will be effective for a retention test only under guided
conditions; it will not be effective for a retention test performed under
unguided conditions. This fits with the principle that transfer tends to be
maximized when the two tasks are similar. Guided and unguided versions of
the same task are thought of as dissimilar, leading to poor transfer between
them.
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Special devices are being developed to assist stroke patients in their
attempts to recover motor function, where factors such as amount of practice
and practice variability come into effect.
Feedback After the Movement
The information-processing perspective about feedback holds that the
learner uses feedback to correct errors. If the feedback presentation is
separated in time (feedback delay) from the action and the learner forgets
various aspects of the movement by the time feedback arrives, wouldn’t the
feedback be less useful in making corrections? In animal conditioning
experiments, laboratory rats learning to press a bar after a presented tone
suffer a decrement to learning from feedback delays; and, if the delay is long
enough, there is no learning at all. Some researchers believed that the same
would apply to humans and motor learning. But what does the evidence say?
Empty Feedback Delays
First, consider simply lengthening the time interval between a movement
and its feedback, with the interval free of other attention-demanding
activities (conversations, other trials, and so on). The information-
processing view would expect longer intervals to interfere with learning.
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Yet when empty feedback delays have been examined in human research,
scientists have almost never found systematic effects on learning (Salmoni,
Schmidt, & Walter, 1984) with delays ranging from several seconds to
several minutes. The lack of any degraded learning when the feedback
delays are lengthened has been surprising. In any case, the evidence seems
to suggest that, without other activities in the interval between a movement
and its feedback, the instructor doesn’t need to worry about the delay in
giving feedback.
Instantaneous Feedback
There is one exception to this generalization about feedback delay, however
—situations in which feedback is presented very soon after a movement.
Under the belief that feedback given quickly will be beneficial for learning,
many instructors have tried to minimize feedback delays, essentially giving
feedback that is almost simultaneous with the completion of movement.
Instantaneous feedback is common in many simulators, for example, such as
medical mannequins, in which feedback about pressure is displayed
immediately after a chest compression is performed.
Note that instantaneous feedback is not, technically, the same as
concurrent feedback, because feedback is being delivered after the
movement has finished. But the effects on performance and learning are
remarkably similar to those for concurrent feedback. Research shows that
giving feedback instantaneously, as opposed to delaying it by a few seconds,
is actually detrimental to learning (Swinnen et al., 1990). This can be seen
in figure 11.13: Subjects in the instantaneous-feedback condition (blue)
performed a simulated batting task more poorly than the delayed group (red)
on the second day of practice and on several retention tests given up to four
months later. One interpretation is that feedback given instantaneously
blocks the subject from processing inherent feedback (i.e., how the
movement felt, sounded, looked). Attending to the augmented feedback from
the instructor likely restricted the learning of error-detection capabilities, as
discussed in chapter 9.
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Figure 11.13 Instantaneous feedback degrades learning compared to
delayed feedback.
Reprinted by permission from Swinnen et al. 1990.
An interesting extension of these findings is that blocking inherent (or
intrinsic) feedback for a few seconds after a trial should also be effective
for learning. This could be done by blocking the golfer’s view of the ball’s
flight at a driving range, or using a blindfold for basketball dribbling
practice. Curiously, one quite old study by Griffith (1931) used exactly this
method to teach golf. After several weeks, learners who had practiced
without the aid of vision of the club’s contacting the ball were
outperforming another group who had practiced with vision. To our
knowledge, these methods have not been studied very thoroughly, so this
extension of the findings should be regarded cautiously.
Filled Feedback-Delay Intervals
When the delays are long in many real-world settings, other attention-
demanding activities can occur between a given movement and its feedback.
These intervening activities may include conversing with a friend,
practicing some other task, or even attempting other trials of the given
movement as with the summary- and average-feedback methods discussed
earlier in this chapter. The research findings fall into two classes,
depending on the nature of the intervening task.
Intervening Activities of a Different Task
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Imagine that the activity occurring between a given movement and its
feedback is a different task that interferes in some way. This activity could
be a trial of a different motor task or even a task involving mental
operations, such as recording one’s scores or giving feedback to a friend.
These events during the interval from the movement until feedback generally
degrade learning as measured on retention tests (Marteniuk, 1986; Swinnen,
1990).
Trials-Delay Technique
But what if the intervening activity is just another trial of the same motor
task? For example, the instructor might give the learner several minutes to
practice a skill, then give feedback about the first movement after the
learner has completed several more attempts in the interim. For instance,
feedback from a therapist to a patient when practicing to stand from a seated
position might include statements such as “On your first attempt you started
to stand before your feet were properly positioned under your knees,” but
this is provided after several more attempts at the sit-to-stand have already
been completed. This has been called trials delay of feedback, with other
trials of a given action intervening between the movement and its feedback.
Although the trials-delay procedure would seem to prevent the learner from
associating the movement and the corresponding feedback, the evidence
says that it is not detrimental at all; and it may be more effective for learning
than presenting feedback after each trial (Lavery & Suddon, 1962). In fact,
researchers have suggested that the performance of intervening trials before
the delivery of feedback has the effect of raising awareness of the inherent
feedback available after performing the task, perhaps making it more
important or valuable when it is then presented to the learner (Anderson et
al., 2005).
Intervening Subjective Estimations
The conclusions of Anderson and colleagues (2005) just discussed support
a view that learning is enhanced when processing of the inherent feedback
occurs before augmented feedback is provided. This idea has been
examined more directly in studies that promoted subjective estimation of
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task performance during the KR-delay period. For example, learners who
practiced a throwing task with their nondominant limb performed more
skillfully in retention tests if they made subjective estimates of their
throwing technique during practice (Liu & Wrisberg, 1997). And a study by
Guadagnoli and Kohl (2001) revealed that the negative effects of 100% KR
frequency were reversed if learners made subjective estimates of error
before the delivery of the feedback on each trial (see figure 11.14). Note,
however, that a group (blue) that benefitted from having feedback presented
on only 20% of their trials (the remaining trials received no feedback)
received no further benefit (compared to the purple group) from error
estimation, perhaps because this 20% group was estimating spontaneously
on the no-feedback trials.
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Figure 11.14 The negative effects of presenting feedback on every trial
(100% KR) were reversed when subjects performed an error-estimation
procedure. RMSE = root-mean-square error (see chapter 1).
Reprinted by permission from Guadagnoli and Kohl 2001.
Together with the results of the trials-delay studies, these findings support a
strong role for processing inherent (intrinsic) feedback information that
leads, of course, to a score-estimation. It is important to remember that
retention tests in all of these studies were performed without augmented
feedback. Thus, the only information that a learner could use to check on
performance accuracy in numerous trials of retention was the inherent
sources of feedback that are always available, with inherent feedback being
the basis for trial-to-trial corrections. Conditions of practice that encourage
a more thorough appreciation and use of the information provided by
inherent feedback promote learning. These conditions are suited to
performance in both (a) these no-augmented-feedback tests and (b) most
real-world applications of these ideas.
Postfeedback Delay Intervals
After receiving feedback for one movement, in the postfeedback delay (see
figure 11.11) the learner attempts to create another movement that is at least
somewhat different from the previous one—a movement that will eliminate
the errors signaled by feedback. How much time is required for processing
this information, and how soon can the next movement begin? The research
on these questions shows that if this interval is too short (less than 5 s),
performance on the next trial will suffer, probably because of insufficient
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time to plan it. However, if the interval is somewhat longer (say, greater
than 5 s), there is no advantage to giving the learner even more time to plan
and generate the next movement (Weinberg, Guy, & Tupper, 1964). The
interval would probably be a little longer in a task that is relatively complex
or where many different decisions have to be made about alternative
movement strategies and methods. Overall, though, the postfeedback
interval is not particularly powerful in determining learning, and you can
focus on more important aspects of the learning environment.
Summary
A learner can receive various kinds of sensory information, but augmented
feedback about errors from the instructor is one of the most critical aspects
of the learning environment. This kind of information can have several
simultaneous roles: It can serve as an “energizer” to increase motivation; it
can provide information, in which case it signals the nature and direction of
errors and how to correct them; and it can produce a learner dependency, in
which case performance suffers when the information is withdrawn.
Feedback can take on many forms, such as videotape replays, films, and, of
course, verbal descriptions. Verbal feedback is best when it is simple and
refers to only one movement feature at a time, a movement feature that the
learner can control.
The largest errors can be corrected in early learning with frequent feedback.
After a few trials, however, learning is stronger if feedback frequency is
gradually reduced (i.e., faded) across practice. Summary feedback, whereby
a set of trial-results is described by a graph shown to the learner only after
the set is completed, is particularly effective for learning; but the optimum
number of trials included in the summary will decrease as task complexity
increases. An early principle—that anything making feedback more
frequent, accurate, and useful enhances learning—is being replaced by
newer viewpoints that focus on the subtle aspects of feedback’s nature and
scheduling.
Web Study Guide Activities
The student web study guide, available at
www.HumanKinetics.com/MotorLearningAndPerformance, offers these
activities to help you build and apply your knowledge of the concepts in this
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chapter.
Interactive Learning
Activity 11.1: Review the types of feedback by matching each with its
definition.
Activity 11.2: Given examples of feedback, determine whether each
represents knowledge of results or knowledge of performance.
Activity 11.3: Answer a series of questions that will help you
understand the effects of concurrent feedback and physical guidance.
Activity 11.4: Tie together concepts discussed throughout the textbook
by selecting labels to complete a conceptual model of motor
performance.
Principles-to-Application Exercise
Activity 11.5: The principles-to-application exercise for this chapter
prompts you to choose an activity and a learner along with an aspect of
the learner’s current performance. You will then create a strategy for
providing feedback to that learner in the most beneficial way possible.
Check Your Understanding
1. Define inherent and augmented feedback, highlighting the differences
between them. Define knowledge of results and knowledge of
performance, highlighting the differences between them. Provide an
example of each of these types of feedback that a beginner watercolor
artist might experience.
2. Briefly explain how each of the following can affect learning.
Frequency of feedback
Precision of feedback
Feedback schedules
Timing of feedback presentation
3. List and briefly discuss four properties of augmented feedback.
Apply Your Knowledge
1. A university wrestling coach is teaching his team some new ways to
finish a takedown. One wrestler has been competing for 10 years and
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is the current national champion, while another is in her first year of
university and has much less experience. Discuss some factors that the
coach might consider when providing feedback to each of the
wrestlers. Explain how the coach might provide feedback in order to
benefit learning of the new skills and transfer to a wrestling match.
2. A physical education teacher is beginning a new unit, teaching the game
of goalball to her high school class. (In the game of goalball each
player is blindfolded, and the ball used for play emits an auditory
signal.) Discuss three types of augmented feedback the teacher can use
to provide feedback to the students during the class. Would the amount,
precision, and frequency of feedback change from her earlier unit
teaching handball? If so, how would it change?
Suggestions for Further Reading
A thorough review of the published research on feedback and motor
learning by Salmoni, Schmidt, and Walter (1984) offers fuller details of the
principles discussed here. An excellent review of the guidance research
was written by Hodges and Campagnaro (2012). An important early review
on videotape replays for motor learning was presented by Rothstein and
Arnold (1976). And chapter 12 of Schmidt and Lee (2011) provides more
detail on each of the sections presented in this chapter. See the reference list
for these additional resources.
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Glossary
ability—A stable, enduring, mainly genetically-defined trait that underlies
skilled performance, is largely inherited, and is not modifiable by
practice.
absolute constant error (|CE|)—The absolute value of CE for a subject; a
measure of amount of bias without respect to its direction.
absolute error (AE)—The average absolute deviation of each of a set of
scores from a target value; a measure of overall error.
absolute frequency of feedback—The actual number of feedback
presentations given in a series of practice trials.
Adams (closed-loop) theory—A theory of motor learning proposed by
Adams (1971), focusing heavily on the learning of slow positioning
movements.
ambient vision—See dorsal stream.
amplitude—The distance between the two target centers in aiming tasks
(“A” in Fitts’ Law).
anti-phase—A coordination timing pattern in which two movement
components oscillate in opposition (180° relative phase).
arousal—An internal state of alertness or excitement.
attention—A limited capacity or set of capacities to process information.
augmented feedback—Information from the measured performance outcome
that is fed back to the learner by some artificial means; sometimes called
extrinsic feedback.
automatic processing—A mode of information processing that is fast, is
done in parallel, is not attention demanding, and is often involuntary.
autonomous stage—The third of three stages of learning proposed by Fitts,
in which the attention demands of performing a task have been greatly
reduced.
average feedback—A type of augmented feedback that presents a statistical
average of two or more trials, rather than results on any one of them.
bandwidth feedback—A procedure for delivering feedback in which errors
are signaled only if they fall outside some range of correctness.
blindsight—A medical condition in which the patient can respond to certain
visual stimuli while being judged legally blind by other criteria.
blocked practice—A schedule in which many trials on a single task are
practiced consecutively; low contextual interference.
capability—The internal representation of skill, acquired during practice,
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that allows performance on some task.
central pattern generator (CPG)—A centrally-located control mechanism
that produces mainly genetically defined actions such as walking.
choice reaction time—A variation in RT procedure in which the performer,
when a particular stimulus is given, must choose one response (the
“correct” response) from a number of possible predetermined responses;
the temporal interval between the presentation of a given stimulus and the
start of its associated response.
choking—Scenario in which a performer changes a normal routine or fails
to adapt to a changing situation, resulting in a failed performance.
closed-loop control—A type of system control involving feedback, error
detection, and error correction that is applicable to maintaining a system
goal.
closed skill—A skill for which the environment is stable and predictable,
allowing advance organization of movement.
cocktail-party effect—A phenomenon of attention in which humans can
attend to a single conversation at a noisy gathering, neglecting most (but
not all) other inputs.
cognitive stage of learning—The first of three stages of learning proposed
by Fitts, in which the learners’ performances are heavily based on
cognitive or verbal processes.
comparator—A component of closed-loop control that compares anticipated
feedback with actual feedback, finally outputting an error signal.
concurrent feedback—Augmented (usually continuous) feedback that is
presented simultaneously with an ongoing action.
constant error (CE)—The signed difference of a score on a given trial from
a target value; a measure of bias for that trial.
constant practice—A practice sequence in which only a single variation of a
given class of tasks is experienced.
contextual interference—The interference in performance and learning that
arises from performing one task in the context of other tasks; blocked
practice has low contextual interference, and random practice has high
contextual interference.
contextual-interference effect—The finding that groups of subjects who
practice under high contextual interference do not perform well relative
to blocked-practice subjects during acquisition, but outperform blocked-
practice subjects when evaluated in transfer or retention tests; see
contextual interference.
continuous skill—A task in which the action is performed without any
recognizable beginning or end.
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controlled processing—A mode of information processing that is relatively
slow, serial, attention demanding, and voluntary.
correlation coefficient (r)—A statistical method that evaluates the strength
of a relationship between two variables; it does not imply causality.
criterion task—The ultimate version, condition, or situation in which the
skill learned in practice is to be applied; the ultimate goal of practice.
cutaneous receptor—A receptor located in the skin that provides inherent
information about touch (haptic sensations).
deafferentation—A surgical procedure that involves cutting one or more of
an animal’s dorsal roots, preventing nerve impulses from the periphery
from traveling to the spinal cord.
degrees of freedom—The collection of separate movements of a system that
need to be controlled; see degrees-of-freedom problem.
degrees of freedom problem—The problem of explaining how a movement
with many degrees of freedom is controlled or coordinated; see degrees
of freedom.
demonstration—Performance of a skill by an instructor (or a model) to
facilitate observational learning.
differential method—A method of understanding behavior by focusing on
individual differences and abilities.
discrete skill—A task that has a recognizable beginning and end; usually
brief in duration.
distributed practice—A practice schedule in which the duration of rest
between practice trials is “relatively long”; the time in practice is often
less than the time at rest.
dorsal stream—Visual information, used specifically for the control of
movement within the visual environment, that is sent from the eye to the
posterior parietal cortex; sometimes called ambient vision.
double stimulation paradigm—A method for studying information
processing in which a given stimulus (leading to one response) is
followed closely by a second stimulus (leading to another response).
effective target width (We)—The amount of spread, or variability, of
movement end points about a target in an aiming task; represents the
performer’s “effective” target size; the within-subject standard deviation
of the movement distances for a set of trials.
elaboration hypothesis—The idea that frequent switching among tasks (e.g.,
in random practice) renders the tasks more distinct from each other and
more meaningful, resulting in stronger memory representations; one
explanation of the contextual-interference effect.
error-detection capability—The learned capability to detect one’s own
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errors through analyzing inherent feedback.
especial skills—A specific representation for one skill (e.g., free throw in
basketball) within a broader class of skills (e.g., set shots in basketball).
experimental method—A method of understanding behavior emphasizing
common principles among people and through the use of experiments.
external focus of attention—Attention directed outside the body to an object
or environmental goal.
exteroception—Sensory information arising primarily from outside the
body.
extrinsic feedback—See augmented feedback.
faded feedback—The practice in delivering feedback whereby the
frequency of feedback is decreased systematically across trials.
faded frequency—A feedback schedule in which the relative frequency is
high in early practice and reduced in later practice.
false-negative normative feedback—An experimental procedure in which
learners are (mis)informed that their performance is less skilled than that
of others.
false-positive normative feedback—An experimental procedure in which
learners are (mis)informed that their performance on some task is more
skilled than that of others.
far transfer—Transfer of learning from one task to another, very different
task or setting.
feedback—Information provided to the learner about the action just made;
often synonymous with augmented feedback.
feedback delay interval—The interval of time from the end of the movement
until the feedback is presented.
feedforward—Anticipated sensory consequences of movement that should
occur if the movement is correct.
Fitts’ Law—The principle that movement time in aiming tasks is linearly
related to the Log2(2A/W), where A = amplitude and W = target width.
fixation (associative or motor) stage—The second of three stages of
learning proposed by Fitts, in which learners establish motor patterns.
focal vision—See ventral stream.
foreperiod—In a reaction time task, the interval of time between a warning
signal and a stimulus to respond.
forgetting—The loss of an acquired capability for responding; loss of
memory.
forgetting hypothesis—The hypothesis that frequent task switching in
random practice causes forgetting of the planning done on the previous
trials, therefore leading to more next-trial planning and resulting in
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stronger memory representations; a hypothesis to explain the contextual-
interference effect.
gearshift analogy—A model regarding the learning of motor programs using
the analogy of learning to shift gears in a standard-transmission
automobile.
generalizability—The process of applying what is learned in the practice of
one task to one or more other unpracticed tasks.
generalized motor program (GMP)—A motor program whose output can
vary along certain dimensions to produce novelty and generalizability in
movement.
general motor ability—An older, incorrect view in which a single, general
ability was thought to underlie individual differences in motor behavior;
sometimes called motor educability.
goal setting—A motivational procedure in which the learner is encouraged
to set personal performance goals during practice.
Golgi tendon organs—Small stretch receptors located in the tendons that
provide precise information about muscle tension.
guidance—A procedure used in practice in which the learner is physically
or verbally directed through the performance in order to improve
performance.
guidance hypothesis—A view emphasizing the guidance properties of
augmented feedback, which promotes effective performance when it is
present but has dependency-producing (guidance-like) effects on retention
tests of learning.
Hick’s Law—The mathematical descriptor showing a linear relationship
between choice reaction time and the logarithm (to the base 2) of the
number of stimulus–response alternatives.
hypervigilance—A heightened state of arousal that leads to ineffective
decision making and poor performance; panic.
inattention blindness—A failure to perceive objects in the visual
environment when attention is directed to other objects or events.
index of difficulty (ID)—The theoretical “difficulty” of a movement in the
Fitts tapping task, or ID = Log2(2A/W), where A is target amplitude and W
is target width.
individual differences—Stable, enduring differences among people in terms
of some measurable characteristic (e.g., age) or performance of some task
(e.g., reaction time).
information-processing approach—Approaches to the study of behavior that
treat the human as a processor of information, focusing on storage,
coding, retrieval, and transformation of information.
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inherent feedback—Information provided as a natural consequence of
making an action; sometimes called intrinsic feedback.
in-phase—A coordination timing pattern in which two movement
components oscillate in synchrony (0° relative phase).
instantaneous feedback—Augmented feedback delivered immediately after
completion of movement (with no delay).
internal focus of attention—Attention directed to locations inside the body,
or to motor or sensory information.
interstimulus interval (ISI)—See stimulus-onset asynchrony.
intrinsic feedback—See inherent feedback.
invariant feature—A feature of a class of movements that remains constant,
or invariant, while surface features change (e.g., relative timing).
inverted-U principle—The principle that increased arousal improves
performance only to a point, with degraded performance as arousal is
increased further.
joint receptors—Sensory receptors located in the joint capsule that provide
information about joint position.
knowledge of performance (KP)—Augmented information about the
movement pattern the learner has just made; sometimes referred to as
kinematic feedback.
knowledge of results (KR)—Augmented verbal (or at least verbalizable)
information fed back to the learner about the success of an action with
respect to the environmental goal.
lead-up activities—Special tasks designed to be learned before the practice
of a more complicated or dangerous criterion task.
learner-determined feedback—A schedule in which the provision of
feedback is determined by the learner.
learning curves—A label sometimes applied to a performance curve (a plot
of average performance against trials), in the mistaken belief that the
changes in performance mirror changes in learning.
long-term memory (LTM)—A virtually limitless memory store for
information, facts, concepts, and relationships; presumably storage for
movement programs.
looked-but-failed-to-see accidents—Traffic accidents in which the driver
looked at, but failed to notice (to see) the presence of a cyclist or
pedestrian; believed to be related to inattention blindness.
M1 response—The monosynaptic stretch reflex, with a latency of 30 to 50
ms.
M2 response—The polysynaptic, or functional, stretch reflex, with a latency
of 50 to 80 ms.
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M3 response—The voluntary reaction-time response to a stimulus, with a
latency of 120 to 180 ms.
massed practice—A practice schedule in which the amount of rest between
practice trials is relatively short (often less than the time for a trial).
mental practice—A practice procedure in which the learner imagines
successful action without overt physical practice.
modeling—A practice procedure in which another person demonstrates the
skills to be learned.
motor learning—A set of internal processes associated with practice or
experience leading to relatively permanent gains in the capability for
skilled performance.
motor program—A prestructured set of movement commands that defines
the essential details of skilled action, with minimal (or no) involvement
of sensory feedback.
movement programming—The third stage of information processing in
which the motor system is readied for the planned action.
movement time (MT)—The interval from the initiation of a movement until
its termination.
muscle spindle—Structure located in parallel with muscle fibers that
provides information about muscle length.
near transfer—Transfer of learning from one task or setting to another that is
very similar.
novelty problem—The concern that simple theories cannot account for the
production of novel, unpracticed movements.
observational learning—The process by which the learner acquires the
capability for action by observing model demonstrations.
open-loop control—A type of system control in which instructions for the
effector system are determined in advance and run off without feedback.
open skill—A skill for which the environment is unpredictable or unstable,
preventing advance organization of movement.
optical array—The collection of rays of light that are reflected from objects
in the visual environment.
optical flow—The change in patterns of light rays from the environment as
they “flow” over the retina during continuous movement of the eye
through the environment, allowing perception of motion, position, and
timing.
parameterized—The process whereby parameters are supplied to the
generalized motor program to define its surface features.
parameters—Values applied to a generalized motor program that determine
a movement’s surface features, such as speed, amplitude, or limb used.
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part practice—A procedure in which a complex skill is broken down into
parts that are practiced separately.
perceptual narrowing—The tendency for the perceptual field to “shrink”;
sometimes called tunnel vision, or “weapon focus” in police work.
performance curve—Graphs of average performance for an individual or a
group plotted against practice trials; sometimes incorrectly called a
learning curve.
physical fidelity—The degree to which the surface features of a simulation
and the criterion task are identical.
population stereotypes—Habitual stimulus–response relationships that
dominate behavior due to specific cultural learning.
postfeedback delay—The interval of time between the presentation of
augmented feedback and the start of the next movement.
precision of feedback—The level of precision with which augmented
feedback describes the movement or outcome produced.
prediction—The process of using people’s abilities to estimate their
probable success in various situations.
probe-task technique—A method that uses an RT task as a secondary task
during the performance of a primary, criterion task to assess the attention
demands of the criterion task.
progressive part practice—A procedure in which parts of a skill are
gradually integrated into larger units during practice.
proprioception—Sensory information arising from within the body, resulting
in the sense of position and movement; sometimes called kinesthesis.
psychological fidelity—The degree to which the behaviors produced in a
simulator are identical to the behaviors required by the criterion task.
psychological refractory period (PRP)—The delay in responding to the
second of two closely spaced stimuli.
quiet-eye effect—The period of time when a performer fixates the eyes on a
target just before movement onset.
random (or interleaved) practice—A schedule in which practice trials on
several different tasks are mixed, or interleaved, across the practice
period; high contextual interference.
reaction time (RT)—The interval from presentation of an unanticipated
stimulus until the beginning of the response.
reference tests—Well-studied tests thought to measure abilities of various
kinds (e.g., reaction time, movement time, spatial relations).
reflex-reversal phenomenon—The phenomenon by which a given stimulus
can produce two different reflexive responses depending on the function
of the limb in a movement.
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relative-age effect—Phenomenon in which members of an age-normative
group who are born early in a given year are “relatively older” than
participants born late in the year.
relative frequency of feedback—The proportion of trials during practice on
which feedback is given; absolute frequency divided by the number of
trials.
relative timing—The temporal structure or rhythm of action; the durations of
various segments of an action divided by the total movement time.
repetition—A type of ineffective practice in which a movement is repeated
again and again.
response selection—The second stage of information processing in which
the system selects a response from a number of alternatives.
response time—The sum of reaction time plus movement time; sometimes
called total time.
retention test—A performance test on a given task provided after a retention
interval without practice; sometimes called a transfer test.
root-mean-square error (RMSE)—The square root of the average squared
deviations of a set of values from a target value; typically used as a
measure of overall tracking proficiency.
schema—A learned rule relating the outcomes of members of a class of
actions to the parameters that were used to produce those outcomes.
schema theory—A theory of motor control and learning based on
generalized motor programs and schemata.
self-organization—A view that describes motor control as emerging from
the interaction of the components of the movement system and the
environment.
self-regulation—Technique used in motor learning studies in which the
learners determine how to schedule practice or feedback or some other
aspect of scheduling.
sensory neuropathy—A medical condition in patients who are unable to
process and respond to most of their own sensory feedback.
serial skill—A task composed of several discrete actions strung together,
often with the order of actions being critical for success.
set—A collection of psychological activities or adjustments that underlie
performance but that can be “lost” after a rest.
short-term memory (STM)—A memory store with a capacity of about seven
elements, capable of holding information briefly (perhaps up to 30 s);
sometimes called “working memory.”
short-term sensory store (STSS)—A functionally limitless memory store for
holding literal, sensory information from the various senses very briefly
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(for only about 1 s).
simple RT—A reaction-time situation in which there is only one possible
stimulus and one response.
simulator—A training device that mimics various features of some real-
world task.
skill—The capability to bring about an end result with maximum certainty,
minimum energy, or minimum time; task proficiency that can be modified
by practice.
spatial anticipation—The anticipation of which of several possible stimuli
will occur; sometimes called event anticipation.
specificity hypothesis—The hypothesis that individual differences are based
on many independent abilities.
specificity of individual differences—A view of motor abilities holding that
tasks are composed of many unrelated abilities.
specificity of learning—A view that what you practice is what you learn.
See specificity hypothesis.
speed–accuracy trade-off—The tendency for accuracy to decrease as the
movement speed or velocity of a movement increases and vice versa.
startle RT—A rapid (<100 ms latency) reaction to an unexpected, often very strong, stimulus; used to study the involuntary release of motor programs. stimulus-onset asynchrony (SOA)—The interval between the onsets of the two stimuli in a double-stimulation paradigm; sometimes called the interstimulus interval (ISI). stimulus identification—The first stage of information processing in which a stimulus is recognized and identified. stimulus–response (S-R) compatibility—The degree of “naturalness” (or directness) between the stimulus and the response assigned to it. storage problem—The concern that simple program theories would require an almost limitless storage capacity for nearly countless different movements. summary feedback—Information about the effectiveness of performance on a series of trials that is presented only after the series has been completed. superability—A weak general ability thought to contribute to all tasks. surface feature—An easily changeable aspect of a movement, such as movement time or amplitude, that does not affect the “deep structure” (the invariant features). sustained attention—Maintenance of attention over long periods of work, such as monitoring a radar-based aircraft detection device; sometimes called vigilance. 510 Tau (τ)—A variable providing optical information about time-to-contact; the size of the retinal image divided by the rate of change of the image. temporal anticipation—The anticipation of when a given stimulus will arrive or when a movement is to be made. tracking—A class of tasks in which a moving track must be followed, typically by movements of a manual control. transfer of learning—The gain or the loss in proficiency on one task as a result of practice or experience on another task. transfer test—A performance test in which the task or task conditions have changed; often provided after a retention interval without practice. trials delay of feedback—A procedure in which the presentation of feedback for a movement is delayed; during the delay the learner practices one or more other trials of the same task. triggered reactions—Coordinated, learned reactions to perturbations that are manifest in large segments of the body; the triggered reaction has a latency shorter than RT yet longer than the long-loop reflex (50 to 80 ms). unintended acceleration—Sudden, uncommanded, violent acceleration of a vehicle accompanied by the perception of a loss of braking effectiveness. variable error (VE)—The standard deviation of a set of scores about the subject’s own average (CE) score; a measure of movement (in)consistency. variable practice—A schedule of practice in which many variations of a class of actions are practiced. ventral stream—Information useful for the identification of an object that is sent to the inferotemporal cortex; sometimes called focal vision. vestibular apparatus—Receptors in the inner ear that are sensitive to the orientation of the head with respect to gravity, to rotation of the head, and to balance. warm-up decrement—Temporary worsening of performance that is brought on by the passage of time away from a task and that is eliminated quickly when the performer begins again. whole practice—A procedure in which a skill is practiced in its entirety, without separation into its parts. width—The size of a target in aiming tasks (“W” in Fitts' Law). yoking—A type of control procedure in which a practice schedule is determined by (and matched to) a learner in a different experimental group or condition. 511 References Abbs, J.H., Gracco, V.L., & Cole, K.J. 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Known as one of the leaders in research on motor behavior, Dr. Schmidt has more than 35 years of experience in this area and has published widely. The originator of schema theory, Schmidt founded the Journal of Motor Behavior in 1969 and was editor for 11 years. He authored the first edition of Motor Control and Learning in 1982 and the first edition of Motor Learning and Performance in 1991. Both texts are now in their fifth edition. Schmidt received an honorary doctorate from Catholic University of Leuven, Belgium, in recognition of his work. He is a member of the North American Society for the Psychology of Sport and Physical Activity (NASPSPA), where he served as president in 1982 and received the organization’s two highest honors: the Distinguished Scholar Award for lifetime contributions to research in motor control and learning (in 1992) and the President’s Award for significant contributions to the development and growth of NASPSPA (in 2013). He is also a member of the Human Factors and Ergonomics Society and the Psychonomic Society and received the C.H. McCloy Research Lectureship from the American Alliance for Health, Physical Education, Recreation and Dance. His leisure-time activities include sailboat racing, amateur Porsche racing, and skiing. Timothy D. Lee, PhD, is a professor in the department of kinesiology at McMaster University in Hamilton, Ontario, Canada. He has published extensively in motor behavior and psychology journals since 1979. More recently, he has contributed as an editor to both Journal of Motor Behavior and Research Quarterly for Exercise and Sport and as an editorial board member for Psychological Review. Since 1984 his research has been supported by grants from the Natural Sciences and Engineering Research Council of Canada. Lee is a member and past president of the Canadian Society for 542 Psychomotor Learning and Sport Psychology (SCAPPS) and a member of the North American Society for the Psychology of Sport and Physical Activity (NASPSPA), the Psychonomic Society, and the Human Factors and Ergonomics Society. In 1980 Lee received the inaugural Young Scientist Award from SCAPPS, and in 2011 he was named a fellow of the society— its highest honor. In 1991-92 he received a senior research fellowship from the Dienst Onderzoekscoordinatie, Catholic University in Leuven, Belgium, and in 2005 he presented a prestigious senior scientist lecture at NASPSPA. In his leisure time, Lee enjoys playing hockey and golf. He has maintained a lifelong fascination with blues music and is currently putting years of research into practice by learning to play blues guitar. Timothy D. Lee, PhD (left) and Richard A. Schmidt, PhD (right) 543 544 目录 Cover Page 2 Motor Learning and Performance 3 Copyright Page 4 Check Out the Web Study Guide! 6 Dedication 7 Contents 8 Preface 11 Student and Instructor Resources 16 Acknowledgments 18 Credits 19 Chapter 1: Introduction to Motor Learning and Performance 28 Part I: Principles of Human Skilled Performance 56 Chapter 2: Processing Information and Making Decisions 57 Chapter 3: Attention and Performance 93 Chapter 4: Sensory Contributions to Skilled Performance 133 Chapter 5: Motor Programs 178 Chapter 6: Principles of Speed, Accuracy, and Coordination 232 Chapter 7: Individual Differences 275 Part II: Principles of Skill Learning 312 Chapter 8: Introduction to Motor Learning 313 Chapter 9: Skill Acquisition, Retention, and Transfer 350 Chapter 10: Organizing and Scheduling Practice 400 Chapter 11: Augmented Feedback 447 Glossary 501 545 About the Authors 542 Back Page Ad 544 546 Cover Page Motor Learning and Performance Copyright Page Check Out the Web Study Guide! Dedication Contents Preface Student and Instructor Resources Acknowledgments Credits Chapter 1: Introduction to Motor Learning and Performance Part I: Principles of Human Skilled Performance Chapter 2: Processing Information and Making Decisions Chapter 3: Attention and Performance Chapter 4: Sensory Contributions to Skilled Performance Chapter 5: Motor Programs Chapter 6: Principles of Speed, Accuracy, and Coordination Chapter 7: Individual Differences Part II: Principles of Skill Learning Chapter 8: Introduction to Motor Learning Chapter 9: Skill Acquisition, Retention, and Transfer Chapter 10: Organizing and Scheduling Practice Chapter 11: Augmented Feedback Glossary References About the Authors Back Page Ad 2 Check Out the Web Study Guide! You will notice a reference throughout this version of Life Span Motor Development, Sixth Edition, to a web study guide. This resource is available to supplement your e-book. The web study guide offers lab activities and video clips that will be used for application of key concepts and for hands-on assessment using the guidelines presented in the book. We are certain you will enjoy this unique online learning experience. Follow these steps to purchase access to the web study guide: 1. Visit www.tinyurl.com/BuyLIfeSpanMotorDevelop6EWSG. 2. Click the Add to Cart button and complete the purchase process. 3. After you have successfully completed your purchase, visit the book’s website at www.HumanKinetics.com/LifeSpanMotorDevelopment6E. 4. Click the sixth edition link next to the corresponding sixth edition book cover. 5. Click the Sign In link on the left or top of the page and enter the e-mail address and password that you used during the purchase process. Once you sign in, your online product will appear in the Ancillary Items box. Click on the title of the web study guide to access it. 6. Once purchased, a link to your product will permanently appear in the menu on the left. All you need to do to access your web study guide on subsequent visits is sign in to www.HumanKinetics.com/LifeSpanMotorDevelopment6E and follow the link! Click the Need Help? button on the book’s website if you need assistance along the way. 3 Life Span Motor Development Sixth Edition Kathleen M. Haywood, PhD University of Missouri-St. Louis Nancy Getchell, PhD University of Delaware Human Kinetics 4 Library of Congress Cataloging-in-Publication Data Haywood, Kathleen, author. Life span motor development / Kathleen M. Haywood, Nancy Getchell. -- Sixth edition. p. ; cm. Includes bibliographical references and index. I. Getchell, Nancy, 1963- author. II. Title. [DNLM: 1. Motor Skills. 2. Human Development. WE 103] RJ133 612.7'6--dc23 2013044126 ISBN: 978-1-4504-5699-9 (print) Copyright © 2014, 2009, 2005, 2001 by Kathleen M. Haywood and Nancy Getchell Copyright © 1993, 1986 by Kathleen M. Haywood All rights reserved. Except for use in a review, the reproduction or utilization of this work in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including xerography, photocopying, and recording, and in any information storage and retrieval system, is forbidden without the written permission of the publisher. Permission notices for material reprinted in this book from other sources can be found on page(s) xv-xx. The web addresses cited in this text were current as of February, 2014, unless otherwise noted. Acquisitions Editor: Myles Schrag Developmental Editor: Christine M. Drews Managing Editor: Susan Huls Assistant Editor: Melissa J. Zavala Copyeditor: Amanda M. 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Box 5076 Champaign, IL 61825-5076 800-747-4457 e-mail: humank@hkusa.com Canada: Human Kinetics 475 Devonshire Road Unit 100 Windsor, ON N8Y 2L5 800-465-7301 (in Canada only) 5 e-mail: info@hkcanada.com Europe: Human Kinetics 107 Bradford Road Stanningley Leeds LS28 6AT, United Kingdom +44 (0) 113 255 5665 e-mail: hk@hkeurope.com Australia: Human Kinetics 57A Price Avenue Lower Mitcham, South Australia 5062 08 8372 0999 e-mail: info@hkaustralia.com New Zealand: Human Kinetics P.O. Box 80 Torrens Park, South Australia 5062 0800 222 062 e-mail: info@hknewzealand.com E5970 6 To the many motor development researchers (past, present, and future) who continue to push our field forward. Your work inspires ours. 7 Contents Preface What’s New in the Sixth Edition Features of the Book Organization Ancillaries Acknowledgments Credits Part I: Introduction to Motor Development Chapter 1: Fundamental Concepts Defining Motor Development Constraints: A Model for Studying Motor Development How Do We Know It Is Change? A Developmental Paradox: Universality Versus Variability Summary and Synthesis Chapter 2: Theoretical Perspectives in Motor Development Maturational Perspective Information Processing Perspective Ecological Perspective Summary and Synthesis Chapter 3: Principles of Motion and Stability Understanding the Principles of Motion and Stability Using the Principles of Motion and Stability to Detect and Correct Errors Summary and Synthesis Part II: Physical Growth and Aging Chapter 4: Physical Growth, Maturation, and Aging Prenatal Development Postnatal Development Summary and Synthesis Chapter 5: Development and Aging of Body Systems Development of the Skeletal System Development of the Muscular System Development of the Adipose System Development of the Endocrine System 8 Development of the Nervous System Summary and Synthesis Part III: Development of Motor Skills Across the Life Span Chapter 6: Early Motor Development How Do Infants Move? Why Do Infants Move? The Purpose of Reflexes Motor Milestones: The Pathway to Voluntary Movements Development of Postural Control and Balance in Infancy Summary and Synthesis Chapter 7: Development of Human Locomotion The First Voluntary Locomotor Efforts: Creeping and Crawling Walking Across the Life Span Running Across the Life Span Other Locomotor Skills Summary and Synthesis Chapter 8: Development of Ballistic Skills Overarm Throwing Kicking Punting Sidearm Striking Overarm Striking Summary and Synthesis Chapter 9: Development of Manipulative Skills Grasping and Reaching Catching Anticipation Summary and Synthesis Part IV: Perceptual-Motor Development Chapter 10: Sensory-Perceptual Development Visual Development Kinesthetic Development Auditory Development Intermodal Perception Summary and Synthesis Chapter 11: Perception and Action in Development The Role of Action in Perception Postural Control and Balance Summary and Synthesis 9 Part V: Functional Constraints to Motor Development Chapter 12: Social and Cultural Constraints in Motor Development Social and Cultural Influences as Environmental Constraints Other Sociocultural Constraints: Race, Ethnicity, and Socioeconomic Status Summary and Synthesis Chapter 13: Psychosocial Constraints in Motor Development Self-Esteem Motivation Summary and Synthesis Chapter 14: Knowledge as a Functional Constraint in Motor Development Knowledge Bases Memory Speed of Cognitive Functions Summary and Synthesis Part VI: Interaction of Exercise Task and Structural Constraints Chapter 15: Development of Cardiorespiratory Endurance Physiological Responses to Short-Term Exercise Physiological Responses to Prolonged Exercise Summary and Synthesis Chapter 16: Development of Strength and Flexibility Development of Strength Development of Flexibility Summary and Synthesis Chapter 17: Development of Body Composition Body Composition and Exercise in Children and Youths Body Composition and Exercise in Adults Obesity Summary and Synthesis Chapter 18: Conclusion: Interactions Among Constraints Using Constraints to Enhance Learning in Physical Activity Settings Interacting Constraints: Case Studies Summary and Synthesis Appendix: Skinfold, Body Mass Index, and Head Circumference Charts References About the Authors 10 Preface Every day, you move. This doesn’t happen in a vacuum, though. Every movement you make occurs within your surrounding environment, whether you are at home, in the gymnasium, or on a ball field. You also move for a reason; the activities or tasks you perform have specific requirements or rules. The way you move has changed a great deal from when you were an infant, and it will keep changing throughout your entire life. This is the essence of the study of motor development: observing changes in movement across the life span, then determining why these movements change in the ways they do. In this edition, we focus on the model of constraints to help you understand why movements change. The model of constraints accounts for several factors: the individual or mover, the environment in which he or she moves, and the task that he or she has undertaken as well as the interactions between these constraints. It also accounts for why movement changes as the body and individual characteristics change over the life span. It allows us to anticipate how we might change movements by altering the environment, the task, or both (something that future physical educators, physical and occupational therapists, and other practitioners will find quite useful). Using the model of constraints will help you gain a more complete view of motor development across the life span and a means for solving motor development problems long after you finish your course. In fact, understanding life span motor development will assist your progress in all movement- related fields. You will learn what motor development is as well as the theoretical and historical roots of the field. You will also observe many factors related to development of movement skills, such as growth, aging, and perception. In addition, you will discover how constraints and other factors can encourage or discourage various movements—perhaps in ways you hadn’t thought about yet! Who can benefit from reading this text? Many people interested in movement can benefit. Educators at all levels—from early childhood educators to gerontologists—can enhance their teaching by becoming aware of various systems of the body and how they change over time. Persons in the health sciences, such as physical and occupational therapists, will find tools that assist them in observing patterns of movement. Readers in exercise science will receive guidance from the descriptions and explanations of developmental physical fitness and of processes underlying change. However, you don’t need extensive experience in movement studies to profit from this text. Because all people go through these developmental changes during their lives, all people can benefit from understanding more about motor development. In fact, parents and future parents will gain understanding of the motor development of their children and how to foster healthful motor development. Thus, many readers of this text will be able to use the information personally no matter how extensively they use it in their professional roles. 11 What’s New in the Sixth Edition The sixth edition of Life Span Motor Development and its ancillaries further emphasize the use of motor development information in the real world. For example, each chapter begins with an example of a common experience related to motor development, and this experience is revisited at the end of the chapter, giving the reader the opportunity to consider the experience in light of the material covered in the chapter. Application questions throughout the chapters engage readers in considering how parents or professionals in various roles might use the material discussed. The number and variety of lab activities, delivered on the website, has been increased, and more than 130 new videos are included to provide more opportunities for student-centered learning and, in some cases, to choose projects related to their career goals. The content in the sixth edition has been updated to reflect new research in the field. The material added to chapter 18 is intended to help practitioners bridge the gap between the model of constraints and structuring learning environments to help their students and clients progress. Readers are shown how to expand the model of constraints to focus on key constraints for a given skill or task and how to scale that constraint to make a learning environment more or less challenging to fit the individual. Readers are also shown how to relate the constraints models to ecological task analyses and how these analyses can be used to assess learners and clients. 12 Features of the Book The sixth edition of Life Span Motor Development continues several important features of previous editions. Motor development in the real world. To remind you that motor development is a common part of our human experience, we begin each chapter with a real-life experience—something that is a common experience for many of us or something we see reported frequently in the media. Chapter objectives. The chapter objectives list the most important concepts you should learn and understand in each chapter. Running glossary. A running glossary appears in the margins throughout each chapter. The running glossary words are highlighted in the text with a second color so that you know to check the margin definition. Some terms don’t require a margin definition because they are defined sufficiently in the text. Those words are boldfaced to help you locate them. This is an example of a running glossary word; it is highlighted in color in the text and is defined in the margin. Application questions. We distribute application questions as shown here, throughout the chapters. These questions challenge you to consider how parents or professionals could use the material under discussion in a real-world situation. Who can benefit from learning about motor development? Key points. Key points appear periodically throughout the chapter to point out the theme of a discussion or a broad conclusion amidst chapter details. These key points are identified by the icon to the left. Key Point Key points look like this and can be found in the margins of the text. Cues to go to the web study guide for lab activities. Each chapter includes cues, such as the example below, to the lab activities in the web study guide. If you purchase a print book, you received access to the web study guide via a key code. If you purchased a used print book or an e-book that does not provide access to the web study guide, you will want to purchase the web study guide so that you have access to 13 the lab activities and videos. WEB STUDY GUIDE Gain experience in observing movement by doing Lab Activity 1.1, Observation as a Tool of Inquiry, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Assessment elements. Most chapters contain an assessment element that will aid you in observing and assessing some aspect of motor development. These elements also help you understand how researchers and other professionals measure certain aspects of motor development. Summary. This section appears toward the end of a chapter and offers a concise wrap-up of the material presented in the chapter. Reinforcement of chapter content. To help you integrate the concepts you’ve learned throughout the chapter into a constraints perspective, each chapter ends with a section titled “Reinforcing What You Have Learned About Constraints.” Here you’ll find subsections that encourage you to take a second look at the real-life experience that opened the chapter, test your knowledge by answering questions about the material, and complete learning exercises that provide hands-on ways to apply what you have learned. Assistance in meeting national standards. The sixth edition continues to assist students in meeting national standards. The text and the approach are consistent with the revised guidelines for minimum competencies drafted by the Motor Development Academy of the National Association for Sport and Physical Education, which is part of SHAPE America (formerly the American Alliance for Health, Physical Education, Recreation and Dance). For those preparing to teach, the sixth edition provides guidelines for constructing developmentally appropriate activities and designing learning activities to help your students meet grade-level expectations. 14 Organization Part I contains three chapters of fundamental concepts. Key to part I is the introduction of Newell’s constraints model, around which the entire text is organized. Chapter 1 defines terms and methods of study in motor development. Chapter 2 offers a historical and theoretical overview of the field. Chapter 3 introduces the principles of motion and stability that underlie all movements and guide motor development. Part II examines two important individual constraints—physical growth and aging—of the body as a whole and of specific body systems as well as how they change over the life span. Part III describes changing motor patterns over the life span. Chapter 6 explores motor development during infancy. Chapter 7 examines the development of locomotor skills, chapter 8 the development of ballistic skills, and chapter 9 the development of manipulative skills. These chapters provide much detail related to the specific sequence of changes one sees through development. The remaining parts focus on additional constraints that influence the movement arising from the interaction of these constraints, emphasizing those that change over the life span. Part IV focuses on sensation and perception as they interact with action, and part V focuses on social, cultural, and psychosocial influences as well as the effect of knowledge on movement. Part VI looks at four important components of physical fitness and how these constrain movement as individuals change over the life span. Many professionals apply adult training principles to children, youths, and older adults without appreciating their unique constraints. These chapters present developmentally appropriate guidelines. For those concerned about the obesity epidemic among children and youths, part VI contains useful information. The concluding chapter encourages readers to apply what they have learned about changing constraints and their interactions to actual people and situations. We expanded this chapter to provide enhanced opportunities for readers to reflect on all they have learned about motor development over the life span and use that knowledge in specific cases. 15 Ancillaries The sixth edition of Life Span Motor Development includes a web study guide for students as well as an instructor guide, presentation package plus image bank, test package, and chapter quizzes for instructors. The web study guide contains learning exercises and lab activities for every chapter. These exercises and activities might be done during class, in the laboratory, or as homework assignments. We modified the existing lab activities so that the instructions are easier to understand and more streamlined. New to this edition, lab activity record sheets and questions are available as fillable documents so that students can complete and submit them electronically, resulting in increased efficiency and reduced paperwork for instructors. The lab activities include video clips so that instructors whose classes cannot perform live observations will still be able to use all of the labs. Clips include footage of infants and toddlers; additional footage of people performing the fundamental motor skills; and context-specific, constraint-rich video for chapters 1 through 18. The instructor guide includes an overview, list of major concepts, suggested class activities, and suggested readings for each chapter. It has been revised to provide more information to guide the instructor through the laboratory activities and learning exercises. Answers are provided for the lab activities that require students to assess performers in video clips. The test package, created with Respondus 4.0, includes a bank of more than 490 questions created especially for this edition of Life Span Motor Development. Question types include true-false, fill in the blank, short answer, essay, and multiple choice. Answers are provided for some of the short-answer and essay questions, although you will likely want your students’ answers to these questions to reflect the particular discussions held by your class. With Respondus LE, a free version of the Respondus software, instructors can create print versions of their own tests by selecting from the question pool; create, store, and retrieve their own questions; select their own test forms and save them for later editing or printing; and export the test forms into a word-processing program. Chapter quizzes provide ready-made quizzes for each chapter. The chapter quiz will help instructors assess students’ comprehension of the most important concepts in the chapter. Each quiz contains 10 questions. The presentation package includes a comprehensive series of PowerPoint slides for each chapter. The slides highlight the most important concepts from the book and include selected video clips from the lab activities. We have tried to include something from every 16 section of each chapter. Depending on the particular emphasis an instructor wants to give the chapter—or on the background and expertise of the students—instructors may want to delete some slides or embellish others. The image bank includes most of the text’s art, photos, and tables. A blank PowerPoint template is also provided so that instructors can customize or create a PowerPoint presentation if they so choose. Easy-to-follow instructions are included. The web study guide, instructor guide, test package, chapter quizzes, and presentation package plus image bank are all accessible at www.HumanKinetics.com/LifeSpanMotorDevelopment. 17 Acknowledgments The Life Span Motor Development project began in 1983. Each edition and each addition to what has now become an instructional package reflect the contributions of many. We acknowledge all of those contributions here. Any undertaking this ambitious could be completed only with the help and support of those contributing their unique expertise and talents. This sixth edition continues to expand on the previous editions, and contributions anywhere along the way have made this work what it is today. First, we extend our appreciation to those who have appeared in photographs or video clips over the years: Jennifer, Douglas, and Michael Imergoot; Laura, Christina, and Matthew Haywood; Anna Tramelli; Cathy Lewis; Jules Mommaerts; Connor Miller; Franklin McFarland; Rachel Harmon; Jessica Galli; Charmin Olion; Chad Hoffman; Stephanie Kozlowski; Julia and Madeleine Blakely; Valeria Rohde; Ian Stahl; Amelia Isaacs; Reese and Nicholas Rapson; Janiyah Bell; Parker Lehn; Logan Allen; Jane Laskowski; Rachel and Jessica Wiley; Diane Waltermire; Jase Elliott; Susan Allen; Susan and Lisa Miller; Emily and Jeremy Falkenham, Sarah Poe; Alex Mitsdarfer; Lyna Buzzard; Blair Mathis; Parker, Addison, Zander, and Willow Burk; Robert, James, and Lillian Hall; Susan Outlaw; Julie and Johnathan Lyons; Grace and Dylan Taylor; Emily and Abram George; Reid and Madi Henness; Kaleb Curry; Kelly Taylor; Luci and Lynette Morris; Tony and Mary Graham; William Gingold; Anna Clark; Sarah, Mary, and Grace White; Neil Hollwedel; Daniel Fishel; Cole and Logan Hasty; Marcia Siebert; and Joshua, Shagna, and Holden Stone; among others. Next, we acknowledge friends who took or donated some of the photographs, in this or previous editions: Brian Speicher, William Long, Rosa Angulo-Barroso, Susan Miller, Dale Ulrich, Mary Ann Roberton, Ann VanSant, John Haubenstricker, B.D. Ulrich, and Jill Whitall, contributed film tracings from which some of the art was drawn. Additional thanks to Mary Ann Roberton for supplying a great picture of Hal in action. The content of Life Span Motor Development touches many subdisciplines and specialty areas of study. We extend our thanks to John Strupel, the late Elizabeth Sweeney, Bruce Clark, Jane Clark, Maureen Weiss, Kathleen Williams, Ann VanSant, and Mary Ann Roberton, who were all kind enough to read sections of the text or contribute information. Thanks to Paul M. Spezia, DO of St. Peters Bone and Joint Surgery, who provided wrist X rays for the laboratory exercise on skeletal age. Ann Wagner and Cynthia Haywood Kerkemeyer helped by keyboarding and checking parts of the earlier editions, and the late Lynn Imergoot, Linda Gagen, Patricia Hanna, and Cathy Lewis were kind enough to help with the index of an earlier edition. 18 We especially thank our many colleagues in motor development who have made suggestions along the way. Their dedication to helping students of motor development appreciate this area of study is always an inspiration. Last but not least, we appreciate the patience and dedication of our team at Human Kinetics for keeping us on track through the many facets of the sixth edition in the face of a tight schedule, and Judy Wright, who helped nurture early editions of Life Span Motor Development. We also acknowledge our families and friends, who have prevailed through many revisions of this textbook. Their continued support over the years makes our work possible. 19 Credits Figure 1.2 KMSP/DPPI/Icon SMI. Figure 1.3 Reprinted from A. Maclaren, ed., 1967, Advances in reproductive physiology, Vol. 2 (London: Elek Books). By permission of Jonathan Michie. Figure 2.1 – Courtesy of the authors. Figure 2.2 – Courtesy of the authors. Figure 2.3 Adapted, by permission, from E. Thelen, B.D. Ulrich, and J. L. Jensen, 1989, The developmental origins of locomotion. In Development of posture and gait across the life span, edited by M.H. Woolacott and A. Shumway-Cook (Columbia, SC: University of South Carolina Press), 28. Chapter 3 Opener Anthony Stanley/Action Plus/Icon SMI. Text 3.1 Based on www.aimeemullens.com. Figure 3.2 Reprinted from J.E. Donnelly, 1990, Living anatomy, 2nd ed. (Champaign, IL: Human Kinetics), 32. Permission by J.E. Donnelly. Table 4.1 Adapted, by permission, from P.S. Timiras, 1972, Developmental physiology and aging (New York; Macmillan), 63-64. Figure 4.3 Reprinted, by permission, from P. Rhodes, 1969, Reproductive physiology for medical students (London: J. & A. Churchill Ltd.), 191. Figures 4.4a and b Data from the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion 2000. Adapted from www.cdc.gov/nchs/about/major/nhanes/growthcharts.clinical_charts.htm. Figures 4.5a and b Data from the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion 2000. Adapted from www.cdc.gov/nchs/about/major/nhanes/growthcharts.clinical_charts.htm. Figure 4.7 Adapted, by permission, from P.S. Timiras, 1972, Developmental physiology and aging (New York: Macmillan), 284. Figures 4.8a, b, and c Reprinted from A. Maclaren, ed., 1967, Advances in reproductive physiology, Vol. 2 (London: Elek Books). By permission of Jonathan Michie. 20 Figure 4.10 Reprinted, by permission, from W.W. Spirduso, 1995, Physical dimensions of aging (Champaign, IL: Human Kinetics), 59. Adapted, by permission, from A.R. Frisancho, 1990, Anthropometric standards for the assessment of growth and nutritional status (Ann Arbor: University of Michigan Press), 27. Figure 4.12 Reprinted, by permission, from W.W. Spirduso, 1995, Physical dimensions of aging (Champaign, IL: Human Kinetics), 59. Adapted, by permission, from A.R. Frisancho, 1990, Anthropometric standards for the assessment of growth and nutritional status (Ann Arbor: University of Michigan Press), 27. Figure 5.1 Printed in U.S.A. © Carolina Biological Supply Company. Reproduction of all or any part of this material without written permission from the copyright holder is unlawful. Figure 5.3 Printed in U.S.A. © Carolina Biological Supply Company. Reproduction of all or any part of this material without written permission from the copyright holder is unlawful. Figures 5.4a and b Reprinted, by permission, from S.I. Pyle, 1971, A radiographic standard of reference for the growing hand and wrist (Chicago: Year Book Medical), 53, 73. Copyright Bolton-Brush Growth Study-B.H. Broadbent D.D.S. Figure 5.8 From R.M. Malina and C. Bouchard, 1988, Subcutaneous fat distribution during growth. In Fat distribution during growth and later health outcomes, edited by C. Bouchard and F.E. Johnston (New York: Liss), 70. Copyright © 1988 by Alan R. Liss, Inc. Reprinted by permission of Wiley-Liss, a division of John Wiley and Sons, Inc. Text 6.1 Reprinted, by permission, from livescience. Available: www.livescience.com/23572-assistive-robotics-aide-young-children-nsf-bts.html Figure 6.2a Geoff Kirby/Press Association Images. Figure 6.2b © D.A. Weinstein / Custom Medical Stock Photo. Figure 6.4 Photo courtesy of Dale Ulrich. Figure 6.7 Reprinted from B.I. Bertenthal, J.L. Rose, and D.L. Bai, 1997, “Perception- action coupling in the development of visual control of posture,” Journal of Experimental Psychology: Human Perception and Performance 23: 1631-1634, fig. 1. Copyright © by the American Psychological Association. Reprinted with permission. Figures 7.2a, b, and c Parts a and c are drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin-Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling 21 Green State University. © Mary Ann Roberton. Part b is adapted from R.L. Wickstrom, 1983, Fundamental motor patterns, 3rd ed. (Philadelphia: Lea & Febiger), 29. © Mary Ann Roberton and Kate R. Barrett. Figures 7.4a and b Part a is drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin-Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Part b is redrawn from R.L. Wickstrom, 1983, Fundamental motor patterns, 3rd ed. (Philadelphia: Lea & Febiger). © Mary Ann Roberton and Kate R. Barrett. Figure 7.5 Adapted, by permission, from R.L. Wickstrom, 1983, Fundamental motor patterns, 3rd ed. (Philadelphia: Lea & Febiger), 29. © Mary Ann Roberton and Kate R. Barrett. Table 7.2 Reprinted, by permission, from R.L. Wickstrom, 1983, Fundamental motor patterns, 3rd ed. (Philadelphia: Lea & Febiger) 68. © Mary Ann Roberton and Kate R. Barrett. Table 7.3 Reprinted, by permission, from R.L. Wickstrom, 1983, Fundamental motor patterns, 3rd ed. (Philadelphia: Lea & Febiger) 68. © Mary Ann Roberton and Kate R. Barrett. Table 7.4 Adapted, by permission, from V. Seefeldt, S. Reuschlein, and P. Vogel, 1972, Sequencing motor skills within the physical education curriculum. Paper presented to the annual conference of the American Association for Health, Physical Education and Recreation. © Vern D. Seedfeldt. Table 7.5 Adapted, by permission, from J.E. Clark and S.J. Phillips, 1985, A developmental sequence of the standing long jump. In Motor development: Current selected research, Vol. 1, edited by J.E. Clark and J.H. Humphrey (Princeton Book), 76- 77. Copyright 1985 by Princeton Book Company, Publishers. Figure 7.7 Adapted, by permission, from R.L. Wickstrom, 1983, Fundamental motor patterns, 3rd ed. (Philadelphia: Lea & Febiger), 74. © Mary Ann Roberton and Kate R. Barrett. Figure 7.8 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin-Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 7.10 Adapted, by permission, from R.L. Wickstrom, 1983, Fundamental motor patterns, 3rd ed. (Philadelphia: Lea & Febiger), 77. © Mary Ann Roberton and Kate R. 22 Barrett. Figure 7.13 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 7.14 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Table 7.6 Reprinted, from M.A. Roberton and L.E. Halverson, 1984, Developing children: Their changing movement (Philadelphia: Lea & Febiger), 56-63. By permission of Mary Ann Roberton. Figure 7.15 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 7.16 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 7.17 “Development sequences for hopping over distance: A prelongitudinal screening,” L. Haleverson and K. Williams, Research Quarterly for Exercise and Sport, 56:38, 1985, reprinted by permission of the American Alliance for Health, Physical Education, Recreation and Dance (www.AAHPERD.org). Figures 7.19a and b Adapted, by permission, from J.E. Clark and J. Whitall, 1989, Changing patterns of locomotion: From walking to skipping. In Development of posture and gait across the life span, edited by M.H. Woollacott and A. Shumway-Cook (Columbia, SC: University of South Carolina Press), 132. Figure 7.20 Adapted, by permission from J.E. Clark and J. Whitall, 1989, Changing patterns of locomotion: From walking to skipping. In Development of posture and gait across the life span, edited by M.H. Woollacott and A. Shumway-Cook (Columbia, SC: University of South Carolina Press), 132. Figure 8.1 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © 23 Mary Ann Roberton. Figure 8.2 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin-Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton and Kate R. Barrett. Table 8.1 Reprinted from M.A. Roberton and L.E. Halverson, 1984, Developing children: Their changing movement (Philadelphia: Lea & Febiger), 103, 106-107, 118, 123. By permission of Mary Ann Roberton. Figure 8.3 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.4 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.5 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.6 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.7 The line drawings within this Observation Plan are: Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin– Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.8 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.9 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © 24 Mary Ann Roberton. Figure 8.10 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.11 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.12 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Table 8.2 Reprinted from M.A. Roberton and L.E. Halverson, 1984, Developing children: Their changing movement (Philadelphia: Lea & Febiger), 103, 106-107, 118, 123. By permission of Mary Ann Roberton. Figure 8.14 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.16 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 8.17 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Table 8.4 The preparatory trunk action and the parenthetical information in Step 3 of Racket Action are reprinted, by permission, from J.A. Messick, 1991, “Prelongitudinal screening of hypothesized developmental sequences for the overhead tennis serve in experienced tennis players 9-19 years of age,” Research Quarterly for Exercise and Sport 62: 249-256. The remaining components are reprinted, by permission, from S. Langendorfer, 1987, Prelongitudinal screening of overarm striking development performed under two environmental conditions. In Advances in motor development research, Vol. 1, edited by J.E. Clark and J.H. Humphrey (New York: AMS Press), 26. 25 Figure 9.3 Reprinted, by permission, from L. Hay, 1990, Developmental changes in eye- hand coordination behaviors: Preprogramming versus feedback control. In Development of eye-hand coordination across the life span, edited by C. Bard, M. Fleury, and L. Hay (Columbia, SC: University of South Carolina Press), 235. Figure 9.5 Drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin–Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Table 9.1 “Developmental sequence for catching a small fall: A prelongitudinal screening,” H.S. Strohmeyer, K. Williams, and D. Schaub-George, Research Quarterly for Exercise and Sport, 62: 549-256, 199, reprinted by permission of the American Alliance for Health, Recreation and Dance (www.AAHPERD.org). Figure 9.7 Illustrations are drawn from film tracings taken in the Motor Development and Child Study Laboratory, University of Wisconsin—Madison and now available from the Motor Development Film Collection, Kinesiology Division, Bowling Green State University. © Mary Ann Roberton. Figure 9.8 From P. McLeod and Z. Dienes, 1996, “Do fielders know where to go to catch the ball or only how to get there?” Journal of Experimental Psychology: Human Perception and Performance 22: 541. Copyright © 1996 by the American Psychological Association. Adapted with permission. Figure 9.9 Redrawn from Lenoir et al. 1999. Figure 10.1 Reprinted, by permission, from G.H. Sage, 1984, Motor learning and control: A neuropsychological approach (Dubuque, IA: Brown), 111. ©The McGraw-Hill Companies. Figure 10.4 From Perception: The world transformed by Lloyd Kaufman, copyright (1979) originally from “Sight and Mind” by Kaufman, L. (1974) by Oxford University Press. Used by permission of Oxford University Press, Inc. Figures 10.5a and b Selected material from the Sensory Integration and Praxis Tests- Figure-Ground Perception Test © 1972 by Western Psychological Services. Reprinted by Human Kinetics Publishing Inc. by permission of WPS, 625 Alaska Avenue, Torrance, California, 90503, U.S.A., rights@wpspublishi.com. Not to be reprinted in whole or in part for any additional purposes without the expressed written permission of the publisher. All rights reserved. Figure 10.10 Reprinted from T.G.R. Bower, 1977, A primer of infant development (San Francisco: W.H. Freeman). Copyright 1977. 26 Figure 10.11 Based on B.A. Morrongiello 1988, The development of auditory pattern perception skills. In Advances in infancy research, Vol. 6, edited by C. Rovee-Collier and L.P. Lipsitt (Norwood, NH: Ablex). Figure 10.12 © Gg/age footstock. Figure 11.1 From R. Held and A. Hein, 1963, “Movement-produced stimulation in the development of visually guided behavior,” Journal of Comparative and Physiological Psychology 56: 872-876, fig. 1. Copyright © 1963 by the American Psychological Association. Reprinted with permission. Figure 12.1 Jacob De Golish/Icon SMI. Figure 12.2 Based on Kenyon and McPherson 1973. Figure 12.3 © Rainer Martens. Coaching Self-Report Form Reprinted, by permission from J. Williams, 2001, Applied sport psychology: Personal growth to peak performance, 4th ed. (New York: McGraw-Hill Companies). © McGraw-Hill Companies, Inc. Chapter 13 opener © Rainer Martens. Figure 13.1 Based on Horn 1987. Figure 13.2 © Rainer Martens. Figure 13.5 Reprinted from J.L. Duda and M.K. Tappe, 1989, Personal investment in exercise among adults: The examination of age and gender-related differences in motivational orientation. In Aging and motor behavior, edited by A.C. Ostrow (Dubuque, IA: Benchmark Press), 246, 248. By permission of J.L. Duda. Figure 14.2 Data from French and Thomas 1987. Figure 14.4 Data are from Colcombe and Kramer 2003. Figures 15.1a and b Reprinted, by permission, from R.M. Malina, C. Bouchard, and O. Bar-Or, 2004, Growth, maturation, and physical activity, 2nd ed. (Champaign, IL: Human Kinetics), 259. Figures 15.2a and b Reprinted, by permission, from O. Bar-Or, 1983, Pediatric sports medicine for the practitioner (New York: Springer), 4, 5. Figures 15.3a and b Data from Shephard 1982. 27 Figures 15.5a and b Reprinted, by permission, from B.A. Stamford, 1986, Exercise and the elderly. In Exercise and sport sciences reviews, Vol. 16, edited by K.B. Pandolf (New York: Macmillan), 344. © The McGraw-Hill Companies. Figure 15.6 Reprinted, by permission, from L.D. Zwiren, 1989, “Anaerobic and aerobic capacities of children,” Pediatric Exercise Science 1: 40. Figure 15.7 Reprinted, by permission, from W.W. Spirduso, K. Francis, and P. MacRae, 2005, Physical dimensions of aging, 2nd ed. (Champaign, IL: Human Kinetics), 108. Figure 16.1 Based on Shepard 1982. Figure 16.2 Based on Shepard 1982. Table 16.1 Reprinted, by permission, from W.W. Spirduso, 1995, Physical dimensions of aging (Champaign, IL: Human Kinetics), 127. Figure 16.3 Reprinted from A. Aniansson, M. Hedberg, G.B. Henning, and G. Grimby, 1986, “Muscle morphology, enzymatic activity, and muscle strength in elderly men: A follow-up study,” Muscle & Nerve 9: 588. Copyright © 1986 by John Wiley & Sons. Reprinted by permission of John Wiley & Sons, Inc. Figure 16.4 Reprinted, by permission, from W.W. Spirduso, K. Francis, and P. MacRae, 2005, Physical dimensions of aging, 2nd ed. (Champaign, IL: Human Kinetics), 112. Figure 16.6 Reprinted, by permission, from R.M. Malina, C. Bouchard, and O. Bar-Or, 2004, Growth, maturation, and physical activity, 2nd ed. (Champaign, IL: Human Kinetics), 495. Figure 16.7 Reprinted, by permission, from R.M. Malina, C. Bouchard, and O. Bar-Or, 2004, Growth, maturation, and physical activity, 2nd ed. (Champaign, IL: Human Kinetics), 498. Figure 16.8 “The specificity of flexibility in girls,” F.L. Hupprich and P.O. Sigerseth, Research Quarterly for Exercise and Sport, 21: 25-33, reprinted by permission of the American Alliance for Health, Recreation and Dance (www.AAHPERD.org). Figure 16.9 Reprinted, by permission, from W.K. Hoeger et al., 1990, “Comparing the sit and reach with the modified sit and reach in measuring flexibility in adolescents,” Pediatric Exercise Science 2: 156-162. Figure 16.10 Reprinted, by permission, from J. Simons et al., 1990, Growth and fitness of flemish girls (Champaign, IL: Human Kinetics), 118. Chapter 17 opener © Rainer Martens. 28 Figures 17.1a and b From J. Parizkova, 1977, Body fat and physical fitness (The Hague, The Netherlands: Martinus Nijhoff B.V.). By permission of J. Parizkova. Figure 17.2 From J. Parizkova, 1977, Body fat and physical fitness (The Hague, The Netherlands: Martinus Nijhoff B.V.). By permission of J. Parizkova. Figure 17.4 Drawn from data contained in the National Health and Nutrition Examination Survey III, as modified in Kotz, Billington, and Levine 1999; from Flegal et al. 1998. Chapter 18 opener Joe Toth/BPI/Icon SMI. Figure 18.1 Jack Terry/Action Plus/Icon SMI. Figure 18.7 Reprinted, by permission, from J. Herkowitz, 1978, Developmental task analysis: The design of movement experiences and evaluation of motor development status. In Motor development, edited by M.V. Ridenour (Princeton, NJ: Princeton Book), 141. Figure 18.8 Reprinted, by permission, from J. Herkowitz, 1978, Developmental task analysis: The design of movement experiences and evaluation of motor development status. In Motor development, edited by M.V. Ridenour (Princeton, NJ: Princeton Book), 149. Figures A.3a and b Adapted from www.cdc.gov/nchs/about/major/nhanes/growthcharts/clinical_charts.htm. Developed by the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion, 2000. Figures A.4a and b Reprinted from www.cdc.gov/nchs/about/major/nhanes/growthcharts/clinical_charts.htm. Developed by the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion, 2000. 29 Part I Introduction to Motor Development When you begin to learn any new area of study, you must start out by deciphering the lingo used by the area’s professionals. In motor development, the professionals include physical educators, athletic trainers, coaches, physical and occupational therapists, and professors. This is quite a variety! It may not surprise you, then, to hear that the first part of this text is dedicated to providing a sound base of information—terms, theories, concepts, and important historical notes—on which you can build your knowledge of motor development. You must learn basic terms so that you can read about motor development and converse with others about this field of study. You must learn the scope of the field and how it goes about researching the developmental aspects of motor behavior. It benefits you to learn how information is pictured or presented in the field of study. All of these topics are addressed in chapter 1. You also need to know the various perspectives that professionals in the field of motor development have adopted to view motor behavior and interpret studies of that behavior. Often, what is known in a discipline of study is a function of the perspectives adopted by those studying in the field. Chapter 2 introduces you to these perspectives. In chapter 3, you will learn about the principles of motion and stability that influence all of your movements at all times. Understanding these principles will help you see patterns in the way motor skills change over time (addressed in the following chapters). The goal of chapter 3 is to help you understand in a general way how these principles work. Most important, part I introduces you to a model that will be used to guide your study of motor development. The model, pictured on page 6, is Newell’s model of constraints (Newell, 1986). Chapter 1 describes the model’s parts and what it depicts. This model gives you a way of organizing new pieces of information. Right now, the most important notion you can gain from this model is that motor development does not focus only on the individual; it also examines the importance of the environment in which an individual moves and the task the individual is trying to accomplish. Moreover, the model gives you a way to analyze and think about issues and problems in motor development. Thus, it will be useful not only in the short term of your study of motor development but also in the long term as you move into a professional position or interact with family and friends regarding their motor skills. 30 Suggested Reading Adolph, K., & Robinson, S. (2008). In defense of change processes. Child Development, 79, 1648–1653. Clark, J.E. (2005). From the beginning: A developmental perspective on movement and mobility. Quest, 57, 37–45. Gagen, L., & Getchell, N. (2004). Combining theory and practice in the gymnasium: “Constraints” within an ecological perspective. Journal of Physical Education, Recreation and Dance, 75, 25–30. Gagen, L., & Getchell, N. (2008). Applying Newton’s apple to elementary physical education: An interdisciplinary approach. Journal of Physical Education, Recreation and Dance, 79, 43–51. Getchell, N., & Gagen, L. (2006). Interpreting disabilities from a ‘constraints’ theoretical perspective: Encouraging movement for all children. Palaestra , 22, 20–53. Jensen, J. (2005). The puzzles of motor development: How the study of developmental biomechanics contributes to the puzzle solutions. Infant and Child Development, 14(5), 501–511. Thelen, E. (2005). Dynamic systems theory and the complexity of change. Psychoanalytic Dialogues, 15(2), 255–283. 31 Chapter 1 Fundamental Concepts 32 Chapter Objectives This chapter defines motor development, distinguishes developmental issues from other concerns, describes some of the basic tools used by researchers in motor development, explains why development occurs over a life span, and introduces a model that guides our discussion of motor development. 33 Motor Development in the Real World The Up Series In 1964, director Paul Almond filmed a group of 14 British children, all 7 years old, from diverse socioeconomic backgrounds and created a documentary about their lives titled 7 Up ! In 1971, Michael Apted followed up with 7 Plus Seven, and he has brought out a new installment of the series every 7 years since, following these same individuals through childhood and adolescence, into adulthood, and now into middle age. Apted hoped to explore the influence of the British class system over time and see if the Jesuit motto “Give me a child until he is seven and I will give you the man” held true. The latest installment, 56 Up, premiered on British television on May 14, 2012, and was released in the United States in January 2013. In total, the Up series will show the lives of the participants over the course of 49 years, from childhood into middle adulthood, thus providing a window into the group and individual development. If we developed a series of documentaries addressing motor development, who might watch? Many professionals might be interested. Educators, especially physical and early childhood educators, might be interested in which practices work best and whether they are developmentally appropriate. Therapists would want to know the factors that affect movement abilities. Engineers and designers might be interested in changes throughout adulthood in order to make appropriately sized and arranged living spaces, control panels, work equipment, sport gear, and vehicles. Health care providers might want to determine how movement and exercise early in life affect health status later on. Clearly, then, motor development interests many people for many reasons. Indeed, we can learn a great deal by examining change in movement patterns—and why it occurs—from birth until old age. Movement is an integral part of our lives, and its change is inevitable. 34 Defining Motor Development Our imaginary documentary series might give you a rough idea of what motor development is. Let’s now be more exact and give the field some boundaries, much as a producer would do in order to decide which segments are appropriate for the motor development film and which are not. Development is defined by several characteristics. First, it is a continuous process of change in functional capacity. Think of functional capacity as the capability to exist—to live, move, and work—in the real world. This is a cumulative process. Living organisms are always developing, but the amount of change may be more noticeable, or less noticeable, at various points in the life span. Second, development is related to (but not dependent on) age. As age advances, development proceeds. However, development can be faster or slower at different times, and rates of development can differ among individuals of the same age. Individuals do not necessarily advance in age and advance in development at the same rate. Further, development does not stop at a particular age but rather continues throughout life. Third, development involves sequential change. One step leads to the next step in an orderly and irreversible fashion. This change results from interactions both within the individual and between the individual and the environment. All individuals of a species undergo predictable patterns of development, but the result of development is always a group of unique individuals. Individuals function in a variety of arenas, including the physical, social, cognitive, and psychological. Hence, we use terms such as cognitive development or social development to address the process of change in particular arenas. Social scientists often specialize in the study of one aspect of development. We use the term motor development to refer to the development of movement abilities. Those who study motor development explore developmental changes in movements as well as the factors underlying those changes. Such study addresses both the process of change and the resultant movement outcome. Not all change in movement constitutes development. For example, if a tennis teacher elicits a change in a student’s forehand stroke by changing the student’s grip on the racket, we do not view the change as motor development. Rather, we use the term motor learning, which refers to movement changes that are relatively permanent but related to experience or practice rather than age. We use the term motor behavior when we prefer not to distinguish between motor learning and motor development or when we want to include both. Motor control refers to the nervous system’s control of the muscles that permits skilled and 35 coordinated movements. In recent years, researchers in motor development and in motor control have found much in common. Understanding how the nervous system and movement abilities change with age expands our knowledge of motor control, and we now see much overlap in motor development and control research. Motor development refers to the continuous, age-related process of change in movement as well as the interacting constraints (or factors) in the individual, environment, and task that drive these changes. Motor learning refers to the relatively permanent gains in motor skill capability associated with practice or experience (Schmidt & Lee, 2005). Motor control is the study of the neural, physical, and behavioral aspects of movement (Schmidt & Lee, 2005). Scan news websites such as MSNBC.com or CNN.com for stories related to motor development. What key words did you select to search for stories on this topic besides motor and development in order to focus in on this topic? Undoubtedly, you have heard the term development paired with the term growth, as in “growth and development.” Physical growth is a quantitative increase in size or magnitude. Living organisms experience a period of growth in physical size. For humans, this growth period starts with conception and ends in late adolescence or the early 20s. Changes in the size of tissues after the physical growth period (e.g., an increase in muscle mass with resistance training) are described with other terms. Thus, the phrase growth and development includes change in both size and functional capacity. Physical growth is an increase in size or body mass resulting from an increase in complete, already formed body parts (Timiras, 1972). The term maturation is also paired with the term growth, but it is not the same as development. Maturation connotes progress toward physical maturity, the state of optimal functional integration of an individual’s body systems and the ability to reproduce. Development continues long after physical maturity is reached. Physiological change does not stop at the end of the physical growth period. Rather, it can occur throughout life. Physiological change tends to be slower after the growth period but nevertheless remains prominent. The term aging can be used in a broad sense to refer to the process of growing older regardless of chronological age; it can also refer specifically to changes that lead to a loss of adaptability or function and eventually to death (Spirduso, Francis, & MacRae, 2005). Physiological maturation is a qualitative advance in biological makeup and may refer to cell, organ, or system advancement in biochemical composition rather than to size alone (Teeple, 1978). Aging is the process, occurring with the passage of time, that leads to loss of adaptability or 36 full function and eventually to death (Spirduso, Francis, & MacRae, 2005). The physiological processes of growth and aging fall on a continuum of life span development. For many years, researchers examined motor development almost exclusively from early childhood through puberty. However, the population across the globe has aged. In many countries—including the United States, China, Russia, Australia, Canada, and the majority of the European Union’s nations—by 2030 at least 13% of the population will be aged 65 years or older (Kinsella & Velkoff, 2001). This change brings more urgency to the need for better understanding of motor development in the later years. Although some motor development students might be particularly interested in one portion of the continuum, motor development as a field still concerns change in movement across the life span. Understanding what drives change in one part of the life span often helps us understand change in another part. This process of examining change is part of adopting a developmental perspective. 37 Constraints: A Model for Studying Motor Development It is useful to have a model or plan for studying the change in movement that occurs over the life span. A model helps us include all the relevant factors in our observation of motor behavior. This is particularly true as we think about the complexity of motor skills and how our skills change over the life span. For this textbook, we adopted a model associated with a contemporary theoretical approach known as the ecological perspective (see chapter 2). We find that this model helps us make sense of developmental changes by providing a framework for observing change. We believe this model—Newell’s constraints model—will help you better understand motor development across the life span. Newell’s Model Karl Newell (1986) suggested that movements arise from the interactions of the organism, the environment in which the movement occurs, and the task to be undertaken. If any of these three factors change, the resultant movement changes. We can picture the three factors as points on a triangle with a circle of arrows representing their interaction (figure 1.1). Because we are concerned only with human movement here, we use the term individual instead of organism. In short, to understand movement, we must consider the relationships between the characteristics of the individual mover, his surroundings, and his purpose or reasons for moving. From the interaction of all these characteristics, specific movements emerge. This model reminds us that we must consider all three corners of the triangle in order to understand motor development. 38 Figure 1.1 Newell’s model of constraints. Picture the different ways in which individuals can walk—for example, a toddler taking her first steps, a child walking in deep sand, an adult moving across an icy patch, or an older adult trying to catch a bus. In each example, the individual must modify his or her walking pattern in some way. These examples illustrate that changing one of the factors often results in a change in the interaction with one or both of the other factors, and a different way of walking arises from the interaction. For example, whether you are barefoot or wearing rubber-soled shoes might not make a difference when you’re walking across a dry tile floor, but your walk might change notably if the floor were wet and slippery. The interaction of individual, task, and environment changes the movement, and, over time, patterns of interactions lead to changes in motor development. Why is Newell’s model so helpful in studying motor development? It reflects the dynamic, constantly changing interactions in motor development. It allows us to look at the individual and the many body systems that constantly undergo age-related changes. At the same time, the model emphasizes the influence of where the individual moves (environment) and what the individual does (task) on individual movements. Changes in the individual lead to changes in his or her interaction with the environment and task and subsequently change the way the individual moves. For example, a young child may enjoy tumbling on mats in his preschool. His parents may put him in a gymnastics class (change in environment); at that class, the instructors may focus on equipment rather than tumbling stunts (change in task). Over time and through his experience focusing on specific equipment in the gymnastics class, the boy may become proficient at the pommel horse. Another example is an older adult who, because of hip arthritis, chooses to walk only when absolutely necessary and stops attending her walking group. Changes in her social environment lead to disengagement in exercise, which in turn leads to loss of strength, flexibility, and mobility and, ultimately, to more hip pain. In both of these examples, the individual, environment, and task influence—and are influenced by—each other. Newell calls the three factors we placed on the points of our triangle constraints. A 39 constraint is somewhat like a restraint: It limits or discourages (in this case, movement) but at the same time it permits or encourages (in this case, other movements). It’s important not to consider constraints as negative or bad. Constraints simply provide channels from which movements most easily emerge. A riverbed acts as a constraint: It restrains the river water from flowing anywhere and everywhere but it also channels the water to follow a specific path. Movement constraints are characteristics that shape movement. They restrain and channel movement to a particular time and place in space; that is, they give movement a particular form. Individual constraints, the top point on our triangle, are a person’s unique physical and mental characteristics. For example, height, limb length, strength, and motivation can all influence the way an individual moves. Consider the swimmer with a disability in figure 1.2. The disability constrains, but does not prevent, this individual’s ability to swim; it simply modifies the way in which the swimmer performs her strokes. Individual constraints are either structural or functional. Structural constraints relate to the individual’s body structure. They change with growth and aging; however, they tend to change slowly over time. Examples include height, weight, muscle mass, and leg length. As we discuss these changes throughout the text, you will see how structural factors constrain movement. Functional constraints relate not to structure but to behavioral function. Examples include motivation, fear, experiences, and attentional focus. Such constraints can change over a much shorter period of time. For instance, you might be motivated to run several miles in cool weather but not in hot, humid weather. This functional constraint shapes your movement to running, walking, or even sitting. A constraint is a characteristic of the individual, environment, or task that encourages some movements while discouraging others. Individual constraints are a person’s or organism’s unique physical and mental characteristics. Structural constraints are individual constraints related to the body’s structure. Functional constraints are individual constraints related to behavioral function. 40 Figure 1.2 This swimmer’s truncated limb is a structural constraint that gives rise to a swimming movement that is different from that of someone without a truncated limb. For many professionals, it is important to know whether the student’s or client’s movement is being shaped by structural or functional constraints. Such information can help one understand how much a movement could change in a short time and whether a change in an environmental or task constraint would be in order to modify the resultant movements. For example, knowing that young volleyball players cannot block a ball at the net because they are not yet at their adult height would lead a youth sports organizer to change the task by using a lower net height. Environmental constraints exist outside the body, as a property of the world around us. They are global rather than task specific and can be physical or sociocultural. Physical environmental constraints are characteristics of the environment, such as temperature, amount of light, humidity, gravity, and the surfaces of floors and walls. The example of a runner not feeling motivated to run in humidity represents the functional constraint of motivation interacting with two environmental constraints—temperature and humidity— to constrain movement. One’s sociocultural environment can also be a strong force in encouraging or discouraging behaviors, including movement behaviors. One example is how change in the sociocultural environment in Western society has changed the involvement of girls and women in sport over the past three decades. In the 1950s, society did not expect girls to participate in sport. As a result, girls were channeled away from sport. 41 Task constraints are also external to the body. They include the goals of a movement or activity. These constraints differ from individual motivation or goals in that they are specific to the task. For example, in basketball the goal is to get the ball in the hoop—this is true for any individual playing the game. Second, task constraints include the rules that surround a movement or activity. If we think about basketball, players could get down the court much more quickly if they could simply run with the ball rather than dribble it. However, the rules dictate that players must dribble while running with the ball, meaning that the resultant movement is constrained to include bouncing the ball. Finally, the equipment we use is a task constraint. For example, using a strung racket rather than a wood paddle changes the game played on an enclosed (racquetball) court. Recall also the case of the youth sports organizer, who by lowering net height used the interaction of a structural individual constraint (body height) and a task constraint to allow a certain movement (blocking) to emerge in the game played by young volleyball players. You can probably imagine many of the task constraints in the depicted situation. The basketball player must pass the ball to a teammate while protecting the ball from a defender. Environmental constraints are constraints related to the world around us. Task constraints include the goals and rule structure of a particular movement or activity. Throughout this discussion of motor development we demonstrate how changing individual, environmental, and task constraints shape the movement that arises from their interaction. Newell’s model guides us in identifying the developmental factors affecting movements, helps us create developmentally appropriate tasks and environments, and helps us understand individual movers as different from group norms or averages. Changing Views on the Role of Constraints It is important to recognize that in the history of motor development research certain researchers and practitioners focused primarily on individual factors to the exclusion of others. For example, in the 1940s it was assumed that an individual constraint— specifically, the structural constraint of the nervous system alone—shaped movement in infants and children (see chapter 2 for a discussion of this concept). Later, in the 1960s, developmentalists commonly believed that environmental and task constraints, more than individual constraints, shaped movement. Only recently have motor developmentalists begun focusing on all three types of constraints simultaneously as well as carefully examining how constraints interact with and influence each other over time. When one or two types of constraints are deemphasized, so is the rich effect of the three constraints interacting to shape movement. Thus, such an approach limits the resulting view of the emerging movement. In our survey of motor development we identify the effects of these various viewpoints on the importance of the three constraints. Sometimes what we know about an aspect of motor development is influenced by the perspective of the researcher who studied that behavior. It is much like seeing the color of a flower change 42 as we try on sunglasses with lenses of different colors. We might “color” our conclusions about motor development as we emphasize one type of constraint and deemphasize other types. Imagine you are a physical educator or a coach. Knowing how height and body size change with growth, how would you adapt the game of basketball (especially the task, through the equipment used) so that movements (shooting, dribbling, passing) remain nearly the same during the growth years? Newell’s model is more global than most models previously used in the study of motor development. We can better account for the complexity of age-related change in movement with this model through the interactions of individual, environment, and task. Keep the model in mind throughout our survey of motor development. Constraints and Atypical Development To understand the basics of constraints, we explain concepts using typical motor development. That is, we use examples that describe what we might expect in people with average individual constraints (strength, height, motivation), who move in typical environments (gymnasiums, playgrounds, grocery stores), and who perform normal tasks (sport, activities of daily living). Essentially, this is motor development “on average.” People can develop in different ways and still be considered to be in an average range. However, individuals can deviate from the average developmental course in a variety of ways. In some instances, development is advanced (motor skills appear sooner than expected) or delayed (motor skills appear later than expected). In others, development is actually different (the person moves in unique ways). When we discuss constraints and atypical development, we focus on delayed and different development, particularly in individuals with disabilities. Interacting constraints lead to movement changes over individuals’ lives. Therefore, we know that differences in structural and functional individual constraints can lead to atypical developmental trajectories. For example, a child with cerebral palsy may be delayed in the acquisition of fundamental motor skills due to muscle spasticity, or an adult with multiple sclerosis will see motor proficiency diminish as a function of deteriorating myelin sheaths in the brain and spinal cord. In certain conditions, people may exhibit motor coordination delays that can be overcome with enhanced practice or experience. In the language of constraints, enhanced practice represents a change in task constraints. Movement practitioners must keep in mind how movement can change as a result of changing constraints and thereby adjust environment and task constraints to accommodate differences in individual constraints. Throughout the text we provide examples of research related to atypical development. 43 How Do We Know It Is Change? We’ve established that age-related change is fundamental in the study of motor development and the developmental perspective. Developmentalists are focused on change. How do we know, though, that a change is age related and not a fluctuation of behavior (a good or bad day on the court, for example) or a product of our measuring instrument (a radar gun versus video motion analysis)? One way to discern developmental change is to carefully observe individuals’ movements and then describe differences between people of different age groups or instances of observation. Web Study Guide Gain experience in observing movement by doing Lab Activity 1.1, Observation as a Tool of Inquiry, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. In addition, behavioral scientists use statistical techniques that can identify significant change. We sometimes discuss these techniques in the context of a research study. For now, let’s focus on the straightforward technique of picturing change by graphing an aspect of development over time. We can then see whether a trend is emerging. Picturing Change When we picture age-related measurements by graphing, we traditionally put time or age on the horizontal axis. Time can be measured in days, weeks, months, years, or decades, depending on our frame of reference. A measurement of interest in infancy might be plotted in days or weeks, whereas a measurement of interest over the life span might be plotted in years or decades. The measurement is plotted on the vertical axis. We usually arrange the measurements so that “more,” “faster,” or “more advanced” is higher on the scale. Figure 1.3 shows a typical graph of the measurement of growth in childhood. It is common to take a measurement periodically and plot its value at selected chronological ages. We assume that change has occurred consistently in the time between our measurements, so we often connect our data points to make a line. When we graph change using a developmental perspective, we should not make the assumption that more is always better. Individuals move in a variety of ways that are qualitatively different. Some ways of moving may result in longer throws or faster runs, but that does not imply that children who do not move this way are in error or are wrong. It simply means that these children move at a different or lower developmental 44 level. 45 Figure 1.3 A typical graphical representation of growth in childhood. Reprinted by permission from Maclaren 1967. Web Study Guide Practice graphing to help picture change in Lab Activity 1.2, Graphing Developmental Data, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Can You Tell It’s Developmental? A Litmus Test Armed with definitions of motor development and motor learning, you still might find it difficult to distinguish whether a particular behavior is a matter of learning or development. Mary Ann Roberton (1988, p. 130) suggests that the answers to three questions help us distinguish developmental issues and topics. 1. Are we interested in what behavior is like now and why the behavior is the way it is? 2. Are we interested in what behavior was like before our present observation, and why? 3. Are we interested in how the present behavior is going to change in the future, and why? Students of both motor learning and motor development answer yes to the first question, but only developmentalists answer yes to the second and third questions. Motor learning specialists are concerned with making changes that bring about a 46 relatively permanent change in behavior within a short time. Motor developmentalists focus on a longer time during which a sequence of changes occurs. Developmentalists might introduce a change in task or environmental constraints to make either or both age appropriate while realizing that the task or environment will have to change again and again as individuals age and change. Researching Developmental Change In the study of development, we ideally watch an individual or group change with age for the entire length of the period we are interested in. This is termed a longitudinal research study. The difficulty here comes when our frame of reference is years or decades. For example, a teacher may be interested in the changes in locomotor skills across childhood. We can see that an individual researcher might be able to do only a few such studies in his or her lifetime. This approach would not inform us about motor development very quickly! Researchers have several ways of learning more in a shorter time. One of these techniques is termed a cross-sectional research study. In a cross-sectional study, researchers select individuals or groups at chosen points in the age span of interest. For example, researchers interested in change during adolescence might measure a group of 13-year-olds, a group of 15-year-olds, a group of 17-year-olds, and so on. When the measurements of each group are plotted, we assume that any observed change reflects the same change we would observe in a single group over the whole time period. The advantage of this method is that researchers can study development in a short time. The disadvantage is that we never really observe change; we merely infer it from age group differences. If something else were responsible for the age group differences, we could be fooled into thinking the differences were caused by developmental change. A longitudinal research study is one in which the same individual or group is observed performing the same tasks or behaviors on numerous occasions over a long time. A cross-sectional research study is one in which developmental change is inferred by observing individuals or groups of varying ages at one point in time. Consider the example of tricycles. At one time, all tricycles were metal and shaped so that the seat was relatively high off the ground. Children under 3 years of age had difficulty just getting on the tricycle. Then someone invented the Big Wheel tricycle, with the large front wheel and the seat only inches off the ground. Toddlers can easily sit on this vehicle. Let’s pretend that a researcher did a cross-sectional study on coordination of the cycling motion of the legs in toddlers at the ages of 1.5, 2.0, 2.5, and 3.0 years. The study was done just 1 year after the Big Wheel tricycle came on the market (note: as a piece of equipment, the Big Wheel is a task constraint). The researcher observed that toddlers 2.5 and 3.0 years of age could coordinate this movement and concluded that approximately 2.5 47 years was the age at which toddlers could coordinate the cycling movement of the legs. But what if the researcher had done the study a year earlier, before any of the children would have been on a Big Wheel? The researcher might have observed that none of the children could coordinate the cycling movement because none would have been able to ride the high-seat tricycle. The researcher would then have concluded that this coordination developed after the age of 3.0 years. The invention of the Big Wheel gave a cohort, or mini-generation, of toddlers earlier practice in coordinating the cycling movement. Older cohorts could not practice the movement until they were big enough to get up on the higher tricycle. Thus, one cohort had an experience that another did not. Such a cohort difference can fool researchers in a cross-sectional study, leading them to associate performance differences between age groups with age alone and not factors such as exposure to new inventions. Researchers must be particularly aware of cohort differences when examining aging populations due to rapidly changing technologies. For example, many older adults may not own or frequently use computers. Let’s say a researcher wants to examine differences in driving techniques among drivers of different age groups and uses a computer simulation in which the car is controlled by a joystick. Older adults may show differences in performance because they are unfamiliar with computer joysticks rather than because of true differences in driving technique. (In fact, children who are not yet eligible to drive may perform better because of their experiences with video games.) Researchers must take care to control for cohort differences. A cohort is a group whose members share a common characteristic, such as age or experience. Researchers have, however, devised a clever way to identify cohort influences while at the same time conducting developmental research in less time than required by a longitudinal study. They do it by combining longitudinal and cross-sectional studies. In effect, they conduct several small longitudinal studies with subjects of different ages of the period of interest. For example, in the first year they would measure three groups of children, one at 4 years of age, one at 6 years of age, and one at 8 years of age. Notice that if the researchers stopped here they would have a cross-sectional study. Instead, a year later they measure all the children again. This time the children are 5, 7, and 9 years of age. They do the same thing another year later, when the children are 6, 8, and 10. In the end they have done three small longitudinal studies: one cohort that was the original 4-year-olds at 4, 5, and 6; one cohort that was the original 6-year-olds at 6, 7, and 8; and one cohort that was the original 8-year-olds at 8, 9, and 10. Thus, information is made available about ages 4 through 10, but only 2 years were needed to obtain it. What about the possibility of cohort differences? Note that the mini- longitudinal studies cover overlapping ages. Two groups were tested at age 6 and two groups were tested at ages 7 and 8. If performance of the different cohorts is the same at a given age, then it is likely that cohort differences are not present. If the cohorts perform 48 differently at the same age, cohort influences might well be present. This type of design is called a mixed-longitudinal, or sequential, research study. In a mixed-longitudinal, or sequential, research study, several age groups are observed at one time or over a shorter time span, permitting observation of an age span that is longer than the observation period. New students of motor development can tell whether a research study is developmental by considering the design of the study. The study is developmental if the design is longitudinal, cross-sectional, or sequential (figure 1.4). Research studies that focus on one age group at one point in time are not developmental. 49 Figure 1.4 A model of a sequential research design. Note that each row is a short longitudinal study and that each column is a small cross-sectional study. The time-lag component of sequential research design, shown by the diagonal lines, allows comparison of groups from different cohorts but at the same chronological age, thus identifying any cohort differences. Ages 5 to 20 can be studied in 10 years—1990 to 2000. 50 A Developmental Paradox: Universality Versus Variability Picture yourself in a gymnasium filled with preschool students. Many children move similarly: They begin to display rudimentary catching and throwing; they walk and run with proficiency but have difficulty skipping. On average, the children perform many of the same motor skills. Yet if you look at an individual child, he or she may move in more or less advanced ways than a child standing a foot away. This difference highlights the paradox of universality of development as opposed to individual differences (Thelen & Ulrich, 1991). Individuals in a species show great similarity in their development in that they go through many of the same (stereotypical) changes. You have heard reference to “stages of development.” Stages, of course, describe the emergence of universal behaviors. Those who anticipate working with individuals in a particular age range are often interested in the behaviors typical of those in that range. However, individual differences in development do exist. Any individual we observe is more likely to be above or below average, or to achieve a milestone earlier or later than average, than he or she is to be exactly average. In addition, children can arrive at the same point in development by very different pathways (Siegler & Jenkins, 1989). All individuals, even identical twins, have different experiences, and people who work with any group of individuals supposedly at the same stage of development are usually amazed by the variability in the group. Thus, developmentalists, educators, parents, and health professionals must consider an individual’s behavior in the context of both universal behaviors and individual differences. It is also important to recognize when others are using a perspective that focuses on the universality of behavior and when they are focusing on variability in behavior. Systematic and controlled observation—that is, research—helps us distinguish between behaviors that tend to be universal and behaviors that reflect human variability. Research also helps us identify the role of constraints such as the environment and individual experiences in creating variability of behavior. As much as possible, the ideas presented in this text are based on research studies; the information comes from an objective source. Keep in mind that this is not the same as saying that any one or two research studies provide us with all the answers we need. Individual research studies do not always rule out all the other possible explanations for the results—to do that, additional research might be required. Deriving the principles and theories from research to guide educational and health care practice is a process. Although practitioners are sometimes frustrated to see that researchers are only in the middle of the process, it is better to recognize that this is the case than to 51 take all research results as the final word. Our goal here is not only to use available research information to make insightful conclusions and decisions about the motor development of individuals but also to learn how to obtain and analyze further research information as it becomes available. Think of generalizations we tend to make about people, such as “tall people are thin,” and then think of at least one person you know who is an exception to this “rule.” What is the consequence of expecting a student or patient to follow a generalization? 52 Summary and Synthesis Now that we understand the developmental perspective, it is easy to see why a documentary series on motor development spanning several decades would be of interest to many viewers. At the present moment each of us is a product of “what we were like before,” and each of us will change to become something different in the future. We are all developing, and our individual constraints constantly change! Add changing environmental and task constraints and you have a very interesting mix related to motor development. Many professions involve relationships with people at critical points of the life span— points at which the change taking place influences life thereafter. This is especially true in regard to the physical being and physical skills. As a result, your knowledge of motor development and the constraints related to it will help you and those around you throughout life. If you choose a profession such as teacher, coach, or therapist, this knowledge will help you help others by providing developmentally appropriate activities. Your study of motor development will be easier if you equip yourself with a few basic tools. Most important is a framework or model to which you can relate new information, and we use Newell’s model of constraints throughout this text. Another important tool is knowledge of how research in motor development is designed, which helps you understand how researchers address developmental issues. Yet another important tool, which we have yet to discuss, is knowledge of the various perspectives that developmentalists adopt as they approach their research. Because the same problem can be approached from many perspectives, it is valuable to know which approach is taken. The next chapter explores various approaches and discusses the theoretical roots of motor development. Reinforcing What You Have Learned About Constraints Take a Second Look At the start of this chapter you learned about the Up series, which has been very successful in Great Britain and the United States. Viewers are interested in how the participants change over time as well as how they stay the same and in what factors (or constraints, as we now call them) influence their life trajectories. In other words, they want to see how the lives of these children (and later adults) develop. Just like viewers of these films, readers of this text will examine motor development interactively, considering the influences that individual, environmental, and task constraints have on an individual’s movement skills over his or her entire life. It is important that the documentaries do not end (nor do they become less interesting) once childhood ends; rather, they continue because individuals 53 continue to develop and change across the entire life span. For this reason, we adopt a life span perspective for motor development. Test Your Knowledge 1. How does the field of motor development differ from that of motor learning? What key perspective separates the two? 2. How do physical growth and physiological maturation differ? 3. Think of your favorite physical activity, exercise, or sport. Describe some of the individual (both structural and functional), environmental, and task constraints of this activity. 4. Why might a person planning a career in teaching children choose also to study older adults? 5. What are the differences between longitudinal research studies and cross-sectional research studies? What characteristics of each are used in sequential, or mixed- longitudinal, research studies? 6. What does it mean if a physical educator uses “developmentally appropriate” teaching practices? Provide some examples using constraints. Learning Exercise 1.1 Searching the Internet for Information on Motor Development The Internet can be a valuable resource for any practitioner. Many organizations and advertisers provide information that anyone with a computer can access. It is important, however, to keep in mind that there are few regulations on the Internet; anyone can make almost any claim (whether it is based on research, opinion, or something else). You, as an informed consumer, must examine websites carefully and determine the usefulness and accuracy of their information, just as you would with library research. In this learning activity you will determine the theoretical assumptions and underpinnings of various websites. 1. Enter the term motor development into the search engine of your choice (e.g., Google, Bing). How many hits are there using these two words? Do any of the websites surprise you? How so? 2. Find a website that sells motor development products. Pick a product. What is it, and what is its purpose? How is it developmental, according to the advertiser? Based on what you’ve learned, is it really developmental? 3. Repeat your Internet search, adding the term infancy to motor development. From the resulting list, examine at least three types of websites (e.g., academic, sales, medical). Identify some unusual or particularly interesting sites. If you were a parent searching for information, what could you learn from these websites? 54 Chapter 2 Theoretical Perspectives in Motor Development Changing Interpretations of Constraints 55 Chapter Objectives This chapter describes the theories currently used to study motor development, illustrates how various theories explain changes in motor behavior, and describes the history of the field of motor development. 56 Motor Development in the Real World The Birth of Your First Nephew Imagine that you visit your older sister in a neighboring state just after she has had her first child. You see the infant as a newborn, just 1 week old. He does not really respond to you unless you place a bottle in his mouth or your finger in his hand. His movements include seemingly random flapping of the arms and legs—unless he is hungry, in which case he flails his limbs and cries! When you leave you think, “He seems pretty uncoordinated and weak.” After 9 months, you visit your sister and nephew again. What a change! He sits up on his own, reaches for toys, and has started to crawl. He can even stand when you hold him. He begins to coordinate his actions so that he can move purposefully. Now, let’s say you visit again, another 9 months later. Your nephew is no longer an infant but a full-fledged toddler. He can now walk —pretty quickly when he wants to—and has no problem with reaching and grasping. He is beginning to respond to language, particularly with the word no. He seems so very different from the newborn you met a mere 18 months before! In this scenario it would be natural to wonder, “What happened during the past year and a half that resulted in these changes?” In other words, how can you (or anyone else) explain the changes seen across development? Given that there appear to be similarities in the development of different people (universality, described in chapter 1), how do we organize and understand these changes so that we can explain them and predict future development? Certain facts exist. How can we make sense of them? We must look at the different theoretical perspectives on motor development. Theories provide a systematic way to look at and explain developmental change. Theories of motor development have roots in other disciplines, such as experimental and developmental psychology, embryology, and biology. Contemporary research in motor development often uses what is called an ecological perspective to describe, explain, and predict change. To interpret developmental “facts,” it is important to understand the different theoretical perspectives from which the supposed facts emerge. Knowing these theoretical perspectives will help us understand the explanations and form interpretations when several explanations conflict. 57 Maturational Perspective In a nutshell, the maturational perspective explains developmental change as a function of maturational processes (in particular, through the central nervous system, or CNS) that control or dictate motor development. According to the assumptions of this theory, motor development is an internal or innate process driven by a biological or genetic time clock. The environment may speed or slow the process of change but it cannot change one’s biologically determined course. Key Point Maturationists believe that genetics and heredity are primarily responsible for motor development and that the environment has little effect. The maturational perspective became popular during the 1930s, led by Arnold Gesell (Gesell, 1928, 1954; Salkind, 1981). Gesell believed that the biological and evolutionary history of humans determined their orderly and invariable sequence of development (i.e., each stage of development corresponds with a stage of evolution). The rate at which people pass through that developmental sequence, however, can differ from one individual to another. Gesell explained maturation as a process controlled by internal (genetic) factors rather than external (environmental) factors. He believed that environmental factors would affect motor development only temporarily because hereditary factors were ultimately in control of development. Using identical twins as subjects, Gesell and his associates introduced the co-twin control strategy to developmental research (figure 2.1). What better way to test the effects of environment and heredity than to look at twins? In this strategy, one twin receives a specific training (the experimental treatment) while the other receives no special training (the control treatment). Thus, the control develops naturally, as any child would without special training. In this manner, Gesell examined the effects of the environment on development. 58 Figure 2.1 Using twins in studies allowed researchers such as Gesell and McGraw to “control” genetics while manipulating the environment. Courtesy of the authors. After a certain period of time, the twins were measured and compared with previously determined developmental criteria to see whether the enhanced experience affected the experimental child in any way. Co-twin research provided significant contributions to the study of motor development. In particular, these studies allowed developmentalists to begin identifying the sequence of skill development and noting variations in the rate of skill onset. Gesell concluded from his research that children develop in an orderly fashion (i.e., developmental change came in a predictable, predetermined order over childhood). Another prominent researcher at the time, Myrtle McGraw, used twins to examine the influence of enhanced experience on motor development (Bergenn, Dalton, & Lipsett, 1992; McGraw, 1935). In her classic study, Growth: A Study of Johnny and Jimmy, McGraw started observing the twins several months after their birth. She provided one twin (Johnny), at around 12 months of age, with challenging environments and unique tasks such as climbing a ramp at a progressively higher incline and roller skating. The tasks often required both motor and problem-solving skills. Johnny did excel in certain motor skills but not in others, which did little to resolve the nature-versus-nurture debate prominent in psychology at the time. McGraw’s results were equivocal, which may have been due (at least in part) to the fact that the twins were fraternal rather than identical. In addition to describing the course of motor development, many maturationists were interested in the processes underlying development. McGraw (1943), for example, associated changes in motor behavior with development of the nervous system. She considered maturation of the CNS to be the trigger for the appearance of new skills. McGraw was also interested in learning (and therefore not strictly a maturationist), but those who followed in the study of development generally overlooked this aspect of her work (Clark & Whitall, 1989a). 59 Use of the maturational perspective as a research tool in motor development began to wane by the 1950s, but the theory’s influence is still felt today. For example, the focus on maturation as the primary developmental process led researchers and laypersons alike to assume that basic motor skills will automatically materialize. Hence, even today, many researchers, teachers, and practitioners feel it is unnecessary to facilitate development of basic skills. In addition, the maturationists’ emphasis on the nervous system as the one system triggering behavioral advancement evolved to almost single-minded emphasis on that system—to the point that no other system was believed to have much significance. The cardiovascular, skeletal, endocrine, and even muscular systems were not deemed of primary importance to motor development. By the mid-1940s, developmental psychologists began to change the focus of their research, and their interest in motor development waned. At that point, physical educators took up the study of motor development, influenced by the maturational perspective. From then until about 1970, people studied motor development by describing movement and identifying age group norms (Clark & Whitall, 1989a). During this period, motor developmentalists from the physical education discipline focused their attention on school-age children. Researchers still used the maturational perspective, and their task therefore was to identify the naturally occurring sequence of changes. Key Point People have interpreted the maturationist perspective to mean that motor skills will automatically emerge regardless of differing environments. This assumption has influenced many teaching, parenting, and research concepts during the 20th and 21st centuries. Normative Descriptive Period Anna Espenschade, Ruth Glassow, and G. Lawrence Rarick led a normative description movement during this era. In the 1950s, education became concerned with standardized tests and norms. Consistent with this concern, motor developmentalists began to describe children’s average performance in terms of quantitative scores on motor performance tests. For example, they described the average running speed and jumping and throwing distances of children at specific ages. Although motor developmentalists were influenced by the maturational perspective, they focused on the products (scores, outcomes) of development rather than on the developmental processes that led to these quantitative scores. Biomechanical Descriptive Period Ruth Glassow also led another descriptive movement during this era. She made careful biomechanical descriptions of the movement patterns children used in performing 60 fundamental skills such as jumping. Lolas Halverson (figure 2.2) and others continued these biomechanical descriptions with longitudinal observations of children. As a result, the developmentalists were able to identify the course of sequential improvement that children followed in attaining biomechanically efficient movement patterns. The knowledge obtained from the normative and biomechanical descriptive periods was valuable in that it provided educators with information on age-related changes in motor development. Because description prevailed as the primary tool of these researchers during this time, motor development was labeled as descriptive. Interest in the processes underlying age- related changes, which had been so meticulously recorded before this period of history, seemed to disappear. 61 Figure 2.2 Lolas Halverson paved the way in the 1960s and 1970s for contemporary research in motor development. Courtesy of the authors. 62 Information Processing Perspective Another theoretical approach focuses on behavioral or environmental causes of development (e.g., Bandura’s social learning [Bandura, 1986] and Skinner’s behaviorism [Skinner, 1938], among others). The perspective most commonly associated from the 1960s to the 1980s with motor behavior and development is called information processing. According to this perspective, the brain acts like a computer, taking in information, processing it, and outputting movement. The process of motor learning and development, then, is described in terms of computer-like operations that occur as a result of some external or environmental input. This theoretical perspective appeared around 1970 and became the dominant perspective among experimental psychologists, developmental psychologists, and motor learning scientists specializing in physical education during the 1970s and 1980s (Schmidt & Lee, 2005; Schmidt & Wrisberg, 2008). This perspective emphasized concepts such as the formation of stimulus–response bonds, feedback, and knowledge of results (for more detailed information, see Pick, 1989; Schmidt & Wrisberg, 2008). Although some motor developmentalists continued with the product-oriented work of the normative and biological descriptive era, many others adopted the information processing perspective. Researchers studied many aspects of performance, such as attention, memory, and effects of feedback, across age levels (French & Thomas, 1987; Thomas, 1984). Motor learning researchers and experimental psychologists tended to study the perceptual-cognitive mechanism in young adults first. Then, developmentalists studied children and older adults, comparing them with the young adults. In this way, they could identify the processes that control movement and change with development (Clark & Whitall, 1989a). Today, the information processing perspective is still a viable approach to the study of motor development. Within the framework of information processing, some developmentalists continued to study perceptual-motor development in children. This work began in the 1960s with proposals that linked learning disabilities to children’s delayed perceptual-motor development. Early research focused on this link; by the 1970s, researchers had turned their attention to the development of sensory and perceptual abilities, adopting information processing research strategies (Clark & Whitall, 1989a). Therefore, much of what we know about perceptual-motor development resulted from researchers working within an information processing and mechanistic theoretical framework. 63 Ecological Perspective A new perspective on development appeared during the 1980s and has become increasingly dominant as the theoretical perspective used by motor development researchers today. This approach is broadly termed the ecological perspective because it stresses the interrelationships between the individual, the environment, and the task. Does this sound familiar? It should—it’s the perspective adopted by this text! We adopted this perspective because we feel it best describes, explains, and predicts motor development. According to the ecological perspective, you must consider the interaction of all constraints—for example, body type, motivation, temperature, and ball size—in order to understand the emergence of a motor skill—such as kicking (Roberton, 1989). Although one constraint or system may be more important or may cast a larger influence at any given time, all systems play a role in the resultant movement. This point makes the ecological perspective very appealing: At any given moment, how you move is related not only to your body or your environment but also to the complex interplay of many internal and external constraints. Key Point The ecological perspective takes into account many constraints or systems that exist both in the body (e.g., cardiovascular, muscular) and outside the body (e.g., ecosystem related, social, cultural) when observing the development of motor skills across the life span. The ecological perspective has two branches, one concerned with motor control and coordination (dynamical systems) and the other with perception (perception–action). The two branches are linked by several fundamental assumptions that differ notably from the maturational and information processing perspectives. In contrast to the maturational perspective, the ecological perspective considers motor development to be the development of multiple systems rather than only one (the CNS). In other words, many constraints change over time and influence motor development. Because these constraints or systems change throughout one’s life, motor development is considered a life span process. This contrasts sharply with the view of maturationists, who felt that development ended with the end of puberty (or at adulthood). Another difference relates to the cause of change. In information processing theory, an executive function is thought to decide all action, based on calculations of perceptual information resulting in hundreds of commands to control the individual muscles. That is, the executive directs all movement and all change. The ecological perspective maintains that a central executive would be overwhelmed by the task of directing all movement and change. In addition, this is a very inefficient way to move. Rather, perception of the environment is direct, and muscles self-assemble into groups, 64 reducing the number of decisions required of the higher brain centers (Konczak, 1990). Let’s look more closely now at each branch of the ecological perspective. Dynamical Systems Approach One branch of the ecological systems perspective is called the dynamical systems approach. In the early 1980s, a group of scientists—working at Haskins Laboratory in New Haven, Connecticut, whose mission was and remains to research spoken and written language, and in the psychology department at the University of Connecticut—began to question the effectiveness of understanding motor control through the then-dominant information processing perspective. Peter Kugler, Scott Kelso, and Michael Turvey (1980, 1982), along with others from UConn and Haskins Laboratory, introduced a new approach, called dynamical systems, as an alternative to existing motor control and coordination theories. Following the writings of Soviet physiologist Nikolai Bernstein, they suggested that the very organization of physical and chemical systems constrains behavior. Think about it: Your body can move in many different ways. However, because of the structure of your hip joints and legs (part of your skeletal system), you, as an adult, tend to walk (as opposed to crawl, scoot, or squirm) as a primary mode of transportation. Thus, the structural organization of your body encourages—constrains—you to walk. In other words, your body’s structure removes some of the movement choices your CNS might have to make (i.e., among crawling, scooting, squirming, or walking). It’s not that you cannot perform these movements; it’s just that because of your body structure, you are more attracted to (or constrained to) walking. Imagine that a human infant is born in a space station on the moon. Predict the types of movements you would see during the first 2 years of life. In particular, how would you expect the infant to get around? Unlike the maturational and information processing perspectives, the dynamical systems approach suggests that coordinated behavior is softly assembled rather than hardwired, meaning that the interacting constraints in your body act together as a functional unit to enable you to walk when you need to. By not having a hardwired plan, you have greater flexibility in walking, which allows you to adapt your walk to many different situations. This process is called spontaneous self-organization of body systems. As we state in chapter 1, movement emerges from the interaction between constraints (individual, environmental, task). The resultant behavior emerges or self-organizes from these interrelationships. If you change any one of them, the emergent movement may change (Clark, 1995). This is the concept of constraints in the dynamical systems approach. An important motor development concept produced by the dynamical systems approach is 65 the notion of rate limiters, or controllers. The body’s systems do not develop at the same rate; rather, some might mature quickly and others more slowly, and each system should be considered a constraint. Consider the hypothetical example graphed in figure 2.3. The development of four hypothetical systems is pictured in each of the small graphs numbered 1 to 4. As time passes, the development of system 1 remains at a constant value. System 2 plateaus, advances in a large step, then plateaus again. System 3 advances gradually and more continuously, whereas system 4 alternately advances and plateaus in a steplike fashion. The exhibited behavior, represented in the large graph, is affected by all the individual systems as they interact among themselves and with the task and environment. A rate limiter, or controller, is an individual constraint or system that holds back or slows the emergence of a motor skill. An individual might begin to perform a new skill, such as walking, only when the slowest of the necessary systems for that skill reaches a certain point. Any such system or set of systems is known as a rate limiter, or controller, for that skill because that system’s development controls the individual’s rate of development at that time. In other words, the system acts as a constraint that discourages the motor skill until the system reaches a specific, critical level. Suppose that system 4 in figure 2.3 is the muscular system. Perhaps an infant’s muscular strength must reach a certain level before the legs are strong enough to support the infant’s weight on one leg in order to walk. Hence, muscular strength would be a rate limiter, or controller, for walking. Until the infant reaches a critical level of leg strength (enough to support the body), strength discourages walking and encourages other forms of transportation, such as creeping, crawling, or rolling. The notion of rate limiters fits well in the model of constraints. 66 Figure 2.3 Four developing systems are depicted as contributing to behavior in an environmental context and for some particular task. The horizontal axis is time, and the vertical axis represents several parallel systems (which act as constraints) developing in different ways. Adapted by permission from Thelen, Ulrich, and Jensen 1989. These tenets of the dynamical systems approach differ significantly from those of the maturational perspective. Maturationists tend to focus on the CNS as the only system relevant to development and the only rate controller. The dynamical systems approach focuses on many systems and acknowledges that different systems might act as rate controllers for different skills (Thelen, 1998). In your experience, what rate limiters have affected your motor behavior? How have these changed at different times in your life? Another feature of the dynamical systems approach is that it allows for the study of development across the life span. The concept of a system acting as a rate limiter, or controller, for a movement behavior applies into older adulthood as well. The maturational perspective does not address aging because the predetermined endpoint of motor development is maturation, which occurs in the first several decades of life. In contrast, the dynamical systems approach accounts for changes in older adults as well as advancement in youths. When one or more of an individual’s systems has declined to a critical point, a change in behavior might occur. This system is a rate controller; because it is the first to decline to some critical point, it triggers the reorganization of a movement to a less-efficient pattern. For example, if an individual’s shoulder joint deteriorates as a result of arthritis and loses flexibility, at some point that individual might have to use a different overhand throwing motion or even throw underhand. The dynamical systems approach is appropriate for explaining developmental changes because changes do not necessarily occur in all systems over the entire span of older adulthood. Disease or injury may strike one system, and systems may be differentially affected by lifestyle. An active older adult who maintains a regular and balanced exercise program may experience fewer declines in many of the systems than a sedentary peer does. 67 Perception–Action Approach The second branch of the ecological perspective is the perception–action approach. J.J. Gibson proposed this model in his writings during the 1960s and 1970s (1966, 1979), but those who study movement have only recently adopted this approach. Gibson proposed that a close interrelationship exists between the perceptual system and the motor system and emphasized that these systems evolved together in animals and humans. In this approach, we cannot study perception independent of movement if our findings are to be ecologically valid—that is, applicable to real-world movement behavior. Likewise, the development of perception and the development of movement must be studied together. In addition, we cannot study the individual while ignoring the surrounding environment. Gibson used the term affordance to describe the function an environmental object provides to an individual; this is related to the size and shape of the object and the individual in a particular setting. For example, a horizontal surface affords a human a place to sit, but a vertical surface does not. A squirrel can rest on a vertical tree trunk, so a vertical surface affords a squirrel a resting place. A baseball bat affords an adult, but not an infant, the opportunity to swing. Hence, the relationship between individual and environment is so intertwined that one’s characteristics define objects’ meanings, which implies that people assess environmental properties in relation to themselves, not according to an objective standard (Konczak, 1990). For example, an individual perceiving whether he or she can walk up a flight of stairs with alternating footsteps considers not just the height of each stair alone but also the height of each stair in relation to the climber’s body size. A comfortable step height for an adult is not the same as that for a toddler. The use of intrinsic (relative to body size) rather than extrinsic dimensions is termed body scaling. When a person looks at an object, he or she directly perceives the function that the object will allow, based both on his or her body and on the object’s size, shape, texture, and so forth. This function is called an affordance. Body scaling is the process of changing the dimensions of the environment or an environmental object in relation to the structural constraints of a performer. Web Study Guide Create body-scaled contexts for individuals with different disabilities by doing Lab Activity 2.1, Equipment That Affords Action, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. The implications of these ideas for motor development are that affordances change as individuals change, resulting in new movement patterns. Growth in size, for example, or enhancement in movement capabilities, might allow actions not previously afforded. When an infant first faces stairs, her perception of their function is not likely to be one of 68 “climbability” because of her small size and relative lack of strength. As a toddler, though, she grows to a size that makes climbing stairs with alternating footsteps easy. Scaling environmental objects to one’s body size permits one to conceive of actions that otherwise seem impossible. Body scaling also applies to other age periods. For example, steps that are an appropriate height for most adults might be too high for an older adult with arthritis to climb comfortably with alternating footsteps. A wall-mounted light switch might be at a comfortable height for most adults yet rest a frustrating inch too high for someone sitting in a wheelchair. At any age, achievement of a movement goal relates to the individual, who is a certain shape and size, and to environmental objects, which afford certain movements to that individual. Key Point The concept of body scaling means that an object, though possessing an absolute size and shape, affords a function relative to the size and shape of the person using it. An activity may become easier or more difficult if the equipment size is changed relative to a person’s body dimensions. Viewed another way, body scaling represents an excellent example of the interaction or interface between individual and task constraints. When walking up stairs, individuals must relate the length of their legs, their strength, and their dynamical range of motion (individual constraints) to the height of the stairs they are about to step on (task constraint). Changes in constraints, such as an ankle sprain, high-heeled shoes, or an icy staircase, will result in changes in the way an individual steps on the stairs. In physical education settings, instructors often assist children in body scaling by providing them with smaller equipment that is more appropriate for their smaller body size. By doing so, instructors manipulate the interaction between individual and task constraints to encourage a more advanced movement pattern (Gagen & Getchell, 2004). What activities would a 10-speed bicycle afford an infant, a typically able adult, an individual with paraplegia, or a chimpanzee? What individual constraints affect the activities afforded to each? Gibson also rejected the notion of a CNS executive that performs almost limitless calculations on stimulus information to determine the speed and direction of both the person and the moving objects. The information processing perspective holds that such calculations are used to anticipate future positions so that we can, for example, reach up to catch a thrown ball. Instead, according to Gibson, individuals perceive their environment directly by constantly moving their eyes, heads, and bodies. This activity creates an optic 69 flow field that provides both space and time information. For example, the image of a baseball approaching a batter not only indicates the ball’s location but also expands on the eye’s retina, and the batter uses the rate of image expansion to time his swing—that is, the rate of expansion gives the batter’s CNS direct information about when the ball will be in range. Likewise, the expansion rate of the image of an oncoming car on a driver’s retina yields the time to collision. From Gibson’s perspective, an individual can perceive time to collision directly and does not need to perform a complicated calculation of speeds and distances to predict where and when collisions and interceptions will occur. The ecological perspective has been taking hold in motor development research throughout the past two decades. Developmentalists are asking different types of questions: How does an infant’s immediate environment affect her motor behavior? What constraints act as rate limiters to children’s throwing? How will changing specific individual constraints in a rehabilitation setting alter motor patterns? Concurrently, they have developed types of research studies, such as examining the relationship between infant reflexes and adult movements (Thelen & Ulrich, 1991). The ecological perspective encourages professionals to view developing individuals in a very different way than before. As a result, these perspectives will excite and challenge students in the field. In many sections of this text, we examine the maturation and dynamical systems approaches on a particular issue and highlight the differences between these perspectives. 70 Summary and Synthesis This chapter reviews the history and theoretical viewpoints specific to motor development —the maturational, information processing, and ecological perspectives. The maturational perspective emphasizes biological development, specifically maturation of the CNS. The information processing perspective sees the environment as the main force driving motor development. Unlike followers of the previous perspectives, ecological theorists stress interaction between all body systems (or, as Newell called them, constraints) as well as the inseparable factors of individual, environment, and task. In the ecological perspective, two related approaches to research exist: dynamical systems and perception–action approaches. This textbook adopts an ecological perspective and focuses on how individual, environmental, and task constraints interact to encourage or discourage movements. The concepts of rate limiters, or controllers, and body scaling exemplify how a motor developmentalist must consider the individual’s performance of a particular task in a given environment in order to fully understand individuals’ motor development over the course of a lifetime. Diametrically opposed perspectives cannot be merged, but students of motor development are free to view behavior from different perspectives. It is important to remember that these theoretical viewpoints often focus only on specific aspects of development; that is, developmentalists with a particular perspective tend to study certain behaviors or age spans. Maturationists focus on infancy, whereas descriptive developmentalists focus on late childhood and adolescence. Information processing theorists search for age differences, whereas those studying from an ecological perspective observe transitions from one skill to another (e.g., from crawling to walking). Reinforcing What You Have Learned About Constraints Take a Second Look Let’s revisit the first 18 months of your hypothetical nephew’s life from an ecological perspective. Thinking about his first week of life, what constraints most influence his motor behavior? That is, what rate limiters keep him moving the way he does? As we suggest in chapter 1, in order to understand developmental change, we should think about where he was, where he is, and where he will be. The first big change, therefore, would be moving from an aqueous environment (his mother’s womb) to one where gravity’s full force can be felt. Keeping this in mind, part of the reason your nephew may move his arms in a seemingly uncoordinated fashion may relate to his strength (or lack thereof). As his environment changes, a need is created for greater strength to move his arms (individual– environmental constraint interaction). Over time, he builds strength, which interacts with 71 other developing systems. When you see him at 9 months, all the systems have converged to allow him to use his arms more effectively. In terms of the rate limiter of strength, he has reached a critical level that allows him to move more functionally. He has sufficient strength to sit up, move his arm toward a toy, grasp it, and bring it to his mouth. He even has enough strength to stand on both legs and support his weight (with a little help from you). Consider his next objective: moving independently around his environment. He might eye a chair (which to you affords sitting), scale the height of the seat to the length of his trunk, and pull himself up to a standing position. Can he walk yet? Probably not, as another rate limiter—balance—prevents him from standing alone unsupported. We could go on and on, examining the different ways in which your nephew uses affordances and body scaling, and you can think of examples on your own. Hopefully the interactions between the various constraints become clearer and clearer to you, and by the end of the text you will naturally evaluate the influence of different constraints on motor development. Test Your Knowledge 1. List the key researchers in the field of motor development from the maturational, information processing, and ecological perspectives. 2. How can a physical educator or physical therapist use the concept of body scaling to help individuals develop motor skills? 3. Why should physical educators be interested in affordances? 4. Explain, based on the different theoretical perspectives, how an infant learns to walk. What are the most important influences on the infant, according to each perspective? Learning Exercise 2.1 Body Scaling to Design Sport Equipment You have been hired by Haywood’s Tennis Supplies to design a line of body-scaled tennis rackets. Because the company would like to distinguish its product line from others on the market, you must prepare an initial report addressing important aspects of the new rackets, available product lines from other companies, and how your rackets will be different. Develop this report; you can use the following questions as a guide. 1. What are the important individual constraints to consider when body scaling tennis rackets? 2. How have other companies body scaled their rackets? On what individual constraint or constraints do they scale? 3. What are some novel ways to scale Haywood’s new rackets? What have other companies overlooked? 72 Learning Exercise 2.2 Hunting for Rate Limiters in Everyday Situations Physical educators, physical therapists, parents, and many others want to encourage proficient motor skills in those with whom they interact. One important consideration in working to improve motor skill performance is this: What is holding a person back, or limiting the rate at which he or she acquires a skill? In this exercise you will determine the key rate limiter for a particular skill, given the constraints described. 1. An 11-month-old infant can use furniture to pull himself upright, can cruise the length of the couch if he keeps one hand in contact, and can push a toy shopping cart down the hall. However, when placed standing in the center of the room, he does not walk but rather gets down on all fours and crawls. What is his primary rate limiter for walking? 2. A stroke patient has control over her limbs and has little difficulty walking. She can lift a pencil and write lists and letters. She can comb her hair and brush her teeth. She experiences problems, however, when she tries to lift cans and jars overhead onto shelves. What is her primary rate limiter for reaching? 3. A 5-year-old can easily walk, run, jump, and hop. She plays games with other children on the playground and is very attentive in physical education class. She has problems, however, with galloping and skipping; she cannot seem to master the asymmetrical rhythms of these skills. What is her primary rate limiter for galloping and skipping? 73 Chapter 3 Principles of Motion and Stability Mechanical Laws Guiding Constraint Interactions 74 Chapter Objectives This chapter outlines the principles of motion and stability that lead to proficient motor performance, discusses the relationships between these principles and motor behaviors of individuals of various skill levels, and explains how skilled performers take advantage of specific principles. 75 Motor Development in the Real World Running to the Best of Her Abilities In 1976, Aimee Mullins was born with fibular hemimelia, a congenital condition that results in malformed or absent fibula bones. As a result, she had both her legs amputated below the knee by her first birthday, and doctors believed she would never be able to walk. To some, pursuing an athletic career with this condition may seem daunting or even impossible. Not to Aimee. Early in her childhood, she began walking with prosthetic limbs. By her high school years, she participated in a variety of sports, including softball, downhill skiing, and track. In fact, Aimee was so fast she competed against able-bodied athletes while attending NCAA Division I Georgetown University, becoming the first amputee ever to do so. Aimee also competed in the 1996 Atlanta Paralympics, setting three world records in track events. For Aimee, running at the best of her abilities means running very, very fast. Aimee Mullens, a world-class athlete, model, and actor, has never let physical differences become obstacles to success. Rather, she challenged conventional wisdom by finding a way to excel in athletics against extreme odds. Both beautiful and eloquent, Aimee discussed the different designs of her prosthetics in the 2009 Technology, Entertainment, Design talk titled “My 12 Pairs of Legs.” Her use of a mechanically efficient design in the 1996 Paralympics helped revolutionize the use of “cheetah” polycarbon-fiber sprinting legs, which are more biomechanically efficient than other legs and are the gold standard for athletes today. Such a design takes advantage of certain principles of motion and stability in order to improve the energy efficiency of the runner. As we note in chapter 1, many elements of motor development tend to occur similarly in different people. That is, humans change patterns of motor behavior in a somewhat predictable fashion. This similarity is not surprising when you consider that most humans share similar individual constraints (two arms, two legs, and upright posture). Similarities also occur because humans operate under a system of rules or principles that dictate how constraints interact in the context of life on this planet. Humans on Earth live in a context that features certain predictable characteristics, such as gravity. Part of the process of developing motor skill involves learning how the human body works within the boundaries of these physical laws. During early life this is not a simple task because individuals must learn to refine their movements while experiencing changes in physical parameters. Furthermore, they must learn to calibrate their movements to the environment while performing specific tasks based on these principles. (Think, for example, of picking up an empty box you thought was full. You must quickly recalibrate your movements in order to 76 maintain your posture or you will fall over.) The process of calibrating movements to task and environment in accordance with mechanical principles can also be difficult for individuals who develop atypically or who must relearn skills after an injury. This chapter considers the physical and mechanical principles under which humans move. In the context of motor development, these are known as the principles of motion and stability. How would you describe the movements of early, inexperienced movers? Often, their initial attempts at performing skills seem to be inefficient and jerky. They may move the body in separate, discrete steps rather than in a whole movement. They often try to optimize one aspect of the movement (e.g., balance) at the expense of another (e.g., speed) to increase the likelihood of success. As individuals become more proficient at skills, their movements become smoother and more efficient; indeed, their movement patterns often change entirely. Many of children’s improvements are due to increases in body size and strength and therefore in their ability to produce force. Yet size and strength alone do not account for how children progress from unskilled to skilled performance. Another part of the process of change involves mastering and exploiting the principles of motion and stability. In fact, all individuals can use these principles to their advantage, in athletic performances as well as in activities of daily living. 77 Understanding the Principles of Motion and Stability Movements occur in a context that is governed by certain principles of motion and stability; that is, certain physical laws of motion limit your movements. Consider gravity as an example of a rule that dictates how constraints interact and objects move. The simplest way to think about gravity is that all objects are attracted to each other and that the amount of attraction depends on the objects’ masses. Because the mass of the earth is so large, objects on its surface are drawn toward its center. If you jump up, you don’t continue to rise. Instead, you come back toward the earth as a result of gravity. What goes up must come down (figure 3.1). The force of gravity exists all around us, dictating that each of us must eventually return to Earth after jumping. Now, let’s look at this rule as it applies to constraints. Given that all objects eventually return to Earth, people must calibrate their movements based on their individual constraints (e.g., overall body mass and strength), acting in an environment governed by specific task rules. The interaction of constraints in this context encourages certain motor patterns while eliminating others. What are some of the behaviors encouraged by gravity? An individual must activate certain postural muscles to assume and maintain a position, even while executing a skilled movement. Furthermore, she must work against gravity to become airborne. If a person projects herself or an object at an angle (as opposed to straight up and down), then the force of gravity will ensure that the flight path is parabolic. As you can see, then, principles of motion influence the interaction of constraints. 78 Figure 3.1 Because gravity acts on the long jumper equally and at all times, his flight path resembles a parabola, or semicircle. Key Point Principles of motion and stability act on all movements and movers. As movers become more proficient at skills, they often use these principles to their advantage. At the same time, the individual constraints or characteristics of the performer influence the movement pattern undertaken. People throw using a movement pattern that is dictated by the shape and structure of the human body and limbs. Consider the shape and structure of the bones in the shoulder (figure 3.2). Muscles also have particular functional shapes and sizes, and they connect bones to each other. In addition, the nervous system coordinates muscular contractions. Furthermore, individuals use their bodies to move with a particular task goal in mind, which also acts to constrain movements. Here lies the interconnectedness of constraints: The individual, with a task goal in mind, acts in the environment to perform a skill. Thus, individual, environment, and task interact to shape or constrain a movement pattern. 79 Figure 3.2 The structure of the articulating bones in the shoulder joint encourages movement in some directions while preventing it in others. For example, the acromion process prevents movement of the upper arm past 90° without movement of the scapula. Reprinted from Donnelly 1990. Key Point Children may execute the most efficient movement pattern for themselves given their body size, strength, posture, and experience, but this pattern can change if any one of the constraints changes. Clearly, some movement patterns optimize the product of skill performance whereas others do not. Given the task of throwing a rock as far as possible, an individual can produce a variety of motor patterns that will move the rock, but only one will move the rock as far as possible. To develop their skills, children and adults must learn to use movement patterns that optimize performance. The changes taking place in children’s bodies complicate this process because growth alters an individual’s overall size and proportions. As children grow and mature, their skeletal, muscular, and nervous systems allow them to produce greater force. Changing bodies mean changing individual constraints, and the individual must recalibrate the interactions between individual and environmental 80 constraints. By taking advantage of the principles of motion and stability, children discover qualitatively different movement patterns that improve the outcome of their skill performance. Thus, young children, given their body size, shape, and strength, might execute what is for them the most successful or efficient movement pattern possible. However, as they grow, mature physiologically, and gain experience, other movement patterns become possible that allow them to execute skills with greater proficiency (Gagen & Getchell, 2008). Injury or disability can change individual constraints for either short or long periods. Affected individuals must relearn how to use mechanics of movement given their unique body structure and function. Adults can also take advantage of these principles of motion and stability. Just as with children, an adult might execute the movement pattern that affords the greatest success, but with time and experience (and, in some cases, with technologically advanced equipment) new patterns may emerge that provide for more adept performance (Getchell & Gagen, 2006). Key Point Observations of developmental change in basic skill performance benefit from application of the principles of motion and stability. Understanding the principles of motion and stability is critical in observing motor performance. These principles help us determine which movement patterns are likely to produce optimal results. Knowledge of the principles also helps us focus on critical aspects of movement that often distinguish skilled movement patterns from unskilled ones. Thus, taking advantage of these principles can help us get much more out of our movements. For these reasons, this chapter reviews and evaluates the principles of motion and stability—the physics of movement—as they apply to basic skill performance. Only some of the more salient principles are discussed here; a more complete analysis of the physics of movement falls in the domain of biomechanics—the mechanics of muscular activity—which is an area of study in itself and which lies beyond the scope of this text. A more detailed explanation of these principles can be found in any biomechanics text (Hall, 2006; Knudson, 2007; McGinnis, 2005). Moving Against Gravity: The Application of Force To move either themselves or objects, individuals must produce force; a stationary object or individual will not move until some force is applied to it. You may recognize this as Newton’s first law of motion: An object at rest stays at rest, or an object in motion stays in motion, until acted on by a force (McGinnis, 2005). You probably understand Newton’s 81 first law without ever thinking about it. Or perhaps if you were a messy teen, your parents told you, “If you leave your socks on the floor, they will be there when you get back!” This is an excellent example of Newton’s first law. Simply put, to move something, you must apply force to it. Lie on the floor on your back and then stand up. What are some of the different ways you can achieve this task goal? Do some movements seem more comfortable or efficient than others? Why? According to Newton’s first law of motion, you must apply force to a stationary object to move it and apply force to a moving object to change its movement.Newton’s first law is relatively simple and straightforward: It takes force to move something standing still and force to change the direction of something moving. Newton’s second law of motion is related to force production, acceleration, and mass. Basically, the amount of acceleration an object has when you apply force to it is proportional to the force and inversely proportional to its mass. This relationship is easier to understand when described with examples. It takes more force to “put” or throw a shot (which has more mass) than to throw a baseball (which has less mass) at the same speed. Also, if you kick a soccer ball harder (more forcefully), it will accelerate faster and thus go farther. Understanding Newton’s second law is important when attempting to move proficiently. Newton’s second law of motion states that the acceleration of a person or object is proportional to the force applied to it and inversely proportional to its mass. You can use these laws with other principles related to force to understand how to maximize performance (Gagen & Getchell, 2008). First, a relationship exists between force applied and the distance over which you apply it. You can improve your performance by applying force over a greater distance. For example, a young child throwing a ball may throw the ball without moving her legs. By keeping her feet in place, she can reduce compromises to her balance; however, the ball will not travel as far. An experienced thrower will take a step forward with her opposite leg, thus increasing the linear (straight-line) distance over which the force is applied. Try throwing a ball with and without a step and you will discover how much increasing your linear distance with a step aids your performance. We can also consider rotary or angular distance in addition to linear distance. When you throw a ball, your arm rotates around a joint; hence, it moves a certain number of degrees, or a certain angular distance. By increasing their range of body motion, individuals can increase the rotary distance over which force is applied and therefore maximize their performance. Using a preparatory windup puts the performer in a position to maximize the linear and rotary distances of force application. The preparatory positioning also stretches the muscles the performer will use, thus readying them for maximal contraction. These 82 actions permit the person to project the object at a greater velocity than could be accomplished without a windup and a full range of motion. Imagine you are a physical educator. Throw a ball with and without using a contralateral (opposite side) step. What differences do you find in several quantitative measures (e.g., distance, accuracy)? What differences do you find in movement form? What might be the reason for these changes? It is important to note that in most movement skills an optimal relationship exists between force and distance. That is, simply increasing linear or angular distance for a given force will not automatically result in an improved performance. The performer must first recognize what is a complete or full range of motion for a given skill. By observing children during movement, you will see them begin to explore relationships between force and distance (Gagen & Getchell, 2008). The changes in movement patterns related to the application of force often allow for a greater velocity and may come at the expense of stability. You can imagine that although a soccer player generates a lot of force in her leg and therefore in a kick, it would not take much effort for another person to move her from her spot on the field (figure 3.3). 83 Figure 3.3 When trying to optimize performance of a skill, athletes must learn the proper relationship between force and distance. This soccer player takes a step to increase distance; any longer step would lead to instability. Key Point To improve movement performance, individuals must find the optimal relationship between force and distance in a given movement. Two important phases in this process are preparation (preparatory movement) and the application of force through a full range of motion. Imagine you are the coach of a wheelchair basketball team. How could a wheelchair basketball player take advantage of Newton’s first and second laws and the related principles of motion? Moving Against Gravity: Action and Reaction When observing changes in motor behavior across the life span, we also notice that performers take advantage of Newton’s third law of motion: To every action there is an equal and opposite reaction. This means that if you exert a force on an object, it exerts a force (equal and opposite) back on you. This may seem confusing at first, but an example may make it clearer. As you walk, you push down on the floor or surface you walk on. What would happen if the floor did not “push” back up on you? If you guessed that your foot would go through the floor, you are right. Perhaps you have experienced this situation when walking on thin ice or unstable floorboards. 84 Newton’s third law of motion, the law of action–reaction, states that for every force you exert on an object, the object exerts an equal force back on you in the opposite direction. How does the law of action–reaction affect movement patterns? To move forward while walking, an individual must push down and back so that the surface can push up and forward. Watch the movement pattern of newly walking toddlers. Much of their force is directed downward, not backward. This allows them to move without compromising their balance; however, forward progression is slow. With more walking experience, individuals begin to exert more force backward and therefore move forward faster. Consider the implications of Newton’s third law: If every action that a performer makes results in an equal and opposite reaction, then any forces that are applied outside of the plane of motion will lead to undesired reaction forces. These forces will in turn detract from the performance (Gagen & Getchell, 2008). For example, if you want to move forward, then any force exerted in other directions will make your walk less efficient. Athletes attempting maximal performance, such as kicking for the maximal force possible, want to exert as much force as possible in one plane of motion. In a skill such as kicking, maximal effort is characterized by full extension (straightening) of the striking limb. We can also see the law of action–reaction applied among parts of the body. For example, in locomotor skills such as running, the lower body twists one way and the upper body twists the opposite way. One leg swings forward and the arm on that side of the body swings backward in reaction; thus, the leg on one side of the body and the arm on the opposite side swing forward and back in unison. This familiar pattern is termed oppositional arm and leg movement and is a characteristic of skilled locomotor movements. Imagine the result of several everyday movements if the law of action– reaction did not exist. Have you ever experienced a situation in which you expected a reaction force and it did not occur? What happened? Relationship Between Rotating Limbs and Projected Objects As we discussed previously, individuals move when their limbs rotate around one or several joints. This is called rotational movement. In essence, when projecting an object (e.g., throwing or kicking), an individual’s limb traces part of a circle—the arm travels in an arc in throwing, and the leg does so in kicking. Releasing or striking an object causes it to fly away from this curved path in a straight line from the release or impact point. A relationship exists between the velocity of the rotating arm and the velocity of the projected object. For example, if a baseball player throws a ball from the outfield, the velocity of the ball as it leaves the player’s hand is dependent on how fast the player’s arm moves and on 85 the length of his arm at the release point. In more scientific terms, an object’s linear velocity is the product of its rotational velocity and its radius of rotation. What does this mean in terms of optimizing performance? First of all, as children grow, their limb length increases, which should lead to changes in projectile velocity (and consequently distance) that may appear even without changes in movement form (Gagen & Getchell, 2008). Increases in rotational velocity also cause these changes. However, at any given time, people cannot increase their absolute limb length or rotational velocity. What if an athlete already rotates his limb as fast as possible? There is one other way to increase a projected object’s velocity. The athlete must extend his limb—and thus increase the radius of rotation—by straightening it out just before the point of release or contact. Consider a tennis server trying to get the most velocity possible on his first serve (figure 3.4). At the point of contact, the player’s arm is extended as far as it can be, which will increase the ball’s release velocity. 86 Figure 3.4 To increase the velocity of a tennis ball, a tennis player extends his arm (thereby increasing his radius of rotation) as much as possible at the moment of contact. Key Point Individuals can increase the velocity of an object they project (throw, kick, hit) by extending the rotating limb as much as possible at the point of release. Imagine you are a physical educator. In which sports and physical activities, besides tennis, do athletes extend their limbs to increase the velocity of a projected object? Try to think of three obvious examples and three not-so- obvious examples. At this point, you may wonder why skilled athletes do not keep their limbs extended as far as possible during the entire movement. If an extended limb leads to greater velocity, why do athletes often begin their movements in a bent or cocked position and not straighten the limb throughout? The answer lies in another law of motion: that of inertia. You have probably heard the term inertia; it refers to an object’s resistance to motion, and it is related to the mass of the object. When dealing with rotating objects (e.g., arms and legs) in sports and physical activities, resistance to motion depends not only on mass but also on limb 87 length. As the limb length increases for a given mass, so does the resistance to motion. As the resistance to motion increases, so does the amount of energy required to move the object. In short, bending the limb decreases the energy necessary to move the limb. Let’s consider several skills in which athletes take advantage of these two principles to maximize performance. First, consider sprinters competing in the 100 m dash (figure 3.5). Just before contact with the ground, the sprinters fully extend their legs to maximize their projected velocity. However, the sprinters bend these limbs as they recover and swing forward. This conserves energy and effort in that limb. Another example is that of batters swinging at pitched balls. The batters conserve effort at the beginning of the swing by keeping their elbows bent. Just before the point of contact, the batters extend their arms as fully as possible. 88 Figure 3.5 A sprinter changes relative leg length during the course of a stride. The leg is bent to decrease momentum and energy requirements; it is then extended to increase force production. Open Kinetic Chain A person can toss or kick an object a short distance with a flick of the wrist or a tap of the toe. But a maximal ballistic effort must involve not only more body parts but also sequential movement of those parts. The sequence must be timed so that the performer applies the force of each succeeding movement just after the previous movement to accelerate the object. For example, recall that an effective thrower steps forward and rotates the pelvis, then rotates the upper trunk as the throwing arm comes forward, extends, and rotates inward. We term a movement sequence such as this an open kinetic chain of movements. Open kinetic chain refers to the correctly timed sequence of movements an individual uses to successfully perform a skill. Key Point One of the most significant changes we see in the skill development of children and beginners is the transition from using a single action to executing skills in a pattern of efficient, properly timed sequential movements. Imagine you are a physical therapist. How important is sequencing and 89 timing to activities of daily living? What results from changing either sequence or timing in a given movement? Two elements are essential to the open kinetic chain of movements. First, an optimal sequence of movements exists. Equally important is the timing of events in this sequence. For example, we have discussed how taking a step before a throw increases the amount of work done; however, the step must occur immediately before the throw in order to provide benefit. You may have observed individuals—particularly children—learning to throw who take a step but do not time the step and throw together. In fact, beginners often use single actions or movements when executing motor skills. As they continue to perform these skills, they begin to link together and time these single movements; as a result, they become more proficient movers. Force Absorption Have you ever landed from a jump without bending your knees? If so, you understand the concept of action–reaction firsthand: The earth immediately returned the force of your landing body back to you. Obviously, you do not want to land this way every time you jump because injury will surely ensue. To decrease the impact of the landing, you simply bend your knees. Bending your knees during landing increases the time and distance of the landing. This brings us to our next principle of motion, related to force absorption. Simply put, to decrease the impact of a reaction force, you must either increase the amount of time in which the impact occurs or increase the area over which the impact occurs (Carr, 1997). You can observe individuals using this principle to decrease impact force in a wide variety of motor activities. When catching a ball, a softball player extends the arms in front of the body, then brings the glove, hand, and arm into the body; this sequence helps absorb the force of a hard-driven ball. Gymnasts are experts at landings, bending their hips, knees, and ankles as fully as possible. Individuals practicing judo increase the time and area of their falls, rolling from the arms to the back (Gagen & Getchell, 2008). Using this principle of motion is important to preventing injury when attempting a maximal performance. Key Point To absorb forces transmitted to their bodies, individuals must increase the amount of time of the impact (by allowing the limbs to flex) or increase the area over which the force is absorbed. Stability and Balance 90 We have discussed principles of motion, but principles of stability are equally important. Most people would have difficulty optimizing their movements from an unstable position. That is, stability and balance are essential elements of many sports and other physical activities. Some activities, such as powerlifting and golf, require maximum stability. In others, such as judo and wrestling, athletes try to maintain their stability while disturbing that of their opponents. Still other activities, such as gymnastics and ice skating, require athletes to maintain balance in relatively unstable positions. Based on the previous examples, you can see that stability and balance do not refer to exactly the same concept. A stable object or person is one that resists movement. You would have a difficult time tipping over a large, wide, heavy box—it is very stable. Balance, on the other hand, relates to the ability of an object or person to maintain equilibrium. If you can stand on one foot while closing your eyes, you exhibit great balance in an unstable condition. In most cases, increasing stability ensures balance; however, maintaining balance does not guarantee stability. In fact, a person may not want stability because it will inhibit mobility. People can readily become more stable by increasing the size of their base of support—for example, by spreading their legs when standing or by spreading their hands when doing a handstand. Individuals can add to their stability by keeping their center of gravity low and inside their base of support. In the example of a handstand, individuals must keep the legs directly above the trunk to maintain optimum stability; otherwise, they will either move or fall. Balance relates to the ability of an object or person to maintain equilibrium. Center of gravity is the concentration point of the earth’s gravitational pull on an individual (McGinnis, 2005). Key Point An individual’s stability is related to the ability to resist movement or disruption. You can increase your stability by increasing your base of support, lowering your center of gravity, and keeping your center of gravity within your base of support. In many activities, maximal performance of the skill requires performers to minimize stability in order to increase mobility. In locomotor skills, a person momentarily sacrifices stability (two-footed support base) in order to move by alternately losing and gaining balance (one-footed support base). The body’s weight is pushed forward, ahead of the base of support, and the person moves the leg forward to regain balance. 91 Key Point Although increasing stability leads to improved balance, it also leads to decreased mobility. Therefore, a skilled performer uses a base of support just wide enough to provide sufficient stability for his or her activity. Figure 3.6 shows individuals with varying degrees of stability and balance. In figure 3.6a, the individual is very stable and has a wide base of support and a low center of gravity. It would be difficult to move him. The athlete in figure 3.6b is less stable but more mobile. Figure 3.6c shows an athlete with great balance in a highly unstable position. 92 Figure 3.6 Different degrees of stability: (a) highly stable, (b) moderately stable, and (c) not very stable. 93 Young children, people with certain disabilities, older adults, and people learning new skills often attempt to improve balance by increasing their stability. In locomotor tasks, they will increase their base of support by planting their feet wide or out-toeing (what would be considered “duck-footing”) their feet. They keep their center of gravity well within their base of support by avoiding excessive trunk rotation or limb movement. In reception skills such as catching, they also increase the base of support and lower their center of gravity. In many cases, if such people gain greater muscle control, more experience, proper training, or confidence, they will narrow the base of support and therefore increase their mobility and ability to move quickly. Imagine you are a physical therapist. Think of four activities of daily living that range from requiring maximum stability to requiring maximum mobility. What are the consequences of losing balance in each activity? Web Study Guide Compare and contrast motion principles in different movements in Lab Activity 3.1, Searching for Movement Similarities and Differences, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 94 Using the Principles of Motion and Stability to Detect and Correct Errors Once you understand the principles that underlie human movement, you can use them to recognize and fix mechanical errors in a person’s technique. At first, the amount of information may seem overwhelming. However, Carr (1997) has provided a simple five- step process for systematically observing and analyzing skill performance. This process provides you with a straightforward method for noticing and assisting with a person’s mechanics. Step 1: Observe the complete skill. Although this seems self-evident, it is surprising how often novice teachers and coaches put themselves in a position where they cannot see a person’s complete movement. Planning before performance of the skill is key, and you should keep several concerns in mind. If you can watch the performance only once or twice, focus on only a few elements. Videotape the performance for future analysis. Make sure the person warms up before the movement and performs in a natural environment, and always ensure the safety of the person and others who may be nearby. Step 2: Analyze each phase and its key elements. Hone in on specific phases of the skill and their key elements. Break the skill into phases; for example, an overarm throw could be divided into preparatory backswing, force production, and follow- through. Next, look at a person’s performance of the skill in these phases. Carr suggests two ways to do this. One is to start with the result and work backward. If a person is trying to serve a tennis ball over a net but continues to hit the net, you concentrate on the action of the racket when it contacts the ball and then work back from that point to identify which part of the performance may be leading to this result. Alternatively, you can watch the movement from start to finish. Watch the person’s preparatory stance, balance, and weight shift before movement; then focus on each phase of the skill. Step 3: Use your knowledge of mechanics in your analysis. You have learned a good deal about mechanics, and now is the time to use it. Observers must focus on how the mover applies muscular force to generate movements. Carr suggests a series of questions to guide your analysis. Does the mover have optimal stability when applying or receiving force? Is the mover using all the muscles that can make a contribution to the skill? Is the mover applying force with the muscles in the correct sequence? Is the mover applying the right amount of force over the appropriate time frame? Is the mover applying the force in the correct direction? Is the mover correctly applying torque and momentum transfer? 95 Is the mover manipulating any linear or rotary inertia properly? Use these questions to evaluate skill performance. Remember, you are not determining a right and wrong way to perform a skill; rather, you are looking for elements of the movement that can be modified to make the skill performance more proficient. Step 4: Select errors to be corrected. Many novice performers do not move in a mechanically efficient manner. This is not necessarily good or bad—it is simply how that person performs a skill. You, as a teacher or coach, can pick out certain aspects of movement that the person can improve to become more proficient at a skill. Keep in mind that there are many different ways that people can successfully perform a skill. However, improving the mechanics in several phases of a skill allows for improved performance. Focus on major errors—a performance that lacks in the areas listed in step 3—and ignore minor problems. Work on one aspect of performance at a time. In many cases, improvement in one area will lead to improvement in several other areas. Step 5: Decide on appropriate methods for the correction of errors. Educators can have different ideas about how to teach or coach motor skills. It’s best to take a class or attend a coaching clinic to learn a variety of activity-specific teaching methods. Regardless of which methods you use, here are some keys to remember. First and foremost, keep safety in mind when attempting to correct errors. Highly complex skills and skills that involve flight can become dangerous if the mover diverts attention from the skill to try to correct errors. Next, communicate with your students in understandable language rather than mechanical terms. Consider also how much time you have with the students to correct errors; this will dictate the number of errors you attempt to correct. Finally, use outside resources, such as textbooks or the Internet, to help you find new and innovative ways to teach movement skills. One such website, the Coaches’ Information Service of the International Society of Biomechanics in Sports (www.csuchico.edu/isbs), is filled with helpful biomechanical information for coaches. Web Study Guide Uncover motion principles underlying developmental change in three skills (Lab Activity 3.2: locomotor movements; Lab Activity 3.3: ballistic skills; Lab Activity 3.4: manipulative skills) in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 96 Summary and Synthesis Mechanical principles govern all of our movements. The principles themselves act as constraints in that they dictate how an individual interacts with the environment when performing a task. For example, the force of gravity is an environmental constraint that influences how we move on Earth. These principles include Newton’s laws of inertia, acceleration, and action–reaction as well as relationships based on these laws dealing with force production and absorption, open kinetic chain, and stability and balance. These principles define relationships that dictate how we move. Over time, individuals gain understanding of these principles (either explicitly or implicitly) and can learn to control certain factors that allow them to perform skills more proficiently. The qualitative changes in motor performance that occur during childhood reflect changes in the interaction between the environment and the changing individual constraints in the growing child. The progress of children, beginners, and relearners is characterized by their selection of movement patterns that increasingly optimize the movement product in a manner consistent with the principles of motion and stability. The major mechanical principles involved in efficient, skilled movement include the application and absorption of force as well as action and reaction, linear and rotational velocity, sequentially timed movements, and stability and balance. Knowledge of these principles allows us to generalize across various basic skills. We need not approach developmental changes in each of the basic skills as a completely new study because some aspects of change in skills overlap, especially in the categories of locomotion, ballistics, reception, and skills requiring balance. Reinforcing What You Have Learned About Constraints Take a Second Look Aimee Mullins has challenged preconceived notions of ability and disability her entire life. While in college, she successfully competed against able-bodied athletes in track and field. In recent years some have argued that “cheetah” legs provide an unfair advantage for the individual using them (most notably, Oscar Pistorius was barred from international competition in 2007). Both during and after her athletic career, Mullins argued that track athletes with and without prosthetics should compete against each other on national and international stages. Focusing on only the idea that “cheetah” legs are mechanically efficient overlooks the fact that the resultant movement comes from an interaction of many constraints. In other words, each athlete must master the different principles of motion and stability with respect to their own individual constraints. 97 Test Your Knowledge 1. Describe some characteristics of two different motor skills that indicate that the individual is optimizing stability instead of mobility. 2. List Newton’s three laws and explain their relationship to movements. 3. What are some of the ways in which a baseball player can increase the force of a throw? 4. How can a child’s growth contribute to greater proficiency at certain motor skills? Learning Exercise 3.1 Understanding the Relationship Between Forces and Balance What are the influences on balance when you stand on one leg? Start by standing and balancing on one leg. Now, move your support foot. How does this affect balance, and what is the ideal position? Next, move your free leg to different positions. How does this affect balance, and what is the ideal position? Repeat this process with your arms and head until you find your own ideal balance position. After you finish, compare your ideal position with that (or those) of a partner (or group). Learning Exercise 3.2 Examining Principles of Motion and Stability in Specific Sports Skills In this exercise you will examine the interplay between force and balance in certain sports skills. Start with the overarm throw. Throw a ball as hard as you can. 1. Where does your balance come from? Examine the specific movements that lead to greater balance. 2. Where does your force come from? Examine the specific movements that lead to greater force production. 3. Now, consider the relationship between force and balance. Describe how you would have to change your movements to create more force. What would have to change so that you could maintain balance when doing the movement as revised for more force? 4. Try to make these changes in your movements. What happens? Try this experiment for different sports skills, such as a volleyball spike, a football punt, or a soccer kick. 98 Part II Physical Growth and Aging One reason the model of constraints is so useful to those studying motor development is that it shows how physical growth and aging, as changing individual structural constraints, in turn change individuals’ interactions with the environment and with the task—and therefore change movement. The change in individual structural constraints through growth is particularly dramatic, as is evident on a whole-body level and a system level. That is, not only does the whole body change in size and proportion, but the body’s systems (e.g., skeletal, muscular, endocrine) also change. Changes are more subtle but still present in aging. Chapter 4 considers the typical pattern of growth and aging of the body as a whole. It emphasizes body size and proportion as well as maturity. Chapter 5 examines five body systems and how they change over the life span. These are the five systems most related to the performance of motor skills. Taken together, these whole-body and system-specific changes play such a large role in a person’s age-related change in skill performance that one must possess a thorough knowledge of physical growth and aging in order to effectively study the course of motor development. As we will see later, movement is influenced by size, mass, and leverage. 99 Suggested Reading Lohman, T.G., Roche, A.F., & Martorell, R. (Eds.). (1988). Anthropometric standardization reference manual. Champaign, IL: Human Kinetics. Malina, R.M., Bouchard, C., & Bar-Or, O. (2004). Growth, maturation, and physical activity (2nd ed.). Champaign, IL: Human Kinetics. Nilsson, L. (1990). A child is born. New York: Delacorte Press. Ratey, J.J. (2001). A user’s guide to the brain. New York: Vintage Books. Spirduso, W.W., Francis, K.L., & MacRae, P.G. (2005). Physical dimensions of aging (2nd ed.). Champaign, IL: Human Kinetics. 100 Chapter 4 Physical Growth, Maturation, and Aging Changing Individual Constraints Across the Life Span 101 Chapter Objectives This chapter describes the course of body growth and aging over the life span, reviews the role of genes in the course of early physical growth and development, reviews the influence of extrinsic factors on growth and development and the increasing role of extrinsic factors as individuals proceed through the life span, identifies typical patterns of growth while recognizing individual differences in the timing of growth, and distinguishes between growth and maturation. 102 Motor Development in the Real World We Can Be Fooled By Size On my desk is a picture of my fifth-grade volleyball team. Of course, the tallest girls on the team are in the back row. They are also the two youngest players—by half a year! You likely have a similar story, perhaps about a team, classmates, or relatives. These stories serve as reminders that there is no single blueprint for the growth and maturation of all individuals. Thus, it is important for teachers, therapists, coaches, doctors, and nurses to understand what factors lead to variations in growth patterns and when those variations are normal or abnormal. Physical growth and aging are fascinating. Humans, as members of a single species, experience many common steps and processes in growth and aging. One example is the adolescent growth spurt. Genetic factors drive a very orderly and sequenced pattern of growth and aging, so in many respects we know what to expect. On the other hand, individuals each have unique potential and their own timing. When we observe a group of preadolescents of the same chronological age, we find a huge range of sizes. Growth and aging are also affected by a variety of extrinsic factors, such as nutrition and disease. Genetic and extrinsic factors combine to influence physical growth and aging. We can identify patterns and relationships in the growth and aging of humans (universality), but we are reminded over and over again of the individual differences (variability). It is important for us to know both the expected pattern and the range of variation. You might wonder why motor developmentalists have any interest at all in physical growth and aging. Recall our triangular model, which pictures the interaction of individual, environmental, and task constraints, and think back to one of the reasons we gave for the usefulness of this model to developmentalists. As individuals grow and age (in other words, as the individual constraints related to the body’s structure change), the interactions between the three types of constraints must change, giving rise to different movements. If our goal is to make the same movements possible over a long range of the life span, then we need to constantly change the environment or the task to accommodate the changing physical constraints. For example, if we want players of a variety of ages to be able to dunk a basketball, we have to adjust the task by changing the basket height as the height of the players or their jumping ability changes. We need to be constantly alert to changing the environment and task in order to help each individual achieve a desired movement. 103 Imagine for a moment that you are the youth sports coach of a sixth-grade basketball team. How much variation would you expect in the height and weight of your players? Would you assign each player a position (forward, center, or guard) based on size? As your players return for future seasons, would these assignments change? Why or why not? Understanding the patterns and variations of growth and aging is fundamental to helping individuals develop their motor skills. One goal of educators and health care providers is to make motor tasks developmentally appropriate—that is, achievable by those at any age and with any set of abilities or disabilities. This would be impossible without knowledge of physical growth and aging. Even for students who anticipate working with people past infancy, a good understanding of growth and aging begins with the study of prenatal growth and development. The talents and limitations that each individual brings to a task are often influenced by the course of prenatal growth and development. So, we begin a review of the growing and aging processes with prenatal development. This brief discussion highlights how sensitive individuals are to extrinsic influences, even in the relatively protective womb. 104 Prenatal Development The growth process begins the instant an ovum (egg) and spermatozoon fuse in fertilization. Carried out under the control of genes, early development is astonishingly precise. Genes, then, determine the normal aspects of development and inherited abnormal development. At the same time, the growing embryo (and, later, the fetus) is very sensitive to extrinsic factors, which include the environment in which the fetus is growing—the amniotic sac in the uterus—and the nutrients delivered to the fetus via the mother’s circulation and the placenta. Even in the womb, individual genetic factors and extrinsic factors interact in the fetus’ development. Some extrinsic factors, such as abnormal external pressure applied to the mother’s abdomen or the presence of certain viruses and drugs in the mother’s bloodstream, are detrimental to the fetus. Other factors, such as delivery of all the proper nutrients, enhance the fetus’ growth. Key Point Both genetic and extrinsic factors influence normal and abnormal embryonic and fetal growth. Prenatal growth is divided into two phases: embryonic growth, from conception to 8 weeks, and fetal growth, from 8 weeks to birth. Let’s consider the key features of each phase. Embryonic Development Development begins with the fusion of two sex cells: an ovum from the female and a spermatozoon from the male (see figure 4.1). The genes direct the continuous development of the embryo in a precise and predictable pattern. 105 Figure 4.1 As the embryo moves through the oviduct, its cells divide and multiply. By the time it implants on the lining of the uterus, it is several hundred cells in size. It is embedded in nutrient cells that nourish it. Implantation in the uterus is facilitated by protuberances of sugar molecules on the surface of the blastocyst. The number of cells increases, and the cells differentiate to form specific tissues and organs. This process occurs in a predictable time line, summarized in table 4.1. At 4 weeks, the limbs are roughly formed and the heartbeat begins. By approximately 8 weeks, the eyes, ears, nose, mouth, fingers, and toes are formed. By this time, the human form has taken shape. 106 Fetal Development The fetal stage, from 8 weeks to birth, is characterized by further growth and cell differentiation of the fetus, leading to functional capacity. This continued growth of the organs and tissues occurs in two ways: by hyperplasia and by hypertrophy. If you examine the landmarks of growth carefully, you will also see that growth tends to proceed in two directions. One direction is cephalocaudal, meaning that the head and facial structures grow fastest, followed by the upper body and then by the relatively slow-growing lower body. At the same time, growth is proximodistal in direction, meaning that the trunk tends to advance, then the nearest parts of the limbs, and finally the distal parts of the limbs (figure 4.2). Body weight increases and the body tissues grow steadily, with the rate of growth increasing at about 5 months and continuing at that rapid rate until birth. 107 Figure 4.2 A fetus at 3 months. Differentiation is the process wherein cells become specialized, forming specific tissues and organs. Hyperplasia is an increase in the absolute number of cells. Hypertrophy is an increase in the relative size of an individual cell. Cephalocaudal is the direction of growth beginning at the head and extending toward the lower body. Proximodistal is the direction of growth proceeding from the body toward the extremities. Although cells differentiate during growth to perform a specialized function, some cells have an amazing quality termed plasticity, which is the capability to take on a new function. If some of the cells in a system are injured, for example, the remaining cells might be stimulated to perform the role that the damaged cells would ordinarily carry out. The cells of the central nervous system have a high degree of plasticity, and their structure, chemistry, and function can be modified both prenatally and postnatally (Ratey, 2001). Plasticity is modifiability or malleability; in regard to growth, it is the ability of tissues to subsume functions otherwise carried out by other tissues. Fetal Nourishment Many characteristics of the fetal environment have the potential to affect growth, either 108 positively or negatively, and the nourishment system is the extrinsic factor that has the most influence on fetal development. The fetus is nourished by the diffusion of oxygen and nutrients between fetal blood and maternal blood in the placenta (figure 4.3). Carbon dioxide and excretory byproducts also are exchanged and carried away in the mother’s blood. 109 Figure 4.3 A diagram of the placenta showing the two circulations—that of mother and that of fetus—which come close enough to each other that substances diffuse from one to the other, though the bloodstreams never mingle. Reprinted by permission from Rhodes 1969. The growing fetus needs energy, nutrients, and oxygen. If these are in short supply, mother and fetus compete for limited resources, possibly compromising the needs of the fetus. Obviously, then, maternal health status plays a role in prenatal development. A woman who lives in better conditions (with an adequate, safe food supply and a protective, clean environment) and who receives early prenatal health care is more likely than a woman living in poorer conditions to meet the needs of the fetus. She is also more likely to be at lower risk for illnesses and infections that might compromise the health of the fetus and would result in low birth weight. Consequently, women at lower socioeconomic levels typically give birth to lighter infants than do women at higher socioeconomic levels. This is significant because low-birth-weight infants are at greater risk of disease, infection, and death in the weeks after birth than are normal-weight infants. Some of the differences in birth weight among ethnic groups can be attributed to parental height and are thus largely influenced by genetic factors (Troe et al., 2007). Further research is needed to distinguish the influences on birth weight that are primarily genetic from those that are environmental and thus might be modifiable to promote health in the early postnatal period. If you were a doctor, would you have to travel to a poor country with primitive living conditions to treat pregnant women with poor health status? Or would you likely see some poor women in your practice in an affluent country? What groups of pregnant women in an affluent country might be at risk of poor health? 110 Abnormal Prenatal Development Abnormal growth may arise from either genetic or extrinsic factors. Genetic abnormalities are inherited and may be immediately apparent or may remain undetected until well into postnatal growth. A host of extrinsic factors can also negatively affect the fetus. Examples include drugs and chemicals in the mother’s bloodstream, viruses in the mother’s bloodstream, and excessive pressure applied to the mother’s abdomen. Let’s consider some examples of congenital defects in more detail. Congenital defects are anomalies present at birth, regardless of whether their causes are genetic or extrinsic. Genetic Causes of Abnormal Prenatal Development An individual may inherit genetic abnormalities as dominant or recessive (including sex- linked) disorders. Dominant disorders result when one parent passes on a defective gene. Recessive disorders occur in children who inherit a defective gene from each parent. Genetic abnormalities can also result from a new mutation—that is, the alteration or deletion of a gene during formation of the egg or sperm cell. Researchers suspect irradiation and certain hazardous environmental chemicals of causing genetic mutations, and the potential for genetic damage to sex cells increases with advancing maternal age (Nyhan, 1990). Mutations can also occur spontaneously, without a known cause. An example of a familiar genetic abnormality is trisomy 21 or Down syndrome. When an egg or sperm cell divides, its 46 chromosomes divide in half. When the sperm cell with 23 chromosomes fertilizes an egg with 23 chromosomes, an embryo ends up with a complete set of 46. Sometimes an egg or sperm cell keeps both chromosome 21s, and every cell in the resulting embryo’s body will have an extra chromosome 21. A combination of birth defects can result, including mental retardation, distinctive facial features, visual and hearing impairments, and heart defects. New mutations and inherited disorders can both result in single or multiple malformations of an organ, limb, or body region; deformations of a body part; or disruptions in development resulting from the breakdown of normal tissue. They can affect one or more of the body systems. Many of these abnormalities are obvious at birth, but some do not appear until later. Genetic abnormalities vary considerably in appearance and severity. Extrinsic Causes of Abnormal Prenatal Development Our earlier discussion of fetal nourishment reveals how dependent the fetus is on the mother for the oxygen and nutrients it needs. Unfortunately, the fetal nourishment system 111 can also deliver harmful substances to the fetus, and various other factors can potentially affect the fetus’ physical environment and thereby its growth and development. Key Point Congenital disorders arising from extrinsic factors can affect the potential for postnatal growth and development. When medical professionals and parents are aware of negative influences, they can manage these influences to minimize the risk to the fetus. Teratogens In addition to oxygen and nutrients necessary for fetal life and growth, other substances— including viruses, drugs, and chemicals—can diffuse across the placenta and be harmful to the developing fetus. Sometimes even necessary vitamins, nutrients, and hormones can be harmful if their levels are too high or too low. In these cases substances can act as malformation-producing agents, or teratogens. The specific effect that a teratogen has on the fetus depends on when the fetus was exposed to the substance as well as the amount of the substance. A teratogen is any drug or chemical agent that causes abnormal development in a fetus upon exposure. There are critical periods of particular vulnerability to change in the growth and development of tissues and organs. Exposure to a teratogen during a critical period has a more significant effect than exposure at another time. For example, the rubella virus is harmful if the embryo is exposed to it during the first 4 weeks of pregnancy. The earlier the infection, the more serious the resulting abnormalities. Very early exposure can result in miscarriage. Some congenital defects result from the mere presence of a harmful substance in the maternal blood. Whether the fetus is exposed depends on the size of the substance. For example, small virus particles present in maternal blood can cross the placenta and harm the fetus. Drugs with molecular weights under 1,000 also cross the placenta easily, whereas those with molecular weights over 1,000 do not. Parents can maximize fetal growth and development by avoiding substances that might be teratogenic. Mothers can maintain a diet that supplies adequate but not excessive nutrients; otherwise, the fetus might develop a specific malformation or be generally retarded in growth and small for age at birth. It is important to recognize that these conditions, including low birth weight, can affect postnatal growth and development. For example, a 112 mother’s alcohol consumption during pregnancy can result in a condition called fetal alcohol syndrome, which involves a cluster of birth defects that often include mental retardation; heart defects; facial, joint, and limb deformities; slowed growth and small brain size; short attention span; and hyperactivity. Although it is not clear when a small amount of alcohol consumption becomes an amount that affects a fetus, this birth defect is completely avoidable when a mother abstains from using alcohol. Other Prenatal Extrinsic Factors Malformation, retarded growth, and life-threatening conditions can also result from external factors affecting the fetus’ environment. Examples include external or internal pressure on the infant, including pressure from another fetus in utero; extreme internal environmental temperature, as when the mother suffers from high fever or hypothermia; exposure to X rays or gamma rays; changes in atmospheric pressure, especially those leading to hypoxia (oxygen deficiency) in the fetus; and environmental pollutants. The precise effects of these factors also depend on the fetus’ stage of development. Like teratogens, external factors have the potential to affect present and future growth and development. Prenatal Development Summary Prenatal development is influenced by genetic and extrinsic factors. The genes direct an orderly and precise course of development, but extrinsic factors can influence the process either positively or negatively. Many of these extrinsic factors exert their influence through the fetal nourishment system. A fetus that receives appropriate levels of oxygen and nutrients has the best chance of reaching its full genetic potential, including its potential for skill performance. Prenatal abnormalities can arise from genetic or extrinsic influence. Some abnormal conditions are a product of both genetic inheritance and the environment; that is, a tendency for a disease might be inherited, and subsequently the disease will appear under certain environmental conditions (Timiras, 1972). We should view physical growth and development, then, as a continuous process that begins at conception. Individuals are, in part, products of the factors that affected their prenatal growth and development. Hence, the individual structural constraints that educators and therapists consider when planning 113 activities for individuals reflect the course of prenatal development. The process of postnatal development is the continuation of prenatal development. 114 Postnatal Development Is an 11-year-old capable of long-distance runs? How about a 60-year-old? We know that no single answer applies to all 11-year-olds or all 60-year-olds because we have acknowledged the coexistence of universality and specificity in development. Educators and therapists benefit from knowing the universal pattern of postnatal growth and physiological maturation as well as the typical pattern of aging in adults. Yet we work with individuals who have their own timing and potential for growth. Therefore, we must be able to evaluate an individual’s status and potential in order to help him or her set reasonable personal goals. We must be able to compare an individual with the average and adjust expectations for performance accordingly. Overall Growth Overall body growth after birth is a continuation of prenatal growth. The growth pattern is predictable and consistent but not linear, no matter which measure of overall growth we choose to study. For example, look at the growth curves for height (figure 4.4, a and b) and weight (figure 4.5, a and b). They are characterized by rapid growth after birth, followed by gradual but steady growth during childhood, then by rapid growth during early adolescence, and finally by a leveling off. Thus, the curves are roughly S-shaped. We call this pattern of overall body growth a sigmoid curve after the Greek letter for S. Key Point Postnatal growth proceeds in a precise and orderly pattern, but individual variability, especially in the timing of landmark events, is increasingly obvious as individuals move through infancy, childhood, preadolescence, and adolescence. 115 Figure 4.4 a Stature (standing height) by age percentiles for boys. Note the sigmoid, or S- shaped, form of the curves. Data from the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion 2000. 116 Figure 4.4 b Stature by age percentiles for girls. Data from the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion 2000. 117 Figure 4.5 a Weight by age percentiles for boys. Note that the curves are S-shaped, though flatter than the height curves. Data from the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion 2000. 118 Figure 4.5 b Weight by age percentiles for girls. Data from the National Center for Health Statistics in collaboration with the National Center for Chronic Disease Prevention and Health Promotion 2000. Although a normal growth curve is always sigmoid, the timing of a particular individual’s spurts and steady growth periods is likely to vary from the average. For example, one girl might begin her adolescent growth spurt at 8 years, whereas another might begin hers at 10. The slope of the curve can also vary from the average. One girl might grow more rapidly (i.e., have a steeper growth curve) than the other. Note that, on the growth charts, the range of variation—the gap between the 3rd and 97th percentiles—widens with age, especially for weight (Malina & Bouchard, 1991). This is another example of universality and specificity in development. The sigmoid pattern of a graph of overall growth is universal but the timing and steepness of segments of the curve are specific to the 119 individual, and with advancing age the influence of environmental factors increases the variation among individuals. Web Study Guide Learn to graph rate of change in height and weight in Lab Activity 4.1, Graphing a Velocity Curve, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Sex Sex is a major factor in the timing as well as the extent of growth. Sex differences are minimal in early childhood, with boys being very slightly taller and heavier. Throughout childhood, though, girls tend to mature faster than boys, so that at any given age girls as a group are biologically more mature than boys. Important sex differences in growth and development are especially pronounced at adolescence. Girls begin their adolescent growth spurt when they are about 9 years old (often termed the age at takeoff because the rate of growth begins to increase), whereas boys begin theirs at about age 11. Note that these ages are group averages. About two-thirds of all adolescents will initiate their growth spurt during the year before or the year after these averages, meaning that approximately one- third will initiate it even earlier or later. Age at takeoff is the age at which the rate of growth begins to increase. Height Height follows the sigmoid pattern of growth: a rapid increase in infancy, tapering off to steady growth in childhood, followed by another rapid increase during the adolescent growth spurt, and finally a tapering off until the end of the growth period. An individual’s height can be compared with group norms, and this comparison is often made using a family of height curves plotted against age. The individual curves represent various percentiles, usually the 3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 97th (as in figures 4.4, a and b). This approach allows us to approximate an individual’s percentile for height at a specific age or over time as well as whether he or she maintains position in the group or changes. For example, we might find one individual who remains in the 40th percentile for most of the growth period and another who begins the adolescent growth spurt early and goes from the 60th percentile at age 8 to the 90th percentile at age 10. Children tend to maintain their relative percentile positions in comparison with group norms after they are 2 or 3 years old; that is, a 3-year-old child in the 75th percentile for height is most likely to be around the 75th percentile throughout childhood. A large 120 fluctuation in relative position could indicate that some extrinsic factor is influencing growth (Martorell, Malina, Castillo, Mendoza, & Pawson, 1988) and that medical examination is warranted. In addition to the extent of growth, it is interesting to examine the rate, or velocity, of growth (i.e., when individuals are growing rapidly or slowly). By plotting the rate of growth, we can find the age at which one is growing the fastest (peak velocity) or the age at which one changes from slow growth to rapid growth (age at takeoff) or vice versa (see the “Assessing the Extent and Rate of Growth” sidebar). Assessing the Extent and Rate of Growth In chapter 1, we acknowledge that we often picture growth and development through graphs. This practice is common in describing physical growth. We often see measurements of height, weight, or length plotted against advancing age. These plots are called distance curves because they convey the extent of growth; figures 4.4 and 4.5 are examples. If we want to know the distance that growth has progressed at a certain age, we merely read the value opposite that age. For example, from a height curve we can determine how tall an individual was at any age (if using the plot of an individual person), or what the average height for a group was at any age (if using the plot of a group average). If the line plotted is going up with age, we know that growth is taking place, provided that the axes of the graph are arranged from low (at the origin) to high. We expect growth measurements to increase with age during the growth period. In adulthood, measurements might go up or down as extrinsic factors influence the measurement. Body weight is a good example. If the slope of a distance curve is gradual, the change is moderate for that age period. If the slope is steep, the change is rapid for that age period. Thus, the slope of a distance curve can indicate changes in the rate of growth. The rate of growth can be illustrated more dramatically on graphs of growth speed that are called velocity curves. These curves are plotted by first selecting small age spans, such as the time between the 8th and 9th birthdays, the 9th and 10th birthdays, and the 11th and 12th birthdays. Then, for each of these short spans, we find the change in growth as indicated by the distance curve. For example, we might check a distance curve for height and see that the increase in height between the 8th and 9th birthdays is 5 cm. We would then plot a point at 5 cm per year and at 8.5 years (representing the midpoint of the age span during which the growth was 5 cm). By doing this for a number of age spans and connecting our points with a smooth 121 curve, we produce a velocity curve. Velocity curves look very different from distance curves. They often have sections where the graphed line is decreasing, indicating that the rate of growth is slowing or decelerating. They also have peaks (i.e., points at which the rate of growth changes from faster to slower). Humans have a peak velocity in their overall growth measurements during early adolescence (termed peak height velocity, peak weight velocity, and so on). This is the age at which growth is the fastest for this portion of the life span. During the downslope of the peak, growth slows, although it may still be quite rapid. For example, a typical peak height velocity for girls is 8 cm per year (figure 4.6), and it typically occurs around 12 years of age. Immediately before this age, the velocity of growth in height increases from 5 to 6 to 7 to 8 cm per year. After this age, growth slows from 8 to 7 to 6 cm per year, and so on. Throughout the age span of 10.5 to 13 years, a fairly rapid increase in height (6–8 cm per year) occurs. In reading a velocity curve, we must keep in mind that we are reading a rate of growth for a short age span, not the extent of growth. We can say how tall an individual girl is from her distance curve for height but not from her velocity curve. On the other hand, her velocity curve enables us to easily determine the age at which she was growing the fastest. Readers who have studied calculus will recognize that a velocity curve is the first derivative of a distance curve. The second derivative would provide an acceleration curve, indicating the ages at which growth is accelerating or decelerating. On average, girls reach peak height velocity during the adolescent growth spurt at 11.5 to 12.0 years of age (figure 4.6). Their growth in height then tapers off at approximately age 14, and notable increases in height end around age 16. Boys reach their peak height velocity at 13.5 to 14.0 years. This velocity is somewhat faster than that of girls— approximately 9 cm per year for boys compared with 8 cm per year for girls (Beunen & Malina, 1988). Boys’ growth tapers off at 17 years, and notable increases end by age 18. Note that males have about 2 more years of growth than females, amounting to 10 to 13 cm of height. This longer growth period accounts for much of the difference in average absolute height between adult men and women. 122 Figure 4.6 Velocity curves plotted from figure 4.4a and b, ages 2 to 18 years. After age 2, the rate of growth slows until the adolescent growth spurt. Note the ages at peak height velocity for boys and girls. Weight Growth in weight also follows the sigmoid pattern: a rapid increase in infancy, a moderate increase in childhood, a spurt in early adolescence, and a steady increase that tapers off at the end of the growth period. Weight, however, is very susceptible to extrinsic factors and can reflect variations in the amount of muscle with exercise as well as variations in the amount of fat tissue with diet and exercise. Disease can also influence body weight. Peak weight velocity during the adolescent growth spurt follows peak height velocity in adolescents by 2.5 to 5.0 months in boys and 3.5 to 10.5 months in girls. The growth of various segment lengths and breadths can reach peak velocity before or after the individual reaches peak height velocity, but all reach their peak before or at peak weight velocity (Beunen, Malina, Renson, & Van Gerven, 1988). This is the factual basis for the commonly observed pattern of individuals first growing “up,” then filling “out.” Relative Growth Although the body as a whole consistently follows the sigmoid growth pattern, specific body parts, tissues, and organs have differential rates of growth. In other words, each part of the growing individual has its own precise and orderly growth rate. These differential growth rates can result in notable changes in the body’s appearance as a whole. Observe how the proportions illustrated in figure 4.7 change dramatically throughout life. Body proportions at birth reflect the cephalocaudal (head to toe) and proximodistal (near to far) directions of prenatal growth. Therefore, a newborn’s form is quite different from that of an adult. The head accounts for one-fourth of the total height at birth but only one-eighth 123 of adult height. The legs make up about three-eighths of height at birth but almost half of adult height. 124 Figure 4.7 Postnatal changes in body proportion shown by placing individuals throughout the growth period on the same scale. Adapted by permission from Timiras 1972. For a newborn to achieve adult proportions, some body parts must grow faster than others during postnatal growth. For example, the legs grow faster than the trunk and head in infancy and childhood, and they undergo a growth spurt early in adolescence. Growth in height results mostly from an increase in trunk length during late adolescence and early adulthood. Boys and girls have similar proportions in childhood, but by the time they are adults, relative growth of some body areas brings about noticeable differences between the sexes. In girls, shoulder and hip breadth increase at about the same rate, so their shoulder- to-hip ratio is fairly stable during growth. Boys undergo a substantial increase in shoulder breadth during their growth spurt, so their ratio changes as they move into adolescence and acquire the typical broad-shouldered shape of adult men. Key Point Late maturers have a longer growth period than early maturers and consequently tend to be taller. Teachers, personal trainers, physical therapists, doctors, researchers, and many other professionals take anthropometric measurements. These measurements must be taken with great precision if they are to be used for comparisons with norms or measurements taken at a later date. Body form might have implications for skill performance in early childhood. For example, even if 5-month-old infants were neurologically ready to coordinate and control the walking pattern, it is unlikely that they could balance their top-heavy bodies on such thin, short legs and small feet. Varying limb lengths and weights can affect balance, momentum, 125 and potential speed in various movements. Recalling Newell’s model, we realize that changing individual structural constraints related to body form and proportion could certainly interact with task and environment to produce different movements. Specific tissues and organs also grow differentially. Although their prenatal growth tends to follow the increase in body weight, the postnatal growth of some tissues and systems follows unique patterns. The brain, for example, achieves more than 80% of its adult weight by the time the individual reaches age 4. Because various tissues of the body grow differentially after birth, our knowledge of individual structural constraints is made more complete by study of the individual body systems. Growth, development, and aging of each of the relevant body systems are discussed in chapter 5. Measuring Height, Lengths, and Breadths Frequent measurement of children’s growth and comparisons of the values with averages at a given age can help detect abnormal growth. Medical or environmental factors influencing abnormal growth can then be identified and moderated or corrected. Of course, such growth measurements reflect the individual’s genetic potential for height and body build (which often correspond to the parents’ height and build) as well as the individual’s personal growth timing. Therefore, in screening growth measurements, children and teens who measure above the 90th or below the 10th percentile for their age—especially those whose parents are not exceptionally tall or short, respectively—should be referred to medical personnel for examination (Lowrey, 1986). For growth screenings to be meaningful, measurements must be done accurately using the same measurement techniques used to establish group norms or averages. Descriptions of standard techniques are widely available (Lohman, Roche, & Martorell, 1988, is one example of a standardization manual), and you should refer to these standards if you decide to conduct a screening program. A detailed description of standard techniques is beyond the scope of this text, but let’s briefly consider the various measurements used to assess physical growth and body size. The measurements used to assess growth and size are known as anthropometric measures, and they include height, weight, segment length, body breadth, and circumference. The measurements are sometimes used in ratios to illustrate a particular aspect of size. Standing height is the most common growth measure, but sitting height is an interesting measurement to observe during the growth period. Standing height minus sitting height yields a functional measure of leg length. Infants 126 have relatively long trunks and short legs, so the proportion of standing height to leg length changes over the growth period to reach the typical adult proportion. Various other segment lengths, such as those of the upper arm and the thigh, can also be measured, as can the breadth of the body at specified locations. Typically, length and breadth measurements are taken where skeletal landmarks can be located so that the measurement reflects skeletal structure and not soft tissues such as fat and muscle that can change with diet and exercise. The most common breadth measures are taken at the shoulders and hips, and shoulder width is often divided by hip width to form a ratio. This shoulder-to-hip ratio is also interesting to observe over the growth period because it undergoes dramatic change in boys during the adolescent growth spurt as they reach the typical male body build of broad shoulders and narrow hips. Circumference measurements can be taken at numerous locations and often represent soft tissue—fat and muscle—as well as bone structure. We expect circumference measurements to increase with growth in size, but a circumference measurement by itself cannot provide information about the amount of fat tissue versus lean body tissue. Pediatricians closely monitor head circumference in infants and toddlers. An abnormally large measurement is associated with hydrocephalus (excess cerebrospinal fluid that could cause brain damage). Body weight is another common measure of growth and body size. Growth in weight reflects the increase in lean body tissues, which is genetically driven, and the increase in adipose, or fat, tissues, which can be readily influenced by extrinsic factors such as exercise and nutrition. Body weight can be apportioned into lean body mass and fat tissue mass by one of several means described later in this text. The resulting measurement typically is expressed as “percent fat.” In another interesting ratio, body weight (in kilograms) is divided by the square of standing height (in meters). This ratio is called the body mass index (BMI), and it is useful for measuring obesity, especially in adults. The normal range for BMI is 18.5 to 24.9; obesity is defined as a BMI over 30.0. Many methods exist for measuring physical growth and body size, and each measurement yields a specific type of information. Particularly in combination, these measurements can provide a great deal of information about the course of growth in size. This information can be used by medical professionals to screen for and possibly correct troubling conditions, thereby enabling individuals to reach their growth potential or to maintain good health after the growth period. Such information can also be used to help children and youths understand the changes that their bodies undergo, especially during the adolescent growth spurt. 127 Physiological Maturation Tissues of the growing body can advance without necessarily increasing in size. The biochemical composition of cells, organs, and systems can advance qualitatively in what is termed physiological maturation. Chronological age, growth in body size, and physiological maturation are related to one another in that as children and youths get older they tend to grow in size and to mature. These dimensions can, however, proceed with their own timing. For example, two children of the same age can be dramatically different in maturation status, with one being an early maturer and the other being a late maturer. Or, two children of the same size can be different ages, and they could be at similar levels of maturation or very different levels of maturation. Thus, it is difficult to infer maturity from age alone, from size alone, or even from age and size considered together. An individual child can appear to be small and slight of build but may actually be relatively mature for his or her chronological age. Anthropometry is the science of the measurement of the human physical form. Physiological maturation is the developmental process leading to a state of full function. One indication of maturation status is the appearance of the secondary sex characteristics during the adolescent growth spurt. The secondary sex characteristics appear at a younger age in girls and boys who are early maturers and at an older age in those who are late maturers. As noted previously, girls as a group mature at a faster rate than boys; they enter their adolescent growth spurt sooner and their secondary sex characteristics appear sooner. Their breasts enlarge; pubic hair appears; and menarche, the first menstrual cycle, occurs. Regardless of the exact chronological age at which a girl begins her growth spurt, menarche typically follows the peak height velocity by 11 to 12 months (figure 4.8, a–c). The average age of menarche therefore is 12.5 to 13.0 years. In boys, the testes and scrotum grow in size and pubic hair appears. Boys have no landmark comparable with girls’ menarche for puberty; the production of viable sperm is a gradual process. Secondary sex characteristics are aspects of form or structure appropriate to males or females, often used to assess physiological maturity in adolescents. Imagine you are a physical education teacher at an elementary school. What would be wrong with expecting all of the tall children in your class to be the most skilled because you assume they are the most mature? Would you be correct in assuming that the most coordinated children in your fifth-grade class are likely to be those you will see playing on their high school varsity team in 6 years? 128 Figure 4.8 (a) Height attained (distance), (b) height velocity, and (c) height acceleration curves for a girl at adolescence. Note that menarche comes after peak height velocity. B2 marks the beginning of breast development and B5 marks adult form. P3 marks the intermediate stage of pubic hair development and P5 marks adult form. Reprinted by permission from Maclaren 1967. Maturation status is relevant as a structural constraint influencing movement. Individuals who are more mature are likely to be stronger and more coordinated than those who are less mature, even at the same chronological age. Parents, educators, and therapists must consider maturation status when designing activities and therapies for youths and when setting performance goals. It is tempting to infer movement performance potential from 129 size alone or age alone, but maturation status is a powerful predictor of performance potential. Key Point Postnatal growth patterns differ among body parts and body systems. Extrinsic Influences on Postnatal Growth As noted earlier, extrinsic factors can have a great influence on prenatal growth even in the relatively protective environment of the womb. It is no surprise, then, that extrinsic factors have an increased influence on growth and development after birth. Genetics control the timing and rate of an individual’s growth and maturation, but extrinsic factors—especially those influencing body metabolism—can have a great effect. During periods of rapid growth, such as just after birth and in early adolescence, growth is particularly sensitive to alteration by environmental factors. Early diet is a particularly important extrinsic influence. Duijts, Jaddoe, Hofman, and Moll (2010) observed that generation R infants who were breastfed exclusively until 6 months of age had a lower risk of infection than those infants who were exclusively breastfed until 4 months of age and only partially thereafter. Key Point Extrinsic factors play a larger role as individuals proceed through adulthood, leading to great variability among individuals in older adulthood. The phenomenon of catch-up growth illustrates the susceptibility of overall body growth to extrinsic influence. A child might experience catch-up growth after suffering a period of severe malnutrition or a bout with a severe disorder such as chronic renal failure (figure 4.9). During such a period, body growth is retarded. After the diet is improved or the child recovers from the disorder (i.e., after a positive environment for growth is restored), growth rate increases until the child approaches or catches up to what otherwise would have been the extent of growth during that period (Prader, Tanner, & von Harnack, 1963). Whether the child recovers some or all of the growth depends on the timing, duration, and severity of the negative environmental condition. One of the generation R studies (Ay et al., 2008) noted that children with slower prenatal growth in weight, especially during the third trimester, demonstrated catch-up growth in 130 the early months after birth. Children who experienced this period of catch-up growth tended to have a higher percentage of fat weight at 6 months of age than did children who did not experience a period of catch-up growth. The generation R project will continue to follow these children to determine whether this early period of catch-up growth might predispose individuals to higher proportions of fat weight later in life. Web Study Guide Collect anthropometric measures to assess children’s growth in Lab Activity 4.2, Taking Growth Measures, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Catch-up growth is relatively rapid physical growth of the body to recover some or all potential growth lost during a period of negative extrinsic influence. It occurs once the negative influence is removed. 131 Figure 4.9 In this illustration of hypothetical catch-up growth, the blue line represents the course of normal growth as it might have been. The purple line represents actual growth as influenced by a negative extrinsic factor. As the factor exerts its influence, growth slows; however, with removal of the negative factor, growth speeds up in order to catch up to the level that otherwise would have been reached. How close actual growth comes to catching the line representing normal growth depends on the time of occurrence and the length of time the factor exerts its influence, as well as its severity. Imagine you are a parent or, if you are in fact one, put on your “parenting cap.” Early maturers are likely to demonstrate better athletic performance than their late-maturing counterparts. If you overlook this and expect your son or daughter who is an early maturer to maintain his or her performance edge over others into adulthood, what could be the repercussions when the late maturers catch up? If you have a late-maturing child, what could be the repercussions of an early assumption that the child has no future as an athlete? Assessment of Physiological Maturation Maturation can be assessed directly or indirectly. A direct measurement would be ideal, but direct measures of maturation are not always easy to obtain or applicable to the entire growth period. For example, dental eruption (the appearance of new teeth) indicates maturation status but is restricted to two age spans: between approximately 6 months and 3 years, when the deciduous (baby) teeth first appear, and between approximately 6 and 13 years, when the permanent teeth appear. The appearances of 132 baby and permanent teeth follow a typical order. For early maturers, teeth appear at a younger age than for late maturers. The appearance of the secondary sex characteristics can also be used to assess maturation. Tanner (Marshall & Tanner, 1969, 1970; Tanner, 1975) devised a system of assessment that separately places boys and girls into one of five stages based on breast, pubic hair, and genitalia development. Stage 1 is the immature, prepubertal state and stage 5 is the fully sexually mature state. The average individual moves through these stages in approximately 4 years, so even with several years of variation in the timing of maturation, this assessment can be used for only about 6 years of the growth period. In addition, there are many individual exceptions to the course of progression. Ratings for axillary hair, voice change, and facial hair are not precise enough for assessing maturation, although these are obvious characteristics of ongoing maturation. A relatively precise assessment of maturation can be obtained from skeletal maturation. By comparing an X ray of skeletal maturation with a set of standards, developmentalists can assign individuals a skeletal age. In early maturers skeletal age is older than chronological age, and in late maturers skeletal age is younger than chronological age. This maturation assessment is described in more detail in chapter 5. Given the disadvantages of and difficulty obtaining direct assessments of maturation, many educators and therapists infer maturation status by comparing a set of growth measurements with group norms. That is, if a girl is in the 75th percentile for height, the 70th percentile for weight, the 80th percentile for shoulder breadth, and so on, we could infer that she is an early maturer. Of course, we could be fooled if her genetic potential is to be a large individual. She might be average in maturation status but larger than average for her age because she has inherited the genetic potential to be large. We must always keep in mind the limitations of inferring maturation from growth measurements, yet we still may find it useful to make an inference when considering the movement performance potential of an individual child or youth. Adulthood and Aging Growth ends for humans in the late teens or early 20s, but the status and size of the body attained during the growth years are not necessarily maintained in adulthood. Some measures of body size can change in adulthood. These changes reflect the aging of tissues and, probably to a greater extent, the influence of extrinsic factors. For example, a lack of weight-bearing exercise and calcium in the diet could contribute to osteoporosis and a resulting decrease in height. The range of extrinsic factors that might or might not 133 influence an individual, as well as the timing of their influence, are extremely variable over the life span. Naturally, then, we expect to see more and more individual variability in changes of body size as we move through the life span. Men and women grow slightly in height into their 20s. Trunk length may even increase very slightly into the mid-40s. Aside from these very small increases, height is stable through adulthood. It is common, however, for an individual’s stature to decrease slightly over the adult years (figure 4.10). Some of this decrease results from the compression and flattening of the body’s connective tissues, especially the cartilage pads between the vertebrae in the spinal column. The result is a compression of the spinal column and a decrease in trunk length. The bones also lose density as a result of progressive modifications in the protein matrix of the skeleton (Timiras, 1972). This breakdown is more severe in persons with osteoporosis and can result in the collapse of one or more vertebrae; if this occurs, the loss of stature is pronounced (figure 4.11). Heightened awareness in recent years of the devastating effects of osteoporosis on well-being has led to more interest in its prevention and treatment, which could at some point lead to less pronounced loss of height in older adulthood. 134 Figure 4.10 Body height in adulthood. Reprinted by permission from Spirduso 1995. 135 Figure 4.11 Loss of height in those with osteoporosis can be pronounced. Compression fractures of the vertebrae lead to kyphosis (“dowager’s hump”) and pressure on the viscera, causing abdominal distension. Imagine you are a doctor. As your patients enter their 50s, what might you do to decrease the likelihood that they will one day suffer a hip fracture due in part to bones that have lost density? Adults typically start gaining excess fat weight in their early 20s (figure 4.12). This increase is related to changes in lifestyle. Young adults who begin careers and families commonly take less time to exercise and prepare healthy meals. In contrast, adults who exercise regularly and eat wisely often maintain their weight or even gain muscle and lose fat. Older adults sometimes lose weight, probably as a result of inactivity and a consequent loss of muscle tissue. Loss of appetite accompanying lifestyle changes can also be a factor. Again, active older adults are not as likely to lose muscle weight. 136 Figure 4.12 Body weight in adulthood. Reprinted by permission from Spirduso 1995; adapted by permission from Frisancho 1990. 137 Summary and Synthesis Knowledge of the process of overall postnatal growth provides parents, educators, and therapists with the information they need to see how changing structural constraints affect the movement that arises from individual, task, and environmental constraints. Whole- body growth proceeds in a characteristic pattern known as the sigmoid pattern, but the rate of maturation varies between the sexes and among individuals. Knowing the normal process and the normal variability among individuals helps professionals detect abnormal or retarded growth in individuals. As individuals move through their growing years and then through their adult years, extrinsic factors contribute more and more to the variability we see among individuals. Because extrinsic factors can usually be manipulated (i.e., accentuated, minimized, or removed), everyone has an interest in knowing what factors have an effect and how they act on individuals. The individual structural constraints that affect movement often do so at a system level rather than at a whole-body level. For example, muscle growth influences available strength for executing skills. Our understanding of the role of changing structural constraints requires a look at the development of those body systems involved in movement over the life span. This is our task in the next chapter. Reinforcing What You Have Learned About Constraints Take a Second Look Think about that fifth-grade volleyball team mentioned at the beginning of the chapter. The two young but tall players might be early maturers. They might be the stars of the team at this age, but some of the smaller players might pass them in height in a few years or improve their skill level more. It is smart for teachers, coaches, and parents to patiently encourage all children to practice their skills and enjoy activities. It is very difficult to predict who might later pursue participation at an elite level or what activities individuals later find fun and rewarding, especially based on size or maturity at a young age. Test Your Knowledge 1. Discuss the differences between measurements of growth and direct measurements of maturation. What does each measure? What are some examples of each? 2. What is the difference between a distance curve and a velocity curve? What does each type of curve tell you about growth? Why are peaks in velocity curves of interest? 3. What areas of a fetus’ body advance first? In what directions does growth proceed? 138 4. Describe how teratogens reach a fetus. What are some factors that determine how a teratogen affects a fetus? 5. Describe sex differences in the course of overall growth from infancy to adulthood. Include the average ages for entering the adolescent growth spurt, peak height velocity, puberty, and the tapering off of growth in height. 6. What body measurements can change in older adulthood? How do they change? 7. What are some environmental factors that could affect middle-aged and older adults and reduce their health or wellness? How? Is it possible to alter these factors? Which ones? Learning Exercise 4.1 Secular Trends A secular trend is a change in a landmark of development over successive generations, usually due to the influence of extrinsic factors. Some researchers have hypothesized current secular trends toward earlier maturity, taller standing height, and greater body weight. Conduct an Internet search to locate information on these possible secular trends. Classify your information as (a) objective research conducted on a large number of participants, (b) anecdotal information about a single case, (c) an author’s hypothesis, or (d) opinion. Considering the information altogether, do you find credible evidence that a secular trend exists for any of these three landmarks of development? Support your conclusion with examples. 139 Chapter 5 Development and Aging of Body Systems The Systems as Individual Constraints 140 Chapter Objectives This chapter identifies developmental changes in the skeletal, muscular, adipose, endocrine, and nervous systems over the life span; notes the interaction of the systems during development and aging; discusses the periods when rapid change in the systems makes them particularly sensitive to external influences; and identifies a trend of increasing influence of external factors and decreasing influence of genetic factors as individuals proceed through the life span. 141 Motor Development in the Real World Early Attention to Development of the Body’s Systems Someone recently told me of a young woman who was 5 months pregnant but who had not yet seen a doctor. This young woman was from a middle-class household and could afford good medical care. Everyone who heard this account found it hard to believe. The emphasis we place today on good prenatal care reflects our knowledge of what is at stake—not just the overall body growth described in the previous chapter, but also the healthy growth and development of each of the body’s systems. We know that the body’s systems, just like the body as a whole, are not completely protected in the womb from extrinsic factors. One of the systems most vulnerable to extrinsic factors, such as a mother’s nutrition, is the nervous system, and because the body’s systems interact, any threat to such an important system has an effect on every system. Our discussion in chapter 4 of the physical growth, maturation, and aging of the body demonstrates that body size and maturity can be a structural constraint. Thinking about the model of constraints and height, for example, we can see that the movement of dunking a basketball is indeed the interaction of the task (dunking), the environment (the basket height as well as the size and weight of the ball), and the individual structural constraint of height. For individuals of differing heights, dunking success can vary. Yet, as we think about this task, it quickly becomes clear that we must think beyond the body as a whole. We must also consider some of the body’s specific systems. In considering how high one might jump, body height is a factor and a product of the body’s skeletal system. We should also consider the muscular system, because someone with stronger muscles likely can jump higher than an individual with weaker muscles. The amount of adipose tissue influences body weight and thus how easily one can jump up. Finally, of course, the nervous system must coordinate the muscles to produce the jumping movement. So, we often need to consider one or more body systems, or their interactions, as individual structural constraints to movement. To understand the role of body systems as structural constraints to movement, we must know how the body systems normally develop, what can influence this development and when, and what effect all of this has on movement. Our discussion of these systems’ effects on movement continues throughout this text as we consider how they act as constraints to various aspects of movement. 142 Development of the Skeletal System The skeletal system defines an individual’s structure. It is not, however, a hard and static structure; it is living tissue. It undergoes considerable change over the life span and reflects the influence of both genetic and external factors. Early Development of the Skeletal System Early in embryonic life, the skeletal system exists as a “cartilage model” of the bones. Sites gradually appear in the cartilage model where bone is deposited; these are called ossification centers. About 400 appear by birth and another 400 appear after birth. There are two types of ossification centers. Primary ossification centers appear in the midportions of the long bones, such as the humerus (upper arm) and femur (thigh), and begin to form bone cells starting at the fetal age of 2 months (figure 5.1). The bone shafts ossify outward in both directions from these primary centers until, by birth, the entire shafts are ossified. The primary ossification centers are areas in the midportion of the shafts of long bones where bone cells are formed so that the cartilage-model bones of the fetal skeleton begin ossifying, from the center outward, to form bone shafts. The secondary ossification centers, or epiphyseal plates, are the areas near the ends of long bones where new bone cells are formed and deposited so that the bones grow in length. Active secondary ossification centers are indicated on X rays by a line (an area not opaque) that is a layer of cartilage cells. Such an area is also called a pressure epiphysis, especially if it is at the end of a weight-bearing bone. 143 Figure 5.1 A human fetal skeleton at about 18 weeks. Dark areas indicate the ossified portions of the developing skeleton. Spaces between dark areas are occupied by cartilage models. Printed in U.S.A. © Carolina Biological Supply Company. Reproduction of all or any part of this material without written permission from the copyright holder is unlawful. Postnatal bone growth in length occurs at secondary ossification centers at the end of the bone shaft. A secondary center can also be called the epiphyseal plate, growth plate, or pressure epiphysis (figure 5.2). The epiphyseal plate has many cellular layers (figure 5.3) where cartilage cells form, grow, align, and finally erode to leave new bone in place. Bone is thus laid down at the epiphyseal plates to increase the length of the bone. The process of laying down new bone depends on an adequate blood supply. Any injury that disturbs this blood supply threatens the bone’s normal growth in length. In contrast to the long bones, small round bones such as those in the wrist and ankle simply ossify from the center outward. 144 Figure 5.2 Pressure epiphyses are located at the ends of long bones, such as the femur (thigh bone) pictured here. Epiphyses also occur at muscle tendon attachment sites, called traction epiphyses. 145 Figure 5.3 Development of a long bone in childhood. The epiphyseal growth plate, between the epiphysis and shaft, is enlarged on the right to show the zones in which new cells ossify. Printed in U.S.A. © Carolina Biological Supply Company. Reproduction of all or any part of this material without written permission from the copyright holder is unlawful. Growth at the ossification centers ceases at different times in different bones. At the epiphyseal plates, the cartilage zone eventually disappears and the shaft, or diaphysis, of the bone fuses with the epiphysis. Once the epiphyseal plates of a long bone fuse, the length of the bone is fixed. Almost all epiphyseal plates are closed by age 18 or 19. Recall that girls as a group mature faster than boys. It is no surprise, then, that the various ossification centers appear at younger chronological ages in girls than in boys. Likewise, the epiphyseal plates close at younger chronological ages in girls than in boys. For example, on average, the epiphysis at the head of the humerus closes in girls at 15.5 years but in boys at 18.1 years (Hansman, 1962). Individuals, of course, have their own unique timing, and a group of children at the same chronological age could easily vary in skeletal age by 3 years or more, demonstrating how variable maturity is in comparison with chronological age during the growth period. While the long bones are growing in length they also increase in girth, a process called appositional bone growth. Girth is increased by the addition of new tissue layers under the periosteum, a very thin outer covering of the bone, much like a tree adds to its girth under its bark. Appositional bone growth involves addition of new layers on previously formed layers so that a bone grows in girth. There are also epiphyses at the sites where the muscles’ tendons attach to bones. They are called traction epiphyses. You might have heard of a familiar condition that occurs during the growth period in some youths—Osgood-Schlatter disease. This is an irritation of the traction epiphysis where the patellar tendon attaches to the shin bone below the knee. Pediatricians usually have youths with this condition refrain for a time from vigorous (in particular, weight-bearing and jumping) activities to prevent further irritation of the site. 146 Overuse injuries to traction epiphyses during the growth period can threaten the pain-free movement at a joint in later life. For example, a traction epiphysis near the elbow can be injured by repeatedly and forcefully pronating the forearm, as in throwing. Ay et al. (2011) found that infants in the generation R study with low birth weight as well as those in the lowest weight groups at age 6 months tended to have a low bone mineral density measure at age 6 months. A positive relationship existed between postnatal growth in weight and bone mineral density as well as bone mineral content. Even infants showing catch-up growth in weight during the first 6 weeks after birth were less likely to have low bone mineral density. As the generation R study continues, it will be interesting to observe the relationship between early levels of bone mineral density and content and later levels so that we might learn whether adults are at risk of bone fractures based on their early growth patterns. Web Study Guide Identify differences in skeletal development in Lab Activity 5.1, Estimating Skeletal Age, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Key Point Because linear growth of the body is almost completely the result of skeletal growth, measures of height reflect the increase of bone length. Imagine you are the parent of a young boy who is the pitcher on his youth baseball team. He already practices once a week and plays a weekly game. The coach wants him to join a second baseball team so that he can get more pitching experience. Would you consent to this? Why or why not? The Skeletal System in Adulthood and Older Adulthood The skeletal structure itself changes little in young adulthood, but bone undergoes remodeling throughout the life span. Old bone is replaced by new bone. In youth, new bone is formed faster than older bone is resorbed, allowing for growth. In adulthood, though, bone formation begins to slow and eventually cannot keep pace with resorption. The result is a loss of bone tissue, starting as early as the mid-20s and averaging about 1% of bone mass per year (Smith, Sempos, & Purvis, 1981). 147 Bone composition also changes over the life span. Children have essentially equal amounts of inorganic and organic components in their bone tissue, but older adults have seven times more inorganic material, making the bone more brittle and subject to microfracture (Åstrand & Rodahl, 1986; Exton-Smith, 1985). Bone loss with aging occurs in both men and women and is related to changes in certain hormone levels, dietary deficiencies, and decreased exercise. In postmenopausal women, decreased levels of estrogen are implicated in more significant losses of bone mass because estrogen hormones stimulate osteoblastic (bone-forming) activity. Prolonged deficiency of calcium in the diet is another major factor in bone loss, as is shortage of vitamins and minerals. Cumming (1990) demonstrated the importance of dietary calcium by finding that women in early menopause who took a calcium supplement lost less than half the bone mass of women who took no supplement. Exercise probably has an effect on the maintenance of bone by increasing bone formation, whereas calcium and estrogen supplementation lower bone resorption (Franck, Beuker, & Gurk, 1991; Heaney, 1986). When a person engages in physical activity, the mechanical forces applied to the bones help maintain bone thickness and density. In fact, significant increases in bone mass are seen when older adults initiate exercise programs (Dalsky, 1989; Smith, 1982). Assessment of Skeletal Age The growth status of the bones can be used to assess maturation by comparing an individual’s state of development with those depicted in an atlas or standard—that is, a publication picturing skeletal development at many levels, each of which is assigned a skeletal age. The most common bones used for this purpose are the hand and wrist bones (figure 5.4). Thus, from an X ray of a person’s hand and wrist, we could determine a skeletal age by finding the picture in the atlas closest to the individual’s X ray. For example, a boy might have a skeletal age of 8.5 years because the extent of ossification in his hand and wrist X ray is most similar to that in the standard for 8.5 years. If his chronological age is under 8.5, we would know that he is an early maturer; if it is over 8.5, we would know that he is a late maturer. Skeletal age can easily be a year ahead of or behind chronological age, emphasizing how much variation is possible in maturation status even among those born on the same day. 148 Figure 5.4 Hand and wrist X rays are often used to assess skeletal age. The numerous hand and wrist bones provide multiple sites for comparison of a child’s X ray with the numerous standard X rays found in an assessment atlas. Here are two X rays from an atlas: (a) the standard for boys 48 months old and girls 37 months old, and (b) the standard for boys 156 months old and girls 128 months old. Note in the latter image how much more ossification (hardening) has occurred in the small wrist bones and the larger ossified area at the epiphyseal plates of the hand bones and the forearm bones. 149 Reprinted by permission from Pyle 1971. Many older adults suffer from a major bone mineral disorder, osteoporosis, which is characterized by a bone mineral density significantly below the average for young adults and, consequently, by a loss of bone strength. The bone becomes abnormally porous through the enlargement of the canals or the formation of spaces in the bone. This condition greatly increases the risk of fractures, especially at the hip, and adds to the difficulty of fracture repair (Timiras, 1972). Osteoporosis can also lead to microfractures of the vertebrae in the spine. Vertebrae eventually may even collapse, resulting in a dramatic change of skeletal structure (refer to figure 4.11) in which the rib cage collapses forward, with the lower edge resting on the pelvis, so that the posture becomes stooped and standing height is notably reduced. The incidence of osteoporosis is higher in older adult women than in men. It is likely that extrinsic factors, including hormone level, diet, and exercise, work in combination to influence the extent of bone loss and that we do not fully understand how these factors interact. However, it is clear that we can implement certain strategies to minimize the loss of bone tissue in adulthood. Women, for example, can maintain 150 adequate calcium intake during adulthood so that they enter menopause with the highest bone mineral density possible. Widespread attention to the factors that can be manipulated and to early detection and treatment of osteoporosis changes the outlook for many in regard to maintaining bone tissue over the life span. Web Study Guide Learn about different types of children’s skeletal injuries in Lab Activity 5.2, Exploring Skeletal Injuries in Youth Sports, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 151 Development of the Muscular System Whereas the skeletal system provides the body’s structure, the muscular system allows its movement. More than 200 muscles permit a vast number of movements and positions for the human body. Like the skeletal system, the muscular system changes over the life span under the influence of genetic and external factors. Early Development of the Muscular System Muscle fibers (cells) grow during prenatal life by hyperplasia (an increase in the number of muscle cells) and hypertrophy (an increase in muscle cell size). At birth, muscle mass accounts for 23% to 25% of body weight. Hyperplasia continues for a short time after birth, but thereafter muscle growth occurs predominantly by hypertrophy (Malina, Bouchard, & Bar-Or, 2004). The sigmoid pattern of growth in weight reflects the growth of muscle tissue. Muscle cells grow in both diameter and length. The amount of increase in muscle fiber diameter is related to the intensity of muscle activity during growth. Naturally, muscles must also increase in length as the skeleton grows, and this increase is accomplished through the addition of sarcomeres (the contractile units of muscle cells; figure 5.5) at the muscle–tendon junction as well as through the lengthening of the sarcomeres (Malina et al., 2004). 152 Figure 5.5 Muscle structure. The sarcomeres, or contractile units, compose muscle cells (myofibrils), which in turn make up a muscle fiber. Bundles of fibers compose the muscle. Sex differences in muscle mass are minimal during childhood; muscle mass constitutes a slightly greater proportion of body weight in boys. During and after adolescence, however, sex differences become marked. Muscle mass increases rapidly in boys up to about age 17 and ultimately accounts for 54% of men’s body weight. In sharp contrast, girls add muscle mass only until age 13, on average, and muscle mass makes up 45% of women’s body weight (Malina, 1978). The large sex differences in muscle mass involve upper body musculature more than leg musculature. For example, the rate of growth in arm musculature is nearly twice as high for males as for females, but the difference in calf muscle growth is relatively small. These sex differences in the addition of muscle mass are related to hormonal influences. Human muscle consists of two main types of fibers: slow-twitch (type I) fibers, which are suited to endurance activities, and fast-twitch (types IIa, IIx, and IIb) fibers, which are suited to intense short-duration activities (figure 5.6). At birth, approximately 15% of muscle fibers have yet to differentiate into slow- or fast-twitch fibers (Baldwin, 1984; Colling-Saltin, 1980) and 15% of type II fibers cannot be clearly categorized. These observations have led to speculation that an infant’s early activities might influence the ultimate proportion of the different types of fibers, but the issue remains unresolved. A twitch is a brief period of contraction of a muscle fiber (cell) followed by relaxation. Muscles can be classified as slow twitch or fast twitch. Slow-twitch muscles have a slower contraction–relaxation cycle and greater endurance than do fast-twitch muscles. 153 Figure 5.6 A cross section of muscle showing that fast-twitch (FT) and slow-twitch (ST) fibers are intermingled. During the first postnatal year the number of undifferentiated fibers decreases, and by 1 year of age the distribution of muscle fiber types is similar to that in adults (Malina et al., 2004). The exact proportions of fiber types for any given muscle vary among individuals (Simoneau & Bouchard, 1989). The heart is muscle tissue too. Like skeletal muscle, it grows by hyperplasia and hypertrophy. The right ventricle (lower chamber) is larger than the left ventricle at birth, but the left ventricle catches up after birth by growing more rapidly than the right, and the heart soon reaches adult proportions (figure 5.7). The heart generally follows the sigmoid pattern of whole-body growth, including a growth spurt in adolescence, such that the ratio of heart volume to body weight remains approximately the same throughout growth. One of the generation R studies (de Jonge et al., 2011) noted that left ventricular mass was already greater in overweight and obese children than in normal-weight children at 2 years. Future research will focus on whether this greater mass is related to a greater risk of cardiovascular disease in later life. 154 Figure 5.7 The human heart. The left ventricle is relatively smaller at birth and must catch up in growth in the early postnatal weeks. Early in the 20th century, some researchers thought that the large blood vessels around the heart developed more slowly than the heart itself, which implied that children who engaged in vigorous activity might be at risk. Later, it was shown that this myth had resulted from a misinterpretation of measurements taken in the late 1800s. In fact, blood vessel growth is proportional to the growth of the heart (Karpovich, 1937). The Muscular System in Adulthood and Older Adulthood Body composition begins changing in young adulthood. The proportion of lean body weight decreases, most often as a result of fat weight increasing. The change in muscle mass during adulthood is small. Only 10% of skeletal muscle mass is lost on average between the mid-20s and age 50. Changes in diet and physical activity level are probably responsible for this shift in body composition; poor diet promotes increased fat weight, and lack of physical activity leads to decreased muscle weight. After age 50, individuals begin to lose muscle mass at a greater rate. The extent of loss varies greatly. Individuals who maintain a good diet and participate in resistance exercise lose far less muscle than others do, but on average an additional 30% of muscle mass is lost 155 by age 80. By very old age, sedentary individuals with poor nutrition can lose as much as 50% of the muscle mass they possessed in young adulthood. Both the number and the diameter (size) of muscle fibers appear to decrease (Green, 1986; Lexell, Henriksson-Larsen, Wimblad, & Sjostrom, 1983). The loss in number of fibers is small before the 50s—only about 5% of the adult number (Arabadjis, Heffner, & Pendergast, 1990)—but more rapid thereafter, amounting to approximately 35% (Lexell, Taylor, & Sjostrom, 1988). Fibers do not seem to decrease in size until the 70s (McComas, 1996). Debate continues about whether the loss in muscle mass involves all three types of muscle fibers or whether type II fibers undergo a greater loss than type I fibers do (Green, 1986; Lexell, 1995). In relation to cardiac muscle, in old age, the heart’s ability to adapt to an increased workload declines. This might relate in part to degeneration of the heart muscle, a decrease in elasticity, and changes in the fibers of the heart valves (Klausner & Schwartz, 1985; Shephard, 1981). The major blood vessels also lose elasticity (Fleg, 1986). Most changes in the heart muscles of individuals, however, are related to changes in lifestyle and resulting pathology rather than aging of the cardiac muscle fibers. Bone and muscle mass are related to one another throughout the life span. Use of the muscles probably stimulates the bones to respond with increased bone formation, but many other factors certainly contribute to this relationship. Decline in bone and muscle mass is a constraint in the movement of older adults, and any loss of muscle strength accompanying decreases in muscle mass with aging can lead to a decrease in physical activities that are important to cardiovascular health or that the individual finds enjoyable. Such a loss also places an older individual at risk of falls, which increases the risk of bone fracture. We discuss muscle strength more extensively in chapter 16. If you were a personal trainer, would the sex of your clients be the only consideration in their potential to maintain or increase muscle mass? Would psychological, social, or cultural factors influence your approach to training them? Key Point It has been difficult for researchers to distinguish the changes that are an inevitable result of age from those that reflect the average older adult’s lack of fitness or poor diet. 156 Development of the Adipose System A common misconception about adipose (fat) tissue is that its presence in any amount is undesirable. In reality, adipose tissue plays a vital role in energy storage, insulation, and protection. Early Development of the Adipose System The amount of adipose tissue increases in early life. It first appears in the fetus at 3.5 months and increases rapidly during the last 2 prenatal months. Despite this late prenatal increase, adipose tissue accounts for only 0.5 kg (1.1 lb) of body weight at birth. A rapid increase of fat occurs in the first 6 months after birth, and the highest peak weight velocity occurs in the first month. Greater-than-average peak weight velocities were associated with increased risk of overweight and obesity at age 4 years in the generation R study group (Mook-Kanamori et al., 2011). After this rapid increase in the first 6 months, fat mass increases gradually until age 8 in both boys and girls. Girls tend to have slightly more fat mass than boys at age 2 (Ay et al., 2008). In the generation R study group, individual children tended to maintain their relative position in the group for amount of subcutaneous fat mass, especially in the trunk, throughout the first 2 years of life. In boys, adipose tissue continues to increase gradually throughout adolescence, but girls experience a more dramatic increase. As a result, adult women have more fat weight than adult men, with averages of 14 kg (30.9 lb) and 10 kg (22 lb), respectively. Fat weight during growth increases by both hyperplasia and hypertrophy, but cell size does not increase significantly until puberty. Individual fatness varies widely during infancy and early childhood. A fat baby will not necessarily become a fat child. After 7 to 8 years of age, though, it is more likely that individuals maintain their relative fatness. An overweight 8-year-old has a high risk of becoming an overweight adult. The distribution of fat in the body changes during growth. During childhood, internal fat (fat around the viscera) increases faster than subcutaneous fat (fat under the skin), which actually decreases until age 6 or 7. Boys and girls then show an increase in subcutaneous fat until they are 12 or 13. This increase in subcutaneous fat continues in girls, but boys typically lose subcutaneous fat in midadolescence. Adolescent boys also tend to add more subcutaneous fat to their trunks than to their limbs, whereas girls have increased subcutaneous fat at both sites. Note that in figure 5.8 skinfold measurements for boys’ extremities (top blue line) actually decrease, except during the growth spurt. Trunk skinfolds tend to hold steady but also increase during the growth spurt. Girls’ skinfold measurements (purple lines) increase steadily for both trunk and limbs, especially after age 157 7. Girls usually add more subcutaneous fat to their legs than to their arms. 158 Figure 5.8 Changes in fat distribution during growth are illustrated by plotting the sum of five trunk skinfold measurements and five extremity skinfold measurements. Note the increase in both types of measurement in girls, contrasting with a decrease in extremity subcutaneous fat in boys during adolescence. Reprinted by permission from Malina and Bouchard 1988. What are the life span implications of individuals adding too much fat during the periods of increase in fat tissue? There is much that we don’t know about adipose tissue development and obesity. Researchers are currently examining many topics, including maternal weight gain during pregnancy, early infant feeding, and genetic factors. Of particular interest are the two periods when the number of adipose cells increases—during the first 6 postnatal months and around puberty. Increases in cell number are significant because adipose cells persist once they are formed, even with malnutrition; that is, the cells may be empty of fat, but they still exist. Therefore, these two periods may be critical in the control of obesity. Adipose Tissue in Adulthood and Older Adulthood Both sexes tend to gain fat weight during the adult years, reflecting changes in nutrition and activity level. The average American woman gains 11.8 kg (26 lb) and the average American man gains 8.2 kg (18 lb) between 20 and 50 years of age (Hellmich, 1999). Total body weight begins to decline after age 50, but this reflects loss of bone and muscle, as body fat continues to increase. Body fat redistributes with aging. Subcutaneous fat on the limbs tends to decrease, whereas internal fat in the abdomen tends to increase (World Health Organization, 1998; World Health Organization Expert Subcommittee, 1998). This pattern is significant because abdominal obesity has been associated with a higher risk of cardiovascular disease. 159 It is difficult to identify typical patterns of adipose tissue gain or loss in older adults. Because obese individuals have a higher mortality rate, either lighter individuals survive to be included in studies of older adults, whereas obese individuals do not, or thinner adults are more eager to participate in research studies. It appears that an increase in fat weight with aging is not inevitable. For example, such gains are not found in lumberjacks in Norway who have a very active lifestyle as a result of their profession, persons from undernourished parts of the world, and master athletes (Shephard, 1978b; Skrobak- Kaczynski & Andersen, 1975). Typically, though, most older adults add some fat weight as they age, and active older adults add less than their sedentary peers. Overweight and obesity can be a constraint to movement at any age. Movement must be more effortful with greater weight, joint movement can be restricted, and social pressure related to body image and self-esteem can discourage participation in physical activities. 160 Development of the Endocrine System The cells of a living being must be precisely regulated for their content and temperature. The control systems regulating the cells of the body are the nervous system and the endocrine system, and it is not surprising that they play a major role in growth and maturation. The endocrine system exerts its control over specific cellular functions through chemical substances called hormones. Those secreted by the hypothalamus in the brain regulate the pituitary gland, which, in turn, regulates the adrenal gland, the thyroid gland, and the release of sex hormones. Hormones are chemical substances secreted into body fluids by a gland. These substances have specific effects on the activities of target cells, tissues, or organs. Early Development of the Endocrine System The endocrine system’s regulation of growth is a complex and delicate interaction of hormones, genes, nutrients, and environmental factors. In fact, hormone levels must be delicately balanced. Either an excess or a deficiency of hormones may disturb the normal process of growth and development. A detailed discussion of how the hormones influence growth and maturation is beyond the scope of this text, so we will just mention three types of hormones here. All three promote growth in the same way: They stimulate protein anabolism (constructive metabolism), resulting in the retention of substances needed to build tissues. There are specific times in the growth period when one of these hormones may play a critical role in growth. Growth Hormone Growth hormone (GH) influences growth during childhood and adolescence by stimulating protein anabolism so that new tissue can be built. Under the control of the central nervous system, GH is secreted by the anterior pituitary gland (figure 5.9). The body needs this hormone for normal growth after birth. A deficiency or absence of GH results in growth abnormalities and in some cases the cessation of linear growth. 161 Figure 5.9 Location of the various endocrine glands. Thyroid Hormones The thyroid hormones are secreted by the thyroid gland, located in the anterior neck region. Two types of thyroid hormones influence whole-body growth after birth, and a third plays a role in skeletal growth. The pituitary gland secretes a thyroid-stimulating hormone that regulates the thyroid hormones secreted by the thyroid gland. Thyroid-stimulating hormone excretion is, in turn, increased by a releasing factor found in the brain’s hypothalamus. Thus, two systems act in concert: a pituitary–thyroid system and a nervous system–thyroid system. This is one example of how the nervous system and endocrine system work together. Gonadal Hormones The gonadal hormones affect growth and sexual maturation, particularly during adolescence, by stimulating development of the secondary sex characteristics and the sex organs. The androgens, specifically testosterone from the testes and androgens from the cortex of the adrenal glands, hasten fusion of the epiphyseal growth plates in the bones. Thus, these hormones promote skeletal maturation (fusion) at the expense of linear growth; this explains why early maturers tend to be shorter in stature than later maturers. Androgens also play a role in the adolescent growth spurt of muscle mass by increasing nitrogen retention and protein synthesis. This spurt is more pronounced in young men than in young women because men secrete both testosterone and adrenal androgens 162 whereas women produce only the adrenal androgens. In women, the ovaries and the adrenal cortex secrete estrogens. Increased estrogen secretion during adolescence, as with androgens, speeds epiphyseal closure, but estrogen also promotes fat accumulation, primarily in the breasts and hips. Men and women both have estrogen and testosterone but in very different proportions. Teenage boys sometimes take steroid supplements in order to add muscle mass and look older. Increased secretion of androgen (a steroid) at puberty, though, hastens fusion at the epiphyseal plates. What unintended effect might these supplements have on growing boys? Insulin The hormones we have discussed up to this point all play a major and direct role in growth and development. Another familiar hormone, insulin, has an indirect role in growth. Produced in the pancreas, insulin is vital to carbohydrate metabolism, stimulating the transportation of glucose and amino acids through membranes. Its presence also is necessary for the full functioning of GH. A deficiency of insulin can decrease protein synthesis, which is detrimental at any time in life but especially during growth. The Endocrine System in Adulthood and Older Adulthood Earlier, we acknowledged that the nervous system and the endocrine system work in concert to regulate cellular functions and organ systems. It is no surprise, then, that the continued functioning of these integrating systems is important to good health throughout the life span. In fact, one group of theories about the cause of aging, called gradual imbalance theories, suggests that over time the nervous system, endocrine system, and immune system gradually fail to function (Spirduso, Francis, & MacRae, 2005). This gradual failure might occur at different rates in the three systems, leading to imbalances between them. Imbalances and reduced effectiveness within systems leave older individuals at increased risk of disease. Thyroid function is an example; it tends to decline with aging, and thyroid disorders are more prevalent among older adults. A long-term increase in thyroid hormone levels can be related to congestive heart failure. It is therefore important for older adults to be screened for hyperthyroidism. On the other hand, insufficiency of thyroid hormone, or hypothyroidism, is associated with acceleration of aging systems. Gonadal hormone levels also decrease with age. Hormone replacement therapy may counteract many of the effects of aging. For example, prescribing androgen supplements 163 has been successful in countering muscle wasting and osteoporosis. We need more information about the side effects of hormone replacement therapies, as evidenced by the eventual finding that women receiving hormone replacement therapy at menopause were unfortunately at increased risk of some types of cancers. Older adults maintain secretion levels of insulin comparable with those of younger adults, but the incidence of type 2 diabetes (non-insulin-dependent diabetes mellitus, which is caused by insulin deficiency) increases markedly with age. It is possible that older adults do not utilize insulin as effectively as younger adults to promote glycogen storage, thus retarding the mobilization of fuel for exercise. All of these examples show that gradual declines or imbalances in the nervous, endocrine, and immune systems can lead to increased risk of disabilities or diseases that in turn can both threaten good health and serve as constraints to physical activity. 164 Development of the Nervous System No single system is as much the essence of an individual as the nervous system. We need only observe an individual with severe brain injury to know this. The nervous system controls movement and speech. It is the site of thinking, analysis, and memory, and its development is important to social, cognitive, and motor development. Early Development of the Nervous System Much of neurological development occurs very early in the life span. The course of neurological development is a prime example of the interplay of genetic and extrinsic factors. Genes direct the development of the nervous system’s structures and its main circuits. The trillions of finer connections between nervous system cells, however, are fine- tuned by extrinsic factors. Let’s consider neurological development in more detail, including the role of extrinsic factors. Prenatal Growth of the Nervous System In general, the formation of immature neurons, their differentiation into a general type, and their migration to a final position in the nervous system occur prenatally. Neurons proliferate in the early prenatal period at an astonishing rate of 250,000 per minute. As many as 200 billion are formed. During the third and fourth prenatal months, almost all the neurons that the individual human brain will ever have are formed, although it seems that genes direct an overproduction of cells so that the system can later be pruned (Ratey, 2001). Neurons contain a cell body, which carries out functions to keep the cell alive; up to 100,000 dendrites, which receive impulses from other neurons; and an axon, which transmits impulses to other neurons, glands, organs, or muscles (figure 5.10). 165 Figure 5.10 The structure of a nerve cell, or neuron. Note that there can be multiple dendrites to carry nerve impulses to the cell body, but only one axon emerges to carry an impulse to other neurons, glands, or muscles. The axon can, however, branch extensively. Neurons are the cells of the nervous system that receive and transmit information. The new neurons also travel to a final destination during the prenatal period. Some form the brain stem, which controls heartbeat and breathing; some the cerebellum, which controls posture; and some the cerebral cortex, where perception and thought take place. Generally, neurons are in their final location by the sixth prenatal month. Neurons specialize. For example, visual neurons are specialized as a function of both their genes and the location to which they migrate, in this case a part of the brain where visual information arrives. The migration process is vital to normal brain development (Ratey, 2001). Once the neurons are in place, they grow an axon along a chemical trail to a general destination in order to connect to other neurons, forming the brain’s circuitry of about 100 trillion connections, or synapses. Because of the overproduction of neurons, axons compete for the chemical trails. Some axons and their neurons die off. The neurons fire electrical impulses that strengthen some of the connections between neurons. The firing is somewhat random prenatally but more organized as the fetus, and later the infant, receive input from the environment (Ratey, 2001). Thus, a natural pruning occurs of both neurons (reducing the number to approximately 100 billion at birth) and their branches and connections. 166 Weak or incorrect connections are sacrificed to make the neural network more efficient. A synapse is a connection between two neurons; it is made by the release of chemicals called neurotransmitters from an axon. These neurotransmitters cross a small gap between neurons, then permeate the cell wall at the dendrite, or cell body, of a receiving neuron to trigger an electrical impulse. Assume you have just found out you will be a new mother. What substances would you avoid consuming until your baby is born? Why? The migration of neurons and the branching of their processes are susceptible to effects of environmental factors delivered via the fetal nourishment system described in chapter 4. Growing evidence shows that some disorders, such as epilepsy, autism, and dyslexia, are caused at least in part by faulty migration of neurons (Ratey, 2001). Nicotine that is introduced to a fetus by a mother’s smoking might affect migration, branching, and pruning of the neurons, and we know that children of mothers who smoked while pregnant are at increased risk of mental retardation. Alcohol introduced by a mother’s alcohol consumption can cause improper neuron migration, and babies with fetal alcohol syndrome are known to later exhibit lower IQs and more prevalent reading and mathematics disabilities than other children. There are many examples of fetal exposure to illicit drugs and toxins and of malnutrition influencing development of the nervous system. It is clear that the nervous system is one of the systems most susceptible to teratogenic exposure during the prenatal period. Postnatal Growth of the Nervous System At birth the brain is about 25% of its adult weight. Brain growth increases rapidly after birth and reaches 80% of adult weight by age 4. Then it enters a period of steady growth through adolescence. The rapid early growth reflects an increase in the size of the neurons, further branching to form synapses, and an increase in glia and myelin. The first postnatal year is one of prolific synaptic formation, and each neuron can establish 1,000 to 100,000 connections. Glia are the cells of the nervous system that support and nourish the neurons. Myelin is an insulating sheath around the axons. The cerebral cortex is the wrinkled surface of the brain containing millions of neurons and regulating many human functions and behaviors. Key Point The brain demonstrates enormous plasticity during all phases of the life span, and its 100 trillion connections change constantly. 167 This rapid growth of the early postnatal period continues to make neurological development very susceptible to extrinsic factors. Poor nutrition, for example, could stunt the growth of the brain, a deficit from which the individual might never recover. Injury to the left side of the cerebral cortex (figure 5.11) early in life leads to deficits in language ability (Witelson, 1987). Increasing evidence also shows that an infant’s early life experiences influence development of the nervous system. Greenough and colleagues (Comery, Shah, & Greenough, 1995; Comery, Stamoudis, Irwin, & Greenough, 1996; Greenough et al., 1993; Wallace, Kilman, Withers, & Greenough, 1992) found that rats raised with much stimulation grew significantly more synapses than those raised without stimuli. The same pattern holds for humans. Learning is one of the most significant extrinsic factors influencing postnatal development of the nervous system. We now know that the brain restructures itself with learning. Magnetic resonance imaging documents that areas of the brain corresponding to frequently used body parts expand as the synaptic connections in those areas expand. From the early weeks of life and continuing over the life span, neural connections and pathways that are stimulated are strengthened whereas those not used are weakened. 168 Figure 5.11 The human brain. The cerebral cortex consists of the frontal, parietal, temporal, and occipital lobes. The Genome Project has determined that 30,000 to 50,000 of the 100,000 genes in the human genome are designated for the brain. Even that number probably does not fully control our 100 trillion synapses; there is room for variation resulting from experience, and most of our traits reflect the interaction of genes and our environment. Brain Structures The spinal cord and lower brain structures are more advanced at birth than are the higher brain structures. Lower brain centers involved in vital tasks, such as respiration and food intake, are relatively mature. Lower brain centers also mediate many reflexes and reactions. These automatic movement responses dominate the fetus’ and newborn’s movements, so it makes sense that lower brain centers are relatively more advanced than higher brain centers at this time. For many years, researchers have interpreted the onset of goal-directed movements in infants as evidence that higher brain centers are maturing. The cortex is involved in purposeful, goal-directed movement. The first clear evidence of successful intentional movement (reaching) occurs at 4 to 5 postnatal months (Bushnell, 1982; McDonnell, 1979). Hence, early researchers assumed that this behavior signaled the first functioning of the cortex at about 4 months of age, even though the cerebral hemispheres are formed at birth. More recently, researchers have used positron emission tomography (PET) scans to study the infant brain. The scans show little activity in the frontal cortex at 5 days of age, increased activity at 11 weeks, and adult levels at 7 to 8 months (Chugani & Phelps, 1986). Clearly, then, the process whereby the frontal cortex becomes functional is gradual. In fact, specialization of areas of the cortex continues well into adulthood. 169 The development of myelin in the nervous system contributes to speedy conduction of nerve impulses. Myelin cells, comprising mostly fat, wrap themselves around the outgoing neuron cell process, or axon (refer to figure 5.10). Myelinated axons can fire nerve impulses at higher frequencies for longer periods than those not myelinated (Kuffler, Nicholls, & Martin, 1984). Axons that are as yet unmyelinated in the newborn are probably functional, but myelination improves the speed and frequency of firing. The function of the nervous system in movements requiring or benefiting from speedy conduction of nerve impulses, such as a series of rapid movements or postural responses, might be related to the myelination process during development. The importance of myelin is evident in multiple sclerosis, a disease that strikes young adults and breaks down the myelin sheath, resulting in tremor, loss of coordination, and possibly paralysis. Myelination is the process whereby the axons of the neural cells are insulated when insulating myelin sheaths formed by Schwann (glial) cells wrap themselves around the axon. Nerve tracts are major neurological pathways. There are two major motor tracts: the extrapyramidal and the pyramidal. The spinal cord is relatively small and short at birth. A cross-sectional view of the spinal cord (figure 5.12) shows a central horn-shaped area of gray matter and a surrounding area of white matter. The central area contains tightly packed neuron cell bodies. Note the roots that lie just outside the cord; they contain the axons of the cord’s neurons and, in the case of the sensory roots, nerve cell bodies. Fibers from the dorsal and ventral roots merge to form the peripheral (spinal) nerves outside the cord. A marked increase in the myelination of these peripheral nerves occurs 2 to 3 weeks after birth, and this process continues through the second or third year of life. 170 Figure 5.12 A cross section of the spinal cord. Two major motor nerve pathways, or nerve tracts, carry impulses from the brain down the spinal cord to various parts of the body. One pathway, the extrapyramidal tract, is probably involved in delivering the commands for both the random and the postural movements made by the infant in the first days after birth. The other, the pyramidal tract, myelinates after birth; it is functioning by 4 to 5 months and controls the muscles for finger movements. The myelination pattern that the spinal cord and nerve pathways undergo might have implications for motor development. Myelination proceeds in two directions in the cord: first in the cervical portion, followed by the progressively lower portions, and then in the motor (ventral) horns, followed by the sensory (dorsal) horns. The direction of myelination tends to be away from the brain in the motor tracts. In contrast, the direction of myelination is toward the brain in sensory tracts, occurring first in the tactile and olfactory pathways, then in the visual pathways, and finally in the auditory pathways. Sensory pathways mature slightly faster than motor pathways, except in the motor roots and cerebral hemispheres. Higher-level functioning might be possible only when the neurons involved in a behavior are myelinated, allowing faster and more frequent conduction of nerve signals. The Nervous System in Adulthood and Older Adulthood The traditional view of the nervous system has been that after adolescence the only changes in the system would be losses, including a loss of neurons, thinning of the dendritic branches, a decline in the number of synapses, changes in the neurotransmitters, and reductions of myelin. This traditional view was based largely on observing behavior, especially slowing motor responses to stimuli as aging proceeds. Now, however, with the use of new imaging techniques that allow researchers to better see what is actually happening to brain tissue, we know that there are significant exceptions to the trend of 171 losses. Our new picture is one of enormous plasticity. Neurogenesis has been observed in some areas of the brain (Verret, Trouche, Zerwas, & Rampon, 2007), and the 100 trillion connections among the neurons are constantly changing over the life span. Neurogenesis is the division and propagation of neurons. The repercussions of age-related losses in the nervous system are widespread. Slowing of responses can affect movements in recreational activities as well as activities of daily living. Response slowing also affects the performance of cognitive tasks. Several theories have been advanced to explain how physiological changes result in slowing of responses. One of these theories is the neural network model. In this model, the nervous system is seen as a neural network of links and nodes. To respond to a stimulus, a signal begins at the input end of the individual’s nervous system and travels through the network to the output end. With aging, links in the network are thought to break at random so that the neural signal must detour, increasing the time before the response is made. With advancing age, more links break and the processing time for a signal gets longer and longer (Cerella, 1990). Clearly, the loss of neurons, dendrites, and synapses, as well as the decline in neurotransmitters, are physiological changes that would result in broken links in the neural network. Key Point Regular vigorous exercise can play a key role in minimizing the loss of neurons and synapses with aging. As in young people, extrinsic factors play a role in nervous system changes, and one of the most important factors is exercise. The very same exercise that benefits the cardiovascular system has positive effects on the nervous system, including reduced risk of stroke, increased branching of dendrites, and maintenance of the neurons’ metabolism. Regular vigorous exercise maintains the level of blood flow to the brain, lessens the loss of dendrites, stimulates neurogenesis, and promotes new synaptic connections. These effects can result in improved cognitive function in older adulthood (Weuve et al., 2004). 172 Summary and Synthesis A major theme in this discussion of body systems is that systems do not develop and age independently of one another; rather, they develop in concert, with one system often stimulating change in another. For example, growth of the long bones may stimulate the muscles to grow in length. In addition, the neurological system directs secretion of the pituitary hormones, which have their own effects (decreasing estrogen levels, for instance, affect bone strength in older women). Thus, a system can have its own timing or pattern of development but also interact with other systems in the bigger picture of the individual’s development. Even when we want to focus on the development or aging of one body system, we should do so in the context of the other systems changing as well. In addition, there are periods when change in a system is more rapid. These are often referred to sensitive periods because we expect extrinsic factors to be more influential in times of greater change than in times of gradual change. Of course, the extrinsic influence could be positive (facilitating growth or slowing aging) or negative (slowing growth or accelerating aging). Last, extrinsic factors can influence development at any point in the life span but have more influence in development as individuals move through the life span. Genes are also influential throughout the life span but have their greatest influence in transforming a tiny embryo of a few cells to a complex individual, all in less than 20 years. Extrinsic factors can affect a fetus, but the fetus is protected in the womb from many extrinsic factors. After birth, though, numerous extrinsic factors can influence many aspects of development at the levels of cell, system, and organism. These extrinsic factors can have transient or long- lasting effects. For example, resistance exercise promotes muscle strength, but when an individual stops exercising, the muscles begin losing strength. On the other hand, an active lifestyle in preadolescence may promote bone density that provides a lifelong benefit. This accumulating effect of extrinsic factors explains the great variation in health status that we can observe across individuals toward the end of the life span. The structural constraints influencing movement can operate at the system level. So, when we consider how the changing individual interacts with the task and environment to give rise to movement, it will most often be in the context of change in a system. We now know more about changes in the systems that make up an individual’s structure. Of course, other characteristics of the individual change with growth and aging; vision, hearing, and self- confidence are just a few examples. Our knowledge of change in structural constraints, however, allows us to consider the course of motor development. The model of constraints offers a deeper understanding of motor development than simply a description of the interaction of changing individual, task, and environmental factors. It 173 also explains why certain movements can arise when they do. This is so because often a particular structural constraint shapes movement at a particular point in the life span. For example, consider a 4-month-old who can hold his head up but can’t sit or stand on his own. Why not? We might suggest that the infant needs to undergo an improvement in balance, an increase in trunk muscle strength, an increase in leg muscle strength, an increase in bone density, an increase in leg length to offset the large size of the upper body, or other changes. Which system is the limiting one? The balance system? The muscle system? The skeletal system? That is, which system is the rate limiter, or the system limiting the rate of development? We have come a long way in knowing more about some of the structural changes that accompany growth and aging. We can better predict how specific changes might influence movement, and because we know more about the course of physical development we can better predict when structural changes might change the movement. We also know more about when an individual is subject to greater or lesser influence from extrinsic factors. This knowledge can inform us about changes in extrinsic factors that could bring about changes in movement. Part III considers the course of motor development as the individual, changing in structure over the life span, interacts with the task to be done and the surrounding environment. Reinforcing What You Have Learned About Constraints Take a Second Look The concern about the young woman described at the beginning of this chapter is real. Insufficient nutrition in the months before and after birth can have a limiting and lasting effect on development of the nervous system. It is important that the growth of the body’s systems get off to a good start and that the health of each system be fostered throughout the life span. For example, adults who consume a diet rich in calcium can forestall loss of bone density. We benefit from knowing the course of growth and aging in the individual systems because we can then identify the points in the life span where the individual is most susceptible to extrinsic influence, for better or for worse. Test Your Knowledge 1. What is the epiphyseal growth plate? What is the difference between a pressure epiphysis and a traction epiphysis? Why would an injury to the growth plate in a young child be of concern? 2. What is osteoporosis? Who does it affect, and what are the repercussions for health and for participation in activities? 3. Discuss sex differences in the growth of muscle tissue. How does the growth of cardiac muscle compare with that of skeletal muscle? 174 4. Do adults necessarily gain fat tissue as they age? What is the evidence for your answer? 5. How does the distribution of fat change over the growth period? How does the amount and distribution of fat change in adulthood? What distribution pattern in adulthood is associated with greater risk of cardiovascular disease? 6. What are the major types of hormones involved in growth? How do the hormones influence growth? 7. What contributes to the rapid gains in brain weight during the first year after birth? What part of the brain is most advanced at birth? 8. How has the view of what happens in the nervous system with aging changed with the use of new imaging techniques? What lifestyle factors could spur changes in the nervous system, and how? 9. Choose one extrinsic or environmental factor and describe how it might influence the growth of various systems. Learning Exercise 5.1 System Changes in Older Adults Throughout our discussion, we describe the average or typical course of aging for each system. It is often useful to consider how a particular person either matches or varies from the typical. Locate an adult who is 60 years of age or older and willing to discuss what he or she remembers about changes in his or her skeletal structure, muscle tissue, and adipose tissue. You might ask, for example, about osteoporosis screenings, changes in body weight during middle and older adulthood, and changes in fat versus muscle weight as the person changed body shape. Write a short paper summarizing your conversation, indicating what matched and what varied from the norm described in this chapter. Learning Exercise 5.2 Human Growth Hormone Human growth hormone, or GH (or a releasing factor that stimulates GH secretion), is available for sale on the Internet. Yet the use of GH is banned by many sport governing bodies, some of which test their athletes for use of GH as a supplement. Research the use of GH, then answer the following questions. 1. Why would athletes want to use GH? What effect does it have in individuals who have reached maturity? How does it produce these effects? 2. Are there known risks of using GH supplements? If yes, explain your findings. 3. Even if little research has been conducted on the long-term effects of using GH as a supplement, what are some of the suspected risks of long-term GH supplementation? 175 4. Based on your findings, what would you recommend to a teenage athlete who is tempted to use GH? Why? 176 Part III Development of Motor Skills Across the Life Span In part III, the pieces of the motor development puzzle begin to come together as we examine motor development over the life span and consider the associated constraints influencing the course that development takes. This part of the book discusses the changes in skill performance observed from birth to older adulthood; it also identifies the individual, environmental, and task constraints that may bring about these changes. Chapter 6 discusses early motor development. During the first year of life, infants rapidly acquire new motor skills, progressing toward the ability to move adaptively in their environment. Because of the scope and speed with which young children change, the entire chapter is devoted to the time period between birth and the onset of walking (at approximately 11 to 15 months of age). This is a critical time in atypical development as well because small differences in individual constraints early on can evolve into large differences later in life. Chapter 7 discusses changes in locomotor skills from approximately 1 year of age through the life span. These skills include the most commonly used locomotor patterns—walking and running—as well as skills used in play and sport contexts, such as jumping, hopping, and galloping. You should begin to see similarities in developmental trajectories, which are known as developmental sequences, given the similarity in individual constraints and the guiding principles of motion and stability. You will also see how the development of specific individual constraints influences developmental sequences. Chapter 8 addresses ballistics skills, such as throwing, striking, and kicking. Again, you will see many similarities in the developmental sequences for various ballistic tasks. Finally, chapter 9 covers manipulative skills—both fine motor skills, such as reaching and grasping, and gross motor skills, such as catching. 177 Suggested Reading Adolph, K.E., & Robinson, S.R. (2013). The road to walking: What learning to walk tells us about development. In P. Zelazo (Ed.), Oxford handbook of developmental psychology (Vol. 1, pp. 403–443). New York: Oxford University Press. Galloway, J.C. (2004). The emergence of purposeful limb movement in early infancy: The interaction of experience, learning and biomechanics. Journal of Human Kinetics, 12, 51– 68. Gagen, L.M., Haywood, K.M., & Spaner, S.D. (2005). Predicting the scale of tennis rackets for optimal striking from body dimensions. Pediatric Exercise Science, 17, 190– 200. Mally, K.K., Battista, R.A., & Roberton, M.A. (2011). Distance as a control parameter for place kicking. Journal of Human Sport and Exercise, 6(1), 122–134. Stodden, D.F., Fleisig, G.S., Langendorfer, S.J., & Andrews, J.R. (2006). Kinematic constraints associated with the acquisition of overarm throwing. Part I: Step and trunk actions. Research Quarterly for Exercise and Sport, 77, 417–427. Whitall, J. (2003). Development of locomotor co-ordination and control in children. In G.J.P. Savelsbergh, K. Davids, J. Van der Kamp, & S. Bennett (Eds.), Development of movement coordination in children: Applications in the fields of ergonomics, health sciences and sport (pp. 251–270). London: Routledge. 178 Chapter 6 Early Motor Development Fundamental Individual Constraints 179 Chapter Objectives This chapter describes types of movement that occur in infancy, lists infantile reflexes and postural reactions, explains the relationship between infants’ earlier and later movements, describes motor milestones, explains how early movements are shaped by a variety of constraints, and examines postural development and balance in infancy. 180 Motor Development in the Real World Innovative Interventions for Infants: Babies Driving Robots Children born with severe mobility impairments, such as those associated with cerebral palsy, are at increased risk for mobility-related developmental delays in cognition, language, and socialization. Providing daily mobility between the ages of 1 and 5 years is critical, given that significant learning, brain, and behavioral development is dependent on mobility during this time. The National Science Foundation-funded project, affectionately called “Babies Driving Robots and Racecars,” began at the University of Delaware when Sunil Agrawal, a professor in the department of mechanical engineering, approached Cole Galloway, a professor in the department of physical therapy. Galloway explains, “Dr. Agrawal told me, ‘We have small robots, and you have small infants. Do you think we can do something together?’” Soon after, the researchers created the first prototype, UD1. This robotic car featured a joystick and infrared sonar sensors with obstacle-avoidance software. The researchers tested the prototype in the university’s early learning center, a research facility that accommodates 250 children with varying abilities. In the initial group study, normal 6-month-olds sat in UD1 and pulled the joystick, and away they would go. The children began to understand the cause-and-effect relationship between the joystick’s movement and the car’s movement. Once the children made this breakthrough, the researchers would train them in how to control the direction of their driving. Galloway and his team began to quantify the results of the children’s mobility. The children had increased cognitive and language scores as well as better motor skills. Follow-up case reports on infants and toddlers with spina bifida and cerebral palsy noted improvements in driving skill and developmental scores. Reprinted, by permission, from livescience. Available: www.livescience.com/23572- assistive-robotics-aide-young-children-nsf-bts.html. In the past few years, researchers have taken a careful look at the relationship between movements that infants make and their developing minds. It appears as though this link is much stronger than originally believed. In other words, it is becoming clear that in order to understand cognitive development in infancy, we need to understand motor development and, eventually, determine the interactions and transactions between them. Such an approach fits with the ecological perspective as well as the constraints model. This chapter provides you with information about the motor behaviors exhibited by infants, which is the 181 first step toward understanding how motor development is related to cognitive and perceptual development during infancy. There are also important practical reasons for knowing about infant motor development. In typically developing infants, many movements occur in fairly predictable order and timing. It is essential to learn about and understand typical development in order to be able to recognize deviance—either progression or regression—from the typical pattern. In terms of motor development, most newborn infants exhibit spontaneous and reflexive movements. As infants move toward becoming toddlers, they begin to attain motor milestones. These gross movements slowly become more refined throughout infancy and early childhood. In addition, infants gain the ability to lift their heads, sit up, and eventually stand with minimal support. In the past, parents and educators alike have thought of this process as maturation. In other words, maturation of the central nervous system was the sole individual constraint guiding early motor behavior. This notion, discussed in chapter 2, comes out of the maturational perspective. However, current research from an ecological perspective suggests that the interplay of many systems (cognitive, perceptual, motor) leads to the movement adaptations seen in infancy. Therefore, this chapter presents the concept that many constraints in addition to maturation encourage or discourage early motor behavior. 182 How Do Infants Move? If you watch newborns, you will notice that some of their movements seem to be undirected and without purpose. For example, infants often kick their legs while lying on their backs. These spontaneous movements appear without any apparent stimulation. At other times, infants will move in a specific way every time they are touched in a certain place. Who can resist an infant “holding your hand”—that is, grabbing your finger when you touch her palm? An infant is born with a variety of reflexes that seem to disappear slowly as she ages, and she appears to move with discrete, purposeless actions that have little to do with future voluntary movements. However, there is more to infant motor behavior than meets the eye. Those seemingly random infant movements have an important relationship with intentional movements that occur later in life. After the first few months of life the infant will begin to attain motor milestones, which are particular movement skills that eventually lead to locomotion, reaching, and upright posture. Newborn movements, then, have been classified into two general categories: random or spontaneous movements, and infantile reflexes (Clark, 1995). These two types of movement are very different from each other. Spontaneous Movements People give much attention to the study of infantile reflexes; however, reflexes represent only a small portion of early motor behavior. How else do newborns move? If not eating or sleeping, newborns most likely will squirm, thrust their legs or arms, stretch their fingers and toes, or make other spontaneous movements (also termed stereotypies). These spontaneous movements seem very different from walking or reaching, and pediatricians, parents, and others have believed that they had no particular purpose or relationship to the future movements the child would someday choose to make. However, this may not be the case. Spontaneous movements are infants’ movements that occur without any apparent stimulation. Supine Kicking and Walking If a child is laid on his back (the supine position), he will likely spontaneously thrust his legs. This is called supine kicking. Thelen and her colleagues (Thelen, 1985, 1995; Thelen & Fisher, 1983; Thelen, Ridley-Johnson, & Fisher, 1983) studied the nature of supine kicking in infancy. In analyzing the position and timing of leg segments in these kicks as well as muscular activity in the leg muscles, they discovered some surprising results. The supine kicking was not random but rhythmical, and the kicks had a coordinated pattern. 183 The ankle, knee, and hip joints moved cooperatively with each other, not independently from one another. It seems amazing that these supine kicks during infancy have a coordinated pattern. What is more remarkable is that the coordination of these kicks resembles the positioning and timing of an adult walking step (figure 6.1). The pattern of muscle use in infant supine kicking is also coordinated. Sometimes an infant kicks only one leg, but at other times an infant will kick both legs alternately, just as an adult alternates legs in walking. Even premature infants perform coordinated supine kicks (Geerdink, Hopkins, Beek, & Heriza, 1996; Heriza, 1986; Piek & Gasson, 1999). 184 Figure 6.1 Kicking. (a) Infant kick. (b) Adult step. What might account for some of the differences in infant kicking and adult walking? Think in terms of individual, environmental, and task constraints. Though an infant’s supine kicks are similar to an adult’s walking steps, they are not identical. Infants’ timing is more variable from kick to kick, and they tend to move the joints in unison rather than in sequence. Infants also tend to activate both the muscles for flexing the limb (flexors) and the muscles for extending the limb (extensors). This is called cocontraction. In contrast, adults move by alternating flexor and extensor muscles. However, by the end of their first year, infants begin to move the hip, knee, and ankle sequentially rather than in tight unison. Both alternating and synchronous (i.e., with both legs in unison) kicks are evident after 6 months, indicating that infants are developing more ways to coordinate the two limbs (Thelen, 1985, 1995; Thelen & Fisher, 1983; Thelen et al., 1983). Web Study Guide 185 Observe an infant throughout the first year of his life in Lab Activity 6.1, Identifying Rate Limiters During the First 6.5 Months, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Spontaneous Arm Movements Infants also move their arms, and newborns’ spontaneous arm movements show well- coordinated extension of the elbow, wrist, and finger joints. In other words, the fingers do not extend independently, or one at a time, but rather in unison with the hand, wrist, and elbow (just as with the kick). Arm movements are not as rhythmical and repetitious as leg kicks, though (Thelen, 1981; Thelen, Kelso, & Fogel, 1987). As with the kick, early arm thrusts are not identical to adult reaching movements. It takes infants several months to begin opening their fingers independently of the other joints in anticipation of grasping objects as adults do (Trevarthen, 1984; von Hofsten, 1982, 1984). In addition, these spontaneous movements appear to be influenced by environmental constraints, as Kawai, Savelsbergh, and Wimmers (1999) found when they placed newborn infants in four environmental conditions and discovered differences in frequency and activity of spontaneous arm movements. Key Point Infants’ movements, though not always goal directed or goal achieving, can be coordinated, and the coordination patterns may resemble patterns seen in adults. As mentioned previously, the rhythmic flapping of arms and kicking of legs have been termed stereotypies because of the underlying temporal structure of the movements (Thelen, 1979, 1981). Other stereotypies exist, which Thelen identified according to specific regions of the body, such as legs and feet (e.g., alternating leg movements), head and face (e.g., head banging), and fingers (e.g., flexing fingers). What does the existence of stereotypies suggest? First, newborns may be weak and unable to produce intentional, precise, goal-directed movements, but even at a young age they exhibit underlying rhythmic coordination within limbs or pairs of limbs (Piek, Gasson, Barrett, & Case, 2002). Second, these coordination patterns resemble the coordination patterns we see in later voluntary movement, which suggests some relationship between random and voluntary movement. Perhaps spontaneous movements are part of the fundamental building blocks of voluntary, functional movement (Jensen, Thelen, Ulrich, Schneider, & Zernicke, 1995). 186 Infantile Reflexes Reflexive movements are often visible in young infants. Unlike random movements, reflexes are involuntary movements that an individual makes in response to specific stimuli. Sometimes these responses occur only when the body is in a specific position. An infant does not have to think about making reflexive movements; they occur automatically. Some reflexes, such as eye blinking, occur throughout the life span, but others are present only during infancy (infantile reflexes). We can categorize those seen during infancy into three types of movements: primitive reflexes, postural reactions, and locomotor reflexes (table 6.1). Let’s define each of these categories and discuss their purposes. An infantile reflex is an involuntary, stereotypical movement response to a specific stimulus; the term refers specifically to such responses seen only during infancy. There are three types of infantile reflexes: the primitive and locomotor reflexes and the postural reactions. 187 188 Primitive Reflexes: Around From the Beginning When a newborn infant grasps an object placed in her hand, she does so automatically and without conscious thought. This is an example of a primitive reflex, an involuntary response to specific stimulation that is often mediated by lower brain centers (Peiper, 1963). Generally, newborns exhibit strong reflexes at birth; these reflexes tend to lose their strength over time until they disappear around the fourth month. How can we tell primitive reflexes from spontaneous movements? Reflexes are responses to specific external stimuli, whereas spontaneous movements do not result from any apparent external stimuli. Reflexive movements are specific and often localized, whereas spontaneous movements tend to be nonspecific and generalized. The same stimulus will elicit a specific reflex over and over again (McGraw, 1943). 189 The palmar grasp and the asymmetric tonic neck reflex are shown in figure 6.2. The labyrinthine righting reflex and the stepping reflex are shown in figure 6.3. 190 Figure 6.2 (a) Palmar grasp. The reflex is stimulated by touching or stroking the palm of the hand. (b) Asymmetrical tonic neck reflex. Note the “fencer’s” position, which is one way to identify and describe this reflex. 191 Figure 6.3 (a) Labyrinthine righting reflex. The infant rights the head when tipped backward. (b) Stepping reflex. Postural Reactions: Moving Upright in the World 192 As their name implies, postural reactions, or gravity reflexes, help the infant automatically maintain posture in a changing environment (Peiper, 1963). Some of these responses keep the head upright, thereby keeping the breathing passages open. Others help the infant roll over and eventually attain a vertical position. Postural reactions generally appear after the infant is 2 months old. For example, an infant can roll over only after derotative righting appears after 4 months of age. By late in the first year or early in the second year of life, these isolated reactions requiring specific postures and stimuli drop out of the infant’s repertoire of movements. However, these reflexes don’t literally disappear. Children and adults react to being thrown off balance with specific muscle responses intended to bring the body back to balance. If you have recently tried in-line skating or snowboarding for the first time, you probably know this all too well! As you start to fall, you automatically extend your arms, and this automatic response results in many broken wrists—and unbroken skulls. Locomotor Reflexes: Moving in Place For a while in the 1980s, infant swim classes were all the rage. In these classes, parents placed their newborns in the water and the infants could actually swim! Precocious newborns? Perhaps, but more likely the infants were exhibiting the swim reflex, which, like other locomotor reflexes, gets its name because it appears similar and related to a voluntary movement (in this case, swimming). The locomotor reflexes appear much earlier than the corresponding voluntary behaviors and typically disappear months before the infant attempts the voluntary locomotor skill. There are three such locomotor reflexes: stepping, swimming, and crawling. Appearance and Disappearance of Reflexes In typically developing infants, infantile reflexes gradually show a less specific response with time; eventually, you can no longer stimulate these reflexes. In fact, the primitive reflexes start to weaken or become modified after about 2 weeks (Clark, 1995). Infants learn to adapt their reflexes after 2 weeks in order to modify the movement outcome (e.g., faster sucking leads to a faster supply of milk). Those who work with infants sometimes use the pattern of reflex appearance and disappearance to assess an individual infant’s development. If the reflexes appear and disappear at an age close to the average, they consider the infant’s development typical, whereas deviation from the typical pattern and typical execution of the response may signal a problem. An individual may deviate from typical in two ways: Exhibiting a reflex when the individual should not Not exhibiting a reflex when the individual should A reflex that persists well after the average age of disappearance may indicate a pathological cerebral condition (Peiper, 1963). A nonexistent or very weak response on one side of the 193 body compared with the other also could reflect a pathological condition. In the popular television program ER, the doctors often check an incoming patient by running a probe along the bottom of the patient’s foot. This is a real technique, called the Babinski test (see table 6.1 for a description of the Babinski reflex). This test is used to check for neurological problems in patients with head injuries. A positive Babinski sign indicates that the Babinski reflex as seen in infancy has returned—and that the patient most likely has an injury to the central nervous system. Be careful when attempting to assess the neurological status of an infant (Bartlett, 1997). Remember that each individual develops as a result of interacting individual, environmental, and task constraints. This means that one infant may continue exhibiting reflexes after another of the same age has stopped without any pathological condition being involved. Most infants are ahead of or behind the average ages represented in normative data or scales. In addition, it is difficult to establish the exact time a reflex disappears. Only when the reflex persists for several months past the average might it constitute a warning sign of a pathological condition. You should also be aware that reflexive responses are very sensitive to environmental conditions. If you change an infant’s body position or provide him with a stimulus that is different from those in table 6.1, you won’t get a response. It is easy for an untrained person to overlook some aspect of the environment and thus fail to elicit a response; as a result, he or she may incorrectly conclude that a pathological condition exists. Therefore, trained professionals should be consulted for such assessments. Key Point Parents, teachers, and others outside of the medical profession should bring infants in question to trained personnel for assessment. 194 Why Do Infants Move? The Purpose of Reflexes Ask any mother and she will tell you that infant movements start well before birth! In fact, several reflexes appear as soon as 2 to 3 months in utero. But why are infants born with reflexes? Some, like the rooting reflex, seem to have an obvious purpose: to help an infant survive. Others, such as the asymmetrical tonic neck reflex or swimming reflex, seem to have no clear relevance to the infant at birth. Perhaps some reflexes are important before birth. Researchers have explained the role of reflexes in three general ways: structural, functional, and applied. The structural explanation views reflexes as a byproduct of the human neurological system. That is, some theorists believe that reflexes merely reflect the structure of the nervous system—in other words, the way humans are wired. The functional explanation suggests that reflexes exist to help the infant survive—to eat, breathe, and grasp (Clark, 1995). Milani-Comparetti (1981; Milani-Comparetti & Gidoni, 1967) suggests that the fetus uses reflexes to position itself for birth, then to assist in the birthing process. Both the structural and functional explanations consider reflexes at birth but do not suggest anything about their purpose past birth. Applied theories, in contrast, examine the role of reflexes in future volitional movements. (Once again, we see some very different ideas depending on the theoretical viewpoints of the researchers.) Others take the view that reflexive movements lead to coordinated limb movements (Peiper, 1963), thus giving the infant the opportunity to practice coordinated movements before the higher brain centers are ready to mediate such actions. Look at table 6.1 again. How do you see each reflex fitting into a role for future movements? Relationship of Reflexes to Voluntary Movement Ideas about the relationship between reflexes and later movement have changed dramatically over the past 50 years as a result of some unique experiments. Early researchers such as McGraw (1943) believed that infants could not move voluntarily until reflexes had been inhibited by the central nervous system—a theory termed motor interference. As time went on, researchers began to question this view. A relatively simple experiment by Zelazo, Zelazo, and Kolb (1972a,b) challenged the notion that reflexes and voluntary movements are not related. They elicited the stepping reflex daily in a small number of infants during their first 8 weeks. This daily practice not only increased the stepping reflex in these infants but also resulted in the earlier onset of voluntary walking in the trained infants compared with infants who did not practice the reflex. The investigators concluded that the 195 involuntary walking reflex could be transformed into voluntary walking (see also Zelazo, 1983). They proposed that the disappearance of the reflex was due to disuse, that the period of reflex inhibition before onset of the voluntary skill was unnecessary, and that the systematic stimulation of a locomotor reflex could enhance infants’ acquisition of voluntary locomotion. Esther Thelen (1983, 1995; Thelen & Ulrich, 1991) also questioned whether reflexes had to be inhibited before voluntary movement could occur. With her colleagues, she proposed that other constraints, rather than strictly maturation, may be related to the disappearance of the stepping reflex. Thelen examined the changing individual constraints during early childhood and noticed that infants have a dramatic increase in leg weight, primarily from fat, during the first 2 months of life. She reasoned that this great increase in leg weight, along with the absence of a corresponding increase in muscle strength, may cause the stepping reflex to disappear because the infant has insufficient strength to lift the now- heavier legs. In other words, strength may be a rate limiter for stepping after 2 months or so of infancy. To test this idea, Thelen and her associates took a group of 4- to 6-week-old infants who were still reflex-kicking and added small weights, equal to the amount of weight gain the infants were to experience, to their ankles. The amount of reflex stepping decreased, suggesting that the weight gain might be a viable explanation for the disappearing reflex. However, studies of these very young infants showed only half the picture. Thelen had to demonstrate that reflex stepping still existed in older infants if she accounted for the constraint of strength. To do so, she took older infants (who no longer reflex-stepped) and submerged them to their chests in a small tank of water. The water had the effect of buoying the legs (simulating an increase in strength), and these infants began to step with greater frequency. This result is similar to that of Zelazo’s, whose training may have made the infants’ legs strong enough to step despite weight gain. Finally, Thelen (1985; Thelen & Ulrich, 1991; Vereijken & Thelen, 1997) found that infants who did not reflexively step at 7 months did step when held over the moving belt of a motorized treadmill. The sum of these studies suggests that several individual constraints (rather than simply maturation) play a strong role as rate limiters on movement patterns during infancy. These studies have inspired several other infant research studies that utilized treadmills. Figure 6.4 illustrates the ongoing research that uses treadmills to elicit stepping in infants with Down syndrome. Key Point The increase and decrease of stepping with changes in environmental and task constraints (e.g., addition of a moving treadmill belt, manipulation of leg weight) indicate that systems other than the nervous system must be involved in this aspect of motor development. 196 Figure 6.4 To improve the onset and quality of independent walking, an infant with Down syndrome is trained on a motorized treadmill. Photo courtesy of Dale Ulrich. 197 Motor Milestones: The Pathway to Voluntary Movements Compare the movements of a newborn with those of the same child 12 months later. Somehow, those spontaneous and reflexive movements give way to complex, coordinated, purposeful activities such as walking, reaching, and grasping. What happens to the infant in the intervening months? Clearly, the infant does not suddenly acquire a complex skill; rather, he must learn how to coordinate and control the many interacting parts of his body. He must attain certain fundamental skills that lead to skilled performance. We call these fundamental skills motor milestones (figure 6.5), and each one is a landmark or turning point in an individual’s motor development. Think of them this way: To walk, you must be able to stand; to stand, you must be able to hold your trunk upright; and to hold your trunk upright, you must be able to hold your head erect. Each skill is associated with a preceding milestone. Individual infants vary in the time at which they reach a motor milestone, but they acquire these rudimentary skills in a relatively consistent sequence. Table 6.2 gives the timing and sequence of selected motor milestones. 198 Figure 6.5 Some of the motor milestone skills: (a) sitting alone steadily, (b) standing up by furniture, (c) creeping, (d) rolling from back to front. 199 A motor milestone is a fundamental motor skill, the attainment of which is associated with the acquisition of later voluntary movements. The order in which an infant attains these milestones is relatively consistent, although the timing differs among individuals. 200 Bayley (1936, 1969), Shirley (1931, 1963), and other researchers observed infants and determined a sequence of motor milestones as well as the average ages at which the infants achieved them. This progressive pattern of skill acquisition can be related to predictable 201 changes in individual constraints that occur in typically developing infants. These include maturation of the central nervous system, development of muscular strength and endurance, development of posture and balance, and improvement of sensory processing. After reading this chapter, students may wonder whether the motor milestone research from Bayley and Shirley is still relevant. Haven’t improvements in nutrition and child- rearing practices—as well as innovations in infant toys and equipment—led to quicker attainment of motor milestones? Or perhaps improvements in experimental methods and observation have resulted in more precise age-range windows? The World Health Organization set out to answer these questions with its Multicentre Growth Reference Study (MGRS; de Onis et al., 2004, 2007; Wijnhoven et al., 2006; WHO Multicentre Growth Reference Study Group, 2006). The entire study involved six nations and thousands of children; for assessment of gross motor development, the group observed a total of 816 children from five countries (Ghana, India, Norway, Oman, and the United States). The groups focused on six motor milestones: sitting without support, creeping, standing with assistance, walking with assistance, standing alone, and walking alone. Children were observed longitudinally from the age of 4 months until they could walk on their own. The fieldworkers making observations were highly trained, and high levels of interobserver reliability were set to ensure that all fieldworkers identified the milestones correctly and similarly. What did the WHO Multicentre Growth Reference Study find in terms of age of attainment for the motor skills? The results for the 50th percentile ranking for the MGRS were remarkably similar to those found by Bayley in 1936 and within the ranges found by Shirley in 1963. The greatest discrepancies occurred in the earliest motor milestones; however, Bayley’s average ages never differed from the ages found in the MGRS by more than 3 weeks. For example, the MGRS reported that the 50th percentile of infants sat without support at 5.9 months, whereas Bayley reported an average of 6.6 months and Shirley reported 7.0 months. Similarly, the MGRS reported that 50% of the infants attained the skill of standing with assistance at 7.4 months, whereas Shirley reported 8.0 months and Bayley reported 8.1 months. From this point on, however, the data from the MGRS and Bayley match closely. Shirley’s average ages vary more and are always later than MGRS ages; however, her age ranges are still within the contemporary ranges found by the MGRS. What can we conclude from all of this? First, based on the MGRS, it appears that a secular trend in milestones does not exist. That is, infants are currently attaining motor milestones at about the same age as infants did more than 80 years ago. Second, the observation techniques that Bayley and Shirley used were both valid and reliable. Finally, we can still use the Bayley and Shirley scales with confidence that the age ranges represented indicate 202 where typically developing infants should, on average, be. Web Study Guide Observe babies move in Lab Activity 6.2, Assessing Motor Milestones, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Constraints and the Attainment of Motor Milestones Remember that individual constraints can act as rate limiters, or controllers. That is, for an infant to exhibit a certain skill, she needs to develop a certain system to a particular level. For example, in order to lift her head while lying on her belly, an infant must have sufficient strength in her neck and shoulders (figure 6.6). Because different systems advance earlier in some infants than in others, the rate of achievement of motor milestones varies. Experience and environmental constraints also play a role in individual variability (Adolph, Vereijken, & Denny, 1998). Culturally defined parental handling practices can alter the rate at which an infant attains motor milestones (Clark, 1995). For example, an infant born to a new mother may experience “first child syndrome,” which is not a disease but a cultural phenomenon in the United States wherein first-time mothers hold their infants for long periods and avoid putting the infants on their stomachs for a long time. These periods of prolonged holding result in delayed onset of certain motor milestones such as crawling; the infant does not have the opportunity to strengthen her neck muscles when lying prone. Once again, we see that motor development arises from the interaction of individual, environment, and task. 203 Figure 6.6 In order to crawl, an infant must first be able to lift her head and shoulders in a prone position, which requires neck and shoulder strength. Recent research suggests that the attainment of certain milestones themselves can act as rate limiters for other skills. Corbetta and Bojczyk (2002) looked at the reaching and nonreaching movements and hand preferences of nine infants from the age of 3 weeks until the time at which they could walk independently. The most striking finding was that when the infants attained certain motor milestones—such as sitting, crawling, or walking—they changed their hand preference and even reverted back to an earlier form of reaching (with two hands). In the attainment of walking, the change back to two-handed reaching is likely the result of the infants’ balance acting as a rate limiter; this type of reach is less likely to compromise the infants’ newfound ability to walk. The fluctuation in hand preference provides more evidence to suggest that many interacting constraints influence the development of motor skills. Norm-Referenced Versus Criterion-Referenced Scales It is clearly necessary and beneficial to assess individual children and groups of children. Assessment can, for example, help professionals identify children who need special attention, chart individual and group progress, and choose appropriate educational tasks. Professionals must recognize, though, that testing instruments have specific purposes and are best used when the purpose matches a particular need. For instance, we can broadly classify testing instruments as either norm referenced or criterion referenced. The purpose of norm-referenced scales is to compare an individual or group with previously established norms. Such a comparison indicates where a person falls in a 204 group of like individuals matched on relevant factors, such as age, sex, and race. The value of norm-referenced scales in identifying slowly developing children is obvious; at the same time, such scales give professionals no information about the nature or cause of a delay or about what educational experiences to prescribe to facilitate future development. Criterion-referenced scales indicate where a child falls on a continuum of skills that we know are acquired in sequence. The developmentalist administers the criterion- referenced scale periodically and compares individuals with their own previous performances rather than with a population norm. Often, criterion-referenced scales indicate what skills the individual has mastered and what skills are just emerging. Educators can prescribe education and practice activities based on those emerging skills, thus guaranteeing that the educational task is developmentally appropriate for the individual. Most of the scales developed for infants are of the norm-referenced type. (Typically, more expertise is needed to administer a criterion-referenced scale than a norm- referenced scale.) The Bayley Scales of Infant Development, discussed in this chapter, are norm referenced (Bayley, 1969). The complete Bayley Scales consist of a mental scale (163 items), a motor scale (81 items), and a behavior record for social and attentional behaviors. Users of these scales can compare infants and toddlers from 2 months to 2.5 years with mental and motor norms. Another well-known norm-referenced scale is the Denver Developmental Screening Test (Frankenburg & Dodds, 1967). This test can be used from birth to 6 years of age to assess four areas: 1. Gross motor performance (31 items) 2. Fine motor performance (30 items) 3. Language development (21 items) 4. Personal–social skills (representing age-appropriate social skills; 22 items) A third well-known norm-referenced scale is the Gesell Developmental Schedules (Gesell & Amatruda, 1949). All these instruments are well standardized, but their motor scales are less reliable and valid than is desirable. In this sense they are useful but limited in the information they provide about motor development. Motor Milestones and Atypical Development Because of their sequential nature, motor milestones may provide clues for trained professionals, such as doctors and physical therapists, about an infant’s neurological health. 205 In a study of 173 high-risk preterm infants, Allen and Alexander (1994) evaluated six motor milestones on sequential visits to screen for cerebral palsy. They found that they could predict cerebral palsy accurately when looking for a 37.5% delay on each of the six motor milestones on subsequent evaluations. This finding underscores two important points. First, the milestone sequence is fairly predictable in typically developing infants. Second, although variability exists in the acquisition of milestones, substantial delay in several milestones may indicate a developmental problem. Of course, it’s always best to check with a professional before assuming that an infant has such a problem. Other conditions can lead to differences in the onset of motor milestones. For example, early in development, infants with Down syndrome often experience hypotonia, which is best described as a lack of muscle tone. In fact, infants with Down syndrome have been characterized as “floppy.” Hypotonia often improves later in development. However, this lack of muscle tone usually results in delayed acquisition of motor milestones, such as grasping, sitting, rolling, and pulling to stand, throughout infancy (Jobling & Virji-Babul, 2004). Because these milestones require a certain degree of strength, hypotonia can be considered a rate-limiting factor in the acquisition of these milestones. Furthermore, infants acquire many of these milestones in a sequence that leads to the ability to maintain an upright posture. Therefore, delays in milestones lead to delays in the attainment of fundamental motor skills, such as walking, and activities of daily living, such as eating (Reid & Block, 1996; Ulrich, Ulrich, & Collier, 1992). Because infant mobility appears to be important to early development, any condition that delays or impedes infant mobility may negatively affect cognitive development. Motor development and early movement influence both social and cognitive development. For example, an infant begins to explore his environment by reaching and grasping objects; this allows for a human–object interface that involves multiple sensory systems (e.g., vision, tactile, and, if the object is placed in the infant’s mouth, taste). Such movements help create neural pathways in the brain, which are critical in the first 3 years of life (Kail & Cavanaugh, 2010; Ulrich et al., 1992). If infants are significantly delayed in these experiences, they will miss out on some or all of these opportunities to learn to integrate sensory information (Ulrich, Lloyd, Tiernan, Looper, & Angulo-Barroso, 2008). Part of this learning is discovering cause–effect relationships in their surroundings. Further, independent locomotion provides infants and toddlers with a way to control and explore their environment as well as an opportunity to interact socially (Lynch, Ryu, Agrawal, & Galloway, 2009). Therefore, delayed acquisition of motor skills can have a far more profound effect than simply the inability to move; it can influence the entire developmental process, leading to greater cognitive disabilities than would exist if the same child had had movement opportunities (Ulrich et al., 2008). If you were a physical therapist, what types of delays would you expect to see if you were working with infants who had low muscle tone or muscle 206 spasticity? Consider how the delay in one milestone would affect later milestones. Web Study Guide Use a criterion-referenced scale to assess toddlers in Lab Activity 6.3, Assessing Toddler Motor Behavior, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 207 Development of Postural Control and Balance in Infancy Many of the motor milestones of the first year of life involve the attainment of certain postures, of which sitting and standing are the most obvious examples. Once infants can maintain a posture, they are balancing. As a result, developmentalists have been interested in whether postural control and balance make up the rate-limiting system in the onset of certain milestone skills. They have also been interested in whether infants rely on the same cues for balance that adults do. Some evidence shows that newborns make postural adjustments of the head in response to a visual display of optical flow, or the change in optic patterns that occurs while one moves (Jouen, 1990; Jouen, Lepecq, Gapenne, & Bertenthal, 2000). This finding might indicate that the rate-controlling factor is not the perception of optical flow but rather the making of appropriate postural responses. Researchers have studied this question using the moving- room technique (figure 6.7). Pope (1984) held infants in a seated position on a stationary platform and observed their muscular responses through electromyograph recordings when the walls and ceiling of the small room surrounding them were moved. The effect was to provide visual information that made it seem as if the body, not the room, were moving, while kinesthetic information from the vestibular and somatosensory receptors indicated that the body was not moving. Thus, the visual information and the kinesthetic information were in conflict. The 2-month-olds reacted to the visual information rather than the kinesthetic information. That is, they responded as if their bodies were swaying and activated muscle to regain their starting posture. 208 Figure 6.7 A moving room. In this drawing, the small “room” (made up of four walls and a ceiling) is moved toward the child, and the child falls backward. This response would occur if the child perceived the optical flow produced by the room movement as forward sway rather than as room movement. Reprinted by permission from Bertenthal, Rose, and Bai 1997. Bertenthal, Rose, and Bai (1997) observed 5-, 7-, 9-, and 13-month-olds sitting in a moving room when the room moved at two different speeds. This age span included some infants who could sit alone and some who could not. The investigators found that all the infants, even those not yet capable of sitting alone, responded to room movement (they believed the visual information more than the kinesthetic information), and their action was linked to the movement speed. Investigators also observed that infants’ responses improved with sitting experience; those with experience made postural responses sooner, more accurately, and more consistently. When infants who have just begun standing are placed in a moving room, they often sway, stagger, or fall—unlike adults, who can keep their balance (Bertenthal & Bai, 1989; Butterworth & Hicks, 1977; Lee & Aronson, 1974; Woollacott & Sveistrup, 1994). Newly standing children take longer than adults to use their postural muscles when thrown off balance and sway more before attaining stability (Forssberg & Nashner, 1982). The moving-room effect diminishes in children after their first year of standing. It seems, then, that visual perception of self-motion is not the rate-controlling factor in infant posture and balance. Rather, the rate-controlling factor may be a coupling of the sensory information with the appropriate motor response. The refinement of this coupling occurs for every task, such as sitting and standing. This view is consistent with research on the neurological system, suggesting that vision must be linked to specific motor response loci (Goodale, 1988; Milner & Goodale, 1995). Once refined, these perception–action couplings provide very sensitive and rapid adjustments to the environment. As infants move, their environments change. As the environments change, infants must regulate and refine their movements based on continuous sensory information. In other words, infants 209 must continuously calibrate their sensorimotor coupling (Chen, Metcalfe, Jeka, & Clark, 2007; Metcalfe et al., 2005). As systems develop and change, the sensorimotor coupling must change as well, requiring a recalibration to the environment. Barela, Jeka, and Clark (1999) observed touch control in infants as they reached four important stages: pulling to stand, standing alone, beginning to walk, and reaching 1.5 months of walking experience. As the infants acquired standing experience, both their body sway and the force they applied to a nearby contact surface decreased. In the first three of these stages (pulling to stand, standing alone, and beginning to walk), the infants responded to body sway by applying force to the surface. After the infants gained walking experience, they used the touch information to control posture rather than simply react to sway. Somatosensory information certainly plays an important role in posture and balance. Infant research has established the important role of perceptual information from the various systems in actions that maintain our posture and balance, and more research is needed on how these various sources of information are integrated in the maintenance of posture and balance. 210 Summary and Synthesis A newborn infant moves in a variety of ways. She may kick her legs and wave her arms in a seemingly random fashion; we call these spontaneous movements or stereotypies. She may also respond to a touch with a specific movement pattern; these responses are called infantile reflexes. During the first year she will begin to lift her head and sit alone. Such skills, which appear in a fairly well-defined sequence, are called motor milestones. She will sit, crawl, and stand, all of which demand postural control. In a typically developing infant, these motor behaviors come forward in a relatively predictable sequence and time frame, owing to rapidly changing individual constraints (both structural and functional). Differences among infants in sequence and timing do exist, however. The difference may simply indicate that for this particular infant, the interactions between individual, environmental, and task constraints encourage motor behaviors in a unique way. On the other hand, some infants may have some underlying condition, such as cerebral palsy or Down syndrome, that leads to a delay in the attainment of motor milestones. What do the movements we see during infancy tell us about the infant himself? Developmentalists still debate the purpose of reflexes and spontaneous movements. However, several things are becoming clear. First, these movement patterns are not random; rather, they are coordinated (if not purposeful). Second, early movement patterns play some role in future movement, most likely creating the foundations of future movements. In any case, we cannot separate early movements from later skills. The infant’s experiences—along with his physical characteristics, the environment, parental handling, and other constraints acting in the context of his infancy—all interact in the development of movement skill. Reinforcing What You Have Learned About Constraints Take a Second Look Referring back to the anecdote about babies and robots at the start of the chapter, some people may ask, “Why let an infant drive a robot?” Others may wonder why, if interested in teaching kindergarten through 12th grade or rehabilitating adults, one would study infancy at all. Two important reasons exist. First, infant motor behavior appears to form the basis of later voluntary behavior, as shown by a variety of researchers, such as Thelen and colleagues. Second, a positive relationship appears to exist between motor and cognitive behavior. As Galloway, the researcher who studied babies and robots, explained about infancy, “As soon as you’re reaching, as soon as you’re walking, your cognition explodes.” For infants and children who are developing atypically, observing developmental delays early on may allow for early intervention and a reduction in later deficits. Therefore, it is 211 important to understand infant development because infancy provides an important piece of the motor development puzzle. Test Your Knowledge 1. For four of the infantile reflexes listed in table 6.1, describe either a survival function or a purpose later in life. 2. Consider the individual, environmental, and task constraints interacting during infancy, then give several reasons why an infant might be delayed in attaining a motor milestone. 3. Describe how both task and environmental constraints can have a profound effect on the emergence of motor skills. 4. On what perceptual system do young infants seem to rely for balance information? Learning Exercise 6.1 Identifying Constraints During Infancy and Toddlerhood The attainment of a milestone indicates a unique interaction of various constraints that allow the particular behavior to emerge. The rate at which motor milestones appear (and to a certain extent, the type of milestone) for one infant can differ from that for other infants. This suggests that particular constraints may act as rate limiters, or controllers, for a given infant and that when a critical value of that rate limiter is at last reached, the infant will achieve the motor milestone or skill. One of the best ways to observe constraints in action is to watch a group of infants whose ages vary. Visit a local infant center and carefully observe the infants as their caretakers interact with them. Given your knowledge of infant development from this chapter, make a list of the constraints that affect the motor behavior of individual infants. Find individual, environmental, and task constraints in the context of the infant center. For individual constraints, explain which might act as rate limiters for motor milestones. For environmental and task constraints, explain how these either encourage or discourage different motor behaviors. 212 Chapter 7 Development of Human Locomotion 213 Chapter Objectives This chapter defines the concept of locomotion in humans, describes the types of locomotion, discusses the development of specific locomotor patterns, and explains the individual constraints that affect development of locomotor patterns. 214 Motor Development in the Real World Who Says Growing Old Means Slowing Down? As the world’s population continues to gray, more and more older adults defy popular stereotypes of frailty and weakness by participating in physical activity at an elite level. One such athlete is Philippa “Phil” Raschker, who was selected as Female World Masters Athlete of the Year by the World Masters Athletics Association. Raschker, competing in the 60-to-64-year-old age group, has amassed an amazing 71 gold, 19 silver, and 7 bronze medals at the World Masters Athletics Championships (many in running events, such as the 100 m dash and the 300 m hurdles) since 1983. These include three she won at the championships in 2011. She has twice been a finalist for the Amateur Athletic Union Sullivan Award, given annually to the country’s top amateur athlete of any age group. Other finalists have included superstar athletes LeBron James, Apolo Ohno, Michael Phelps, and Diana Taurasi. The 2004 documentary Racing Against the Clock featured Raschker and four other senior women who are still competing successfully in track and field. Phil Raschker exemplifies the fact that aging does not necessarily preclude one from maintaining, or even developing, considerable locomotor abilities. People interested in motor development often focus on early acquisition of locomotor skills. Our introduction demonstrates, however, that locomotion is a lifelong movement activity. Changes do, of course, occur in walking, running, galloping, and other motor skills as individual, environmental, and task constraints change. This chapter examines various locomotor skills across the life span, how they change systematically, and how individual constraints act as rate controllers. Locomotion is the act of moving, or the capability to move, from place to place. Moving around, getting from here to there: Locomotion is something we do each day without much thought at all. However, this seemingly simple definition may hide the fact that moving from place to place is actually a complex activity that involves many interacting systems and constraints. The study of locomotion falls within many fields, from medicine to psychology, and includes many movements, from squirming to swimming. Across the life span, individuals use various methods of locomotion. Of course, the type of locomotion they use depends on interacting constraints. During the childhood years, height, weight, and lengths change dramatically and may act as rate controllers. During much of the life span, other types of constraints, such as motivation or even the perceived gender association of a skill (e.g., “skipping is for girls”), may encourage or discourage behavior. As one approaches old age, structural constraints such as physical characteristics may return as 215 important rate controllers. However, functional constraints, such as fear of falling or loss of balance capability, may act just as strongly to discourage locomotion, as can environmental constraints, such as weather changes (e.g., snow and ice). As a result, we must examine many changing constraints to understand locomotion across the life span. Locomotion is the act of moving from place to place. 216 The First Voluntary Locomotor Efforts: Creeping and Crawling What does it take for an infant to move from one place to another for the first time? Certain motor milestones must be achieved, such as lifting the head in the prone position. The infant must also have enough strength to support and move himself and must uncouple his limbs, which have primarily moved in unison and in the same direction. In addition to these individual constraints, the environment must allow for infant locomotion, and the infant must evaluate the environment to see how well it matches his or her individual constraints. Adolph (1997) suggests that the environment must afford the infant several things: The surface must provide a continuous path to support the body, must be large enough to allow passage as the body moves forward, must be sturdy enough to support body weight, and must be firm enough, flat enough, and have sufficient friction to maintain balance as weight shifts from limb to limb. (p. 6) Certain systems act as rate limiters, or controllers, that hold an infant back from the initiation of locomotion. Once critical levels are reached in these systems, an infant can begin to move. The first types of locomotion that infants exhibit are usually creeping (moving on hands and knees) (figure 7.1) and crawling (moving on hands and stomach, in a “combat crawl”). The following progression of skills leads to creeping and crawling: 1. Crawling with the chest and stomach on the floor 2. Low creeping with the stomach off the floor but the legs working together (symmetrically) 3. Rocking back and forth in the high creep position 4. Creeping with the legs and arms working alternately 217 Figure 7.1 Infant creeping. Balance and strength must be sufficient for infants to support themselves, first on three limbs and eventually on one arm and the opposite leg. Creeping and crawling occur when all four limbs are in contact with the supporting surface. In crawling, the infant’s chest and stomach also touch the surface. In creeping, only the hands and knees touch the surface. Although not typically seen, another form of quadrupedal gait exists in infants: walking on hands and feet. Burton (1999) reviewed the work of Hrdlicka, who published a book in 1931 called Children Who Run on All Fours, and interpreted it using a dynamic systems approach. He concluded that the emergence of this gait pattern resulted from infrequently occurring interactions between constraints. First, environmental constraints related to crawling surface may make knee support uncomfortable (e.g., gravel, asphalt); thus, the infant changes to feet support. Next, the reinforcement or response of the parent or caregiver may encourage further use of this gait. Finally, average- or above-average strength and health of the infant must interact with these environmental factors to allow the hands- and-feet gait pattern to appear. Because these factors (and perhaps others that haven’t been explored) often do not exist and interact to encourage the hands-and-feet gait pattern, we hardly ever see “running on all fours” in infants. 218 Walking Across the Life Span Typically, a developing human can look forward to a long career as a walker. It’s easy to assume that once people can walk, they won’t change their walking technique much over the life span. As with other motor behaviors, however, people continually change the way they walk as constraints change. What remains the same across one’s lifetime is the underlying timing of walking, which is 50% phasing between the legs (Clark, Whitall, & Phillips, 1988). In other words, individuals alternate their legs so that the left leg is halfway through its motion as the right leg begins its own. Also, there is a period of double support, when both feet contact the ground, followed by a period of single support. These are the relative timing relationships (coordination) that appear early in life, and they don’t seem to change much (Clark, 1995). However, as an individual’s body or the environment changes, the absolute timing (i.e., slower or faster) and placement (i.e., step height or length) can change substantially. Imagine you are a parent. What sort of rate controllers might keep your crawling infant from creeping? Walking is defined by a 50% phasing relationship between the legs as well as a period of double support (when both feet are on the ground) followed by a period of single support. Key Point After infancy, most humans move from place to place using upright bipedal locomotion. A particular pattern of locomotion is called a gait. Upright bipedal gait patterns include walking, running, galloping, skipping, and hopping. First Steps: Characteristics of Early Walking Most people—especially parents—know what a toddler’s first solo steps look like. In fact, researchers have studied and described these first steps (Adolph, Vereijken, & Shrout, 2003; Burnett & Johnson, 1971; Clark et al., 1988; Sutherland, Olshen, Cooper, & Woo, 1980). At first, each step tends to be independent of the next. The toddler takes short steps with little leg and hip extension. She steps with flat feet and points her toes outward. The toddler spreads her feet wide apart when planted to improve her lateral balance. She doesn’t use any trunk rotation. The toddler holds her arms up in high guard; that is, her hands and 219 arms are carried high in a bent position. All of the characteristics in early walking lead to improved balance for the new walker (figure 7.2, a and b). As the child continues to develop, her arms will drop to about waist level (middle guard) and later to an extended position at the sides (low guard; figure 7.2c), but they still will not swing. When children do begin to use the arm swing, it frequently is unequal and irregular; both hands might swing forward together (Roberton, 1978b, 1984). 220 Figure 7.2 (a) A beginning walker. Note the short stride and high-guard arm position. (b) To maintain balance, beginning walkers often plant their feet wide apart, with the toes out. (c) Rather than swing the arms in time with the legs, beginning walkers often hold their arms in high-, middle-, or low-guard position. Parts a and c © Mary Ann Roberton; Part b © Mary Ann Roberton and Kate R. Barrett. Key Point Rate limiters in early walking are muscular strength and balance. Rate Limiters in Early Walking Infants have the ability to move their legs in an alternating pattern from birth onward, yet they cannot walk for at least 7 months after birth. Clearly, several individual constraints must develop to certain critical levels before the infant can support and move his own weight. His legs must be able to move alternately, and he must have enough strength to support himself on a single limb. He must also balance on one leg while transferring his weight to his other foot. These requirements suggest specific rate-controlling factors. Thelen, Ulrich, and Jensen (1989) suggest that infants must have muscle strength in the trunk and extensor muscles to allow them to maintain an upright posture on a small base of support. They must also develop balance, or an erect posture or body position, to the point where they can compensate for the shift of weight from one leg to the other (Adolph et al., 2003; Clark & Phillips, 1993; Clark et al., 1988). Addressing Atypical Walking Development in Down Syndrome As we mention in chapter 6, children with Down syndrome often experience delays in motor milestones. The cascading effect of delayed early milestones leads to delayed onset of walking, sometimes significantly. As part of a series of studies investigating the use of a treadmill intervention to promote earlier onset of walking, Ulrich, Ulrich, Angulo-Kinzler, and Yun (2001) examined a group of infants with Down syndrome. On average, infants 221 began the study at 10 months of age (±1.5 months) and began participating when they could sit independently for 30 s. All participants received physical therapy that infants with Down syndrome typically receive. In addition, the intervention infants received in-home practice stepping on a small motorized treadmill. During the intervention, the parents held the infants over the treadmill, which moved at 0.46 mile per hour. If the infants did not step, the parents repositioned them. This process was performed for 8 min a day (starting at 1 min of intervention followed by 1 min of rest) 5 days per week until the infant began independent walking. Further, the research team visited participants on a biweekly basis and carefully monitored motor development and growth. The results indicated that the experimental protocol was successful. Before independent walking, the intervention group could rise to a standing position as well as walk with assistance sooner than the control group could (the former not significant at p = .09 and the latter difference significant at p = .03). In addition, the group with treadmill training learned to walk with assistance sooner and to walk independently significantly sooner (at 19.9 months vs. at 23.9 months) than the control group. The researchers concluded that the treadmill intervention was successful in encouraging the emergence of independent walking in this sample of infants with Down syndrome. Observing Motor Skill Performance An instructor of motor skills must be able to critically observe children’s skill patterns. The instructor needs to give students feedback, provide further practice experiences, and formally assess their skills. The observation process requires a disciplined, systematic focus on the critical features of a skill pattern rather than on the outcome, or product, of a skill. The observer must learn observation techniques and practice them like any other skill in order to make them automatic. Barrett (1979) provided a guide for improving the observation skills of instructors and coaches based on three principles: 1. Analysis 2. Planning 3. Positioning To analyze developmental movement, the observer must first know the developmental sequences of the skill, including the critical features that characterize a given developmental step and the mechanical principles involved in proficient performance. 222 Observers must also organize and plan their observations in order to prevent their attention from wandering once activity begins. They may find it helpful to have written observation guidelines, many of which can be based on the developmental sequences suggested by researchers. One can, however, design suitable observation guidelines by simply listing the critical features of the skill to be watched. It might also be a good idea for observers to watch a given feature of a skill many times (two or three tries, or more). The third principle is positioning. Many new observers rivet themselves to one location and attempt to watch everything from there. However, some critical features of motor skills can be seen only from the side; others are best seen from the front or back. It is important, then, for the observer to move about and to watch the performer from several angles. The process of motor skill observation demands focused attention. New observers must plan ahead, know the critical features of the skill to be watched, position themselves properly, and practice observing. You can see the key features that distinguish developmental levels by simply watching skill performance, but often there is a need to conduct a more formal assessment. Teachers, therapists, and researchers typically need to record an individual’s development level in order to track progress, design activities, or make comparisons. Conducting a more formal assessment requires several tools: A description of the movements and positions that are characteristic of each step in a developmental sequence A plan for observing movement so that an individual can be quickly and accurately placed into a developmental step or level A recording sheet so that observations can be quickly recorded for future use These tools are provided for many of the fundamental motor skills discussed in the next several chapters. A developmental sequence table lists the developmental steps as well as a description of the movements or positions characteristic of each step. They are organized by a body component, such as the legs or arms, or by a phase of the skill, such as the backswing. An observation plan follows. For each component, the observation plan directs you, by means of a question, to watch for one specific movement or position at a time. By indicating what you observe, you move through the observation plan until you arrive at the performer’s developmental level. This information can be placed on a record sheet, much like the ones used in the lab activities for chapters 7 – 9. 223 Imagine you are a parent. What environmental or task constraints might limit the rate at which walking develops in your infant? Proficient Walking Patterns Part of becoming a proficient walker involves taking advantage of the principles of motion and stability discussed in chapter 3. For example, new walkers optimize balance by widening their stance, which increases their base of support. However, stability is not always desirable, especially because it comes at the expense of mobility. Thus, once an infant’s balance improves, she must decrease her base of support to become more mobile. Many of the characteristics of proficient walking relate to exploiting biomechanical principles as body dimensions change—in other words, recalibrating the body to the environment. Consider these developmental changes in walking that lead to a proficient level: Absolute stride length increases, reflecting greater application of force and greater leg extension at push-off. Also, as children grow, increased leg length contributes to a longer stride. Planting the foot flat on the ground changes to a heel-then-forefoot pattern, which results from an increased range of motion. The individual reduces out-toeing and narrows the base of support laterally to keep the forces exerted in the forward–backward plane. The skilled walker adopts the double knee-lock pattern to assist the full range of leg motion. In this pattern, the knee extends at heel strike, flexes slightly as the body weight moves forward over the supporting leg, and then extends once more at foot push-off. Because the knee extends twice in one step cycle, we call this pattern a double knee-lock. The pelvis rotates to allow the full range of leg motion and oppositional movement of the upper and lower body segments. Balance improves, and forward trunk inclination is reduced. The skilled walker coordinates oppositional arm swing (with the arms extending at the sides) with the movement of the legs. This pattern is consistent with the principle of action and reaction; that is, the opposite arm and leg move forward and back in unison. The arm swing must become relaxed and move from the shoulder, with a slight accompanying movement at the elbow. Developmental Changes in Walking During Early Childhood Children usually achieve developmental changes in walking by an early age; by age 4, most 224 children have the essential ingredients of an advanced walk (Sutherland, 1997). Adolph and her colleagues provide an excellent overview of the development of infant walking in a 2003 paper, aptly titled “What Changes in Infant Walking and Why.” Children exhibit pelvic rotation at an average age of 13.8 months, knee flexion at midsupport at 16.3 months, foot contact within a trunk-width base of support at 17.0 months, synchronous arm swing at 18.0 months, and heel-then-forefoot strike at 18.5 months (Burnett & Johnson, 1971). The length of time for which one foot supports body weight while the other swings forward increases, especially from 1.0 years to 2.5 years of age (Sutherland et al., 1980). Stride length increases throughout midadolescence, partly because of the fuller range of motion at the hips, knees, and ankles and partly because of the increase in leg length resulting from growth. The velocity of the walk also increases, especially between 1.0 and 3.5 years of age (Sutherland et al., 1980). The rhythm and coordination of a child’s walk improve observably until age 5 or so, but beyond this age, pattern improvements are subtle and probably not detectable by the novice observer. Developmental Changes in Walking During Older Adulthood We do not wish to imply that no development occurs between early childhood and older adulthood. However, the changes that do occur represent individual (rather than universal) differences, as discussed in chapter 1. Individuals may change their walking patterns over time due to weight gain or loss, changes in strength or balance, injury, or gait training. Any of these changes will change the constraint interactions during walking. Therefore, we cannot make generalizations about any specific developmental trends in walking in the late teens or early 20s. As for middle adulthood, we cannot predict developmental changes for these years as we can for early childhood because change is individualized and based on changing individual constraints. As individuals enter older age, they will again tend to change in a more predictable way as certain individual constraints tend to change more (figure 7.3). Again, the changes in walking patterns seen in older adults represent a recalibration to the environment and the task based on changes in individual constraints. 225 Figure 7.3 Walking patterns in adulthood tend to change as a result of changing individual constraints. A number of research studies have focused on walking patterns in adults over 60 years old. Murray and coworkers (Murray, Drought, & Kory, 1964; Murray, Kory, Clarkson, & Sepic, 1966; Murray, Kory, & Sepic, 1970) conducted a series of studies on gait patterns in older men and women in which they measured the linear and rotary displacements and the velocity of the limbs during walking. They found that the older men walked in a pattern similar to that of younger men but with these differences: Step length of the older men was approximately 3 cm shorter. Older men toed out approximately 3° more than younger men. Older men had a reduced degree of ankle extension. Pelvic rotation was diminished in older men. Similarly, older women showed greater out-toeing, shorter stride length, and less pelvic rotation than younger women. Another common finding is that older adults walk more slowly than younger adults (Drillis, 1961; Gabel, Johnston, & Crowninshield, 1979; Molen, 1973). Schwanda (1978) confirmed the finding of a shorter stride length among older men and further demonstrated that most other aspects of the walking pattern (stride rate, swing time of the recovering leg, time of support, and vertical displacement of center of gravity) remain similar to those of middle-aged men. You may recall that new walkers also had greater out-toeing and a shorter stride length to assist in balance. Could balance be the reason older adults demonstrate similar movement characteristics? That possibility exists because balance can be affected by the aging process. On the other hand, researchers have associated some of these changes with differences in walking speed. When younger adults walk slowly, they too shorten their strides and 226 decrease joint rotation (Craik, 1989; Winter, 1983). Gabell and Nayak (1984) observed walking in a group of 32 older adults, aged 66 to 84 years, who were selected from a group of 1,187. (The researchers repeatedly screened members of the large group for various types of pathology in order to select the small healthy group.) They found no significant differences between walking patterns of the 32 older adults and those of younger adults. Thus, some of the changes in older adults’ movement patterns might relate to disease and injury in the various body tissues, especially those that result in loss of muscle strength. Even so, these and other studies (Adrian, 1982) indicate that the changes in older adults’ walking patterns are minor. Key Point Rate controllers in walking during older adulthood may be caused by factors such as disuse and fear of falling and therefore may be altered and improved. Rate Controllers in Later Walking Any of the changes associated with the aging process can act as rate controllers in the task of walking. Structural constraints may result from osteoarthritis in the joints or from a decline in muscle mass. However, as noted previously, older adults do not necessarily change their gait in a drastic way. A disease state must progress to a critical level before it will discourage all walking. More often, older adults modify their gait to accommodate pain or changes in balance. Functional constraints, such as balance and fear, can also affect walking patterns. Often, two types of individual constraints will interact and their sum will act as a rate controller. If older adults fall, they may develop a fear of falling. That fear of falling results in a gait designed to assist with balance (wide base of support, short step length). If these factors are combined with pain from osteoarthritis, older adults may be less inclined to walk long distances. Unfortunately, a decrease in walking (and other physical activities) leads to a decrease in muscle mass and flexibility, which in turn affects walking patterns. What results is a sequence of events that eventually discourages walking—a sequence that can be altered if one or several individual constraints are actively manipulated. 227 Running Across the Life Span Picture this scenario: You leave your house late and must rush to catch the bus to get to class. As you approach the bus stop, you notice the bus pulling away from the curb. What do you do? This is not a trick question; of course you run to catch the bus. Humans often run when they need to get from one place to another quickly. Running is a more advanced motor skill than walking, but the two motor patterns have many similar features. For example, in both patterns an individual’s legs move symmetrically but in an alternating pattern with each other. Walking and running also have distinct differences. Walking has a period of double support when both feet are in contact with the ground. This never occurs in running; in fact, running has a flight phase, during which neither foot is on the ground. Running, like walking, has a 50% phasing relationship between the legs. Unlike walking, running has a period of flight, during which neither foot is in contact with the ground. Children typically start to run about 6 to 7 months after they begin to walk (Clark & Whitall, 1989b; Whitall & Getchell, 1995). Remember, for a gait to be considered a run it must include a flight phase. That means that an infant’s earliest attempts to run are actually fast walks. Infants running for the first time may exhibit some of the characteristics of an early walk, even though the infant no longer uses these characteristics in her walk (Burnett & Johnson, 1971). When first learning to run, the child may adopt a wide base of support, a flat-footed landing, leg extension at midsupport, and the high-guard arm position. This regression probably reflects an attempt by the child to simplify the task (e.g., by eliminating the arm swing) until she acquires more experience. As the child practices the running stride and gets used to its balance demands, she will put the swing back into the movement pattern. Characteristics of Early Running Imagine toddlers attempting to run for the first time. All prior attempts at upright locomotion involved at least one limb on the ground at all times. Now they must propel themselves into the air with one leg and then catch themselves with the other. For a toddler, this feat takes tremendous strength and balance. Early characteristics of running reflect the changes in speed (task constraint) between walking and running (see table 7.1 for the developmental sequence). Some of these characteristics are pictured in figure 7.4a. Notice the leg action. You see a brief period of flight, but the legs still have a limited range of motion. The rear leg does not extend fully as the child pushes off the ground. As the swinging leg comes forward, the recovering thigh moves with enough acceleration that the knee bends but without too much acceleration, which would carry the thigh to a position parallel to the ground at the end of the leg swing. 228 Therefore, the range of motion is limited and the stride length is short. 229 Next, examine the arm swing and note the opposition of the arms to the legs. The arms swing to accompany the trunk’s rotation rather than drive forward and back as they would in a skilled sprinter’s movement. The elbows extend when they swing back, which is unnecessary movement; the arms swing out slightly to the side, wasting energy. Beginning runners sometimes swing their arms horizontally, across the body, rather than forward and back, probably to aid their unsteady balance. Figure 7.4b portrays some characteristics of early running that one can observe from the rear. As the child swings the recovering thigh forward, it inefficiently rotates to the side rather than moving straight forward. The arm swings to the side and away from the body, probably to assist with balance, but, again, this movement pattern wastes energy that could be directed toward running forward. 230 Figure 7.4 A beginning runner. (a) The legs have a limited range of motion. The arms extend at the elbows and swing slightly to the side rather than driving forward and back. (b) The thigh and arms swing out rather than forward and back. Part a © Mary Ann Roberton; Part b © Mary Ann Roberton and Kate R. Barrett. Rate Limiters in Early Running To understand the rate limiters in early running, we must review similarities and differences between walking and running. First of all, the coordination patterns are quite similar; both have a 50% phasing relationship between the legs. Therefore, coordination is not likely to be a rate limiter for running. However, running requires a flight phase. To propel themselves into the air, toddlers must have sufficient strength in each leg to lift themselves off the ground. Clearly, then, strength is a very important rate limiter in running (Clark & Whitall, 1989b). Also, once in the air, infants must catch themselves on the other leg and then balance on that leg while shifting their weight forward. Thus, balance is another important rate limiter for running. Proficient Running Like walking, proficient running requires effective use of the biomechanical principles discussed in chapter 3. When running, you must optimize movement forms that allow you to move quickly, even at the expense of balance. Keeping this in mind, we can identify the developmental changes that beginning runners make to optimize their performance, as pictured in figure 7.5, as follows: Stride length increases, indicating that the runner is applying greater force. As greater force is used, several characteristics of mature running emerge: the rear leg is fully extended at push-off; the heel is tucked close to the buttocks and the thigh swings forward with greater acceleration; and the thigh comes parallel to the ground before foot strike. When the recovery leg is swung forward in a tuck position, the runner’s effort is conserved. The runner eliminates lateral leg movements so that forces are kept in the forward– backward plane. For extended running, each foot strikes the ground with the heel first and then the forefoot or strikes the ground in an approximately flat pattern. 231 The runner eliminates out-toeing and narrows the base of support. The runner’s support leg flexes at the knee as the body’s weight comes over the leg. Trunk rotation increases to allow for a longer stride and better arm–leg opposition. The trunk leans slightly forward. The arms swing forward and back, with the elbows approaching right angles, and move in opposition to the legs. Imagine you are a spectator at an Olympic track meet. Most elite sprinters look very similar—their form is almost identical. However, the form of marathon runners differs greatly among individuals. What can you speculate about why sprinters have similar running forms but distance runners have different ones? 232 Figure 7.5 An advanced runner. Note the full range of leg motion. © Mary Ann Roberton and Kate R. Barrett. Developmental Changes in Early Running As children grow, these qualitative changes in running pattern, together with increased body size and strength and improved coordination, typically result in improved quantitative measures of running speed and time in flight. Such changes have been well documented in several University of Wisconsin studies of children between ages 1.5 and 10 years (Beck, 1966; Clouse, 1959; Dittmer, 1962) and in other studies (Branta, Haubenstricker, & Seefeldt, 1984; Roberton, 1984). Therefore, we can expect improvement in the process and product of running performances as children grow, and improvements in the product—increased speed, for example—certainly may continue throughout adolescence. However, not every individual achieves all of the improvements in running pattern during childhood. Most teenagers continue to refine their running form, and it is not uncommon to observe inefficient characteristics in adults’ running, especially out-toeing, lateral leg movements, and limited stride. Perhaps these tendencies reflect skeletal and muscular imbalances in individual runners. Thus, age alone does not guarantee perfect running form; adolescents and adults may have inefficient running patterns. Developmental Changes in Later Running Some research exists on the developmental changes that occur as people age. Nelson (1981) studied the walking and running patterns of older women (aged 58 to 80). She asked the participants in her study to walk normally, walk as fast as possible, jog, and then run as fast as possible. Average speed, stride length, and stride frequency all tended to increase over this sequence, but individuals varied greatly in how they changed from walking to jogging. The older women generally increased their walking speed by lengthening their stride, but they increased their running speed by increasing stride frequency, as do young women. However, major differences were found between younger and older women in the pattern used for fast running: Older women did not tuck their recovering leg as completely. Older women had a shorter stride length. 233 Older women took fewer strides than younger women. The absolute speeds of jogging and running also differed between the age groups. Older women jogged more slowly (1.85 vs. 3.93 m/s) and ran more slowly (2.60 vs. 6.69 m/s) than a group of 20-year-old women (Nelson, 1981). Rate Controllers in Later Running Many of the rate controllers mentioned for later walking also affect running. However, because running requires greater generation of force and greater ability to balance, considerably smaller changes in these constraints may lead to the disappearance of this skill. Furthermore, one may have the ability to run but not the desire or the opportunity; in other words, an older adult may run only in an urgent situation, such as escaping a burning house. However, as more and more seniors discover that maintaining fitness levels can postpone undesired changes associated with aging, more and more opportunities for running exist. The Senior Games (formerly known as the Senior Olympics) have expanded greatly in the past decade; many states have statewide games, and the Huntsman World Senior Games are held in Utah each year. The running events range from the 100 m dash to the half-marathon and even the triathlon. Age categories for runners range from 50 to 85 or 90-plus for men and women. Still, participants in the Senior Games represent a small portion of the population over 50. In fact, it is still noteworthy when an older adult participates in athletics. Not all of the constraints that discourage running in older adults are structural. Imagine you are a physical therapist and think of at least two other constraints in each of the constraint categories. Assessment of Running: Observation Plan Assessing motor skills using developmental sequences can seem like a daunting task to a novice observer. Fortunately, using an observation plan can make the task much simpler (see figure 7.6 “Observation Plan for Running”). In a nutshell, an observation plan allows you to make quick judgments about the developmental level of a particular runner by completing a flow chart of quick yes or no checkpoints. By observing a runner and making decisions about movements, you can establish developmental levels very efficiently and effectively. 234 Web Study Guide Identify developmental differences among runners in Lab Activity 7.1, Assessing the Developmental Levels of Runners, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 235 Other Locomotor Skills Most other locomotor skills have not received the amount of empirical attention given to walking and running. However, many researchers, teachers, and therapists have observed these skills, and we can gain valuable insights from their observations. The skills we discuss here are jumping, hopping, galloping, sliding, and skipping, and like many of the observers, we focus primarily on the childhood years. Jumping Typically, children attempt jumping tasks at a young age and often achieve the simplest forms before age 2. In jumping, individuals propel their bodies from a surface with either one or both feet and land with both feet. Children also acquire specialized forms of jumping during childhood, such as hopping and leaping. Hopping requires taking off and landing on the same leg, often repeatedly. Leaping involves a run with a projection forward from one foot to a landing on the other (increased flight time). Table 7.2 outlines several examples of jumping, hopping, and leaping. Let’s first look at jumping. Jumping occurs when individuals propel themselves off the ground with one or both feet and then land on both feet. Hopping occurs when individuals propel themselves with one foot and then land on the same foot. Leaping occurs when individuals propel themselves with one foot and then land on the other foot. 236 Characteristics of Early Jumping We can gauge developmental changes in jumping in various ways: The age at which a child can perform certain kinds of jumping (age norms) The distance or height of a jump The jumping form or pattern Early developmentalists determined age norms for jumping achievements in preschool children (Wickstrom, 1983). These norms appear in table 7.3, which indicates that children learn to step down off of a higher surface from one foot to the other before jumping off the floor with both feet. Children then learn to jump down from progressively greater heights onto both feet. Later, they master forward jumps, jumps over objects, and hopping a few times on one foot. By school age, children can usually perform all of these jumps. 237 Because of a secular trend, the exact ages at which children today can perform the various jumps might be younger than those in table 7.3, but the order in which they acquire those skills is the same. Developmentalists frequently use product assessments—that is, they measure the horizontal or vertical distance jumped—to assess jumping skill after children have refined the movement process. We focus here on the movement pattern because the measurement of distance jumped is rather self-explanatory and straightforward. Basic skill development in children is a gradual process of refining skills. Oftentimes, this process includes a qualitative change in the skill, such as taking a step forward when throwing. Authors have described development of a particular skill through successive steps, or developmental sequences, based on qualitative changes in critical features of the skill. Two types of developmental sequences exist. The whole-body approach describes all characteristic positions of various body components in a step (table 7.4). The component approach follows each separate body component through whatever number of steps accounts for the qualitative changes observed over time (table 7.5). 238 Several published developmental sequences help us examine the developmental changes that occur in jumping movement patterns. Such sequences identify the steps that children achieve in making the transition from inefficient to proficient movement patterns. The advancements reflect the children’s adoption of movements that take advantage of the principles of motion. We can see improvements in both the vertical jump and the horizontal (standing long) jump, but the developmental sequences that researchers have suggested thus far are based on the standing long jump (Clark & Phillips, 1985; Roberton, 1978b, 1984; tables 7.4 and 7.5). 239 We first identify some of the characteristics of beginning jumpers in both the vertical jump and the standing long jump. Most young jumpers begin by executing a vertical jump, even if they intend to jump horizontally. Look at the beginning jumpers in figures 7.7, 7.8, and 7.9. A vertical jump is shown in figure 7.7 and a horizontal jump in figures 7.8 and 7.9. Note that in all three jumps, the preparatory crouch is slight and the legs are not fully extended at liftoff. In fact, the vertical jumper in figure 7.7 tucks the legs to leave the ground rather than extend them at takeoff to project the body up. In this example, the head is no higher at the peak of the jump than at takeoff. 240 Figure 7.7 Sequential views of a vertical jump. The form here is inefficient. The legs are tucked up under the body rather than fully extended to project the body off the ground. Notice that one foot touches down first. The arms do not assist in the jump; the jumper holds them in the winging posture. Adapted by permission from Wickstrom 1983. 241 Figure 7.8 A beginning long jumper. As the jumper’s weight shifts forward, the toes are pulled off the floor to “catch” the body at landing. The trunk lean at takeoff is less than 30° from the vertical. The arms are used at takeoff but are in an abducted position, laterally rotate in flight, and “parachute” for the landing. © Mary Ann Roberton. 242 Figure 7.9 A beginning jumper. The leg action is in step 3 (table 7.5) at takeoff, as the knees extend at the same time the heels leave the ground. The knees and hips flex together during flight, and the knees then extend before landing. The trunk is somewhat erect at takeoff; it hyperextends in flight, then flexes for landing. The arms wing at takeoff (step 1) before parachuting for the landing. Another characteristic of beginning jumpers is that they do not use a two-foot (symmetrical) takeoff or landing, as shown in figure 7.7, even when they intend to do so. A one-foot takeoff, or step-out, is the lowest level of leg action in the developmental sequence of the standing long jump takeoff. The legs may also be asymmetrical during flight. To improve this leg action, the jumper needs to (1) make a symmetrical two-foot takeoff, flight, and landing and (2) fully extend the ankles, knees, and hips at takeoff after a deep preparatory crouch. The knees and hips flex together in the flight phase of the standing long jump after a full and forceful extension of the legs at takeoff. To jump a long distance, the skilled performer leans the trunk forward at least 30° from the vertical. By age 3, children can change their trunk angle at takeoff to make either a vertical or a horizontal jump (Clark, Phillips, & Petersen, 1989). However, beginning jumpers often keep the trunk too erect during a horizontal jump. When a skilled jumper leans the trunk forward to facilitate jumping for distance, the heels usually come off the ground before the knees start to extend (Clark & Phillips, 1985). Skilled jumpers appear to tip forward at the start of the takeoff. The leg action in which “heels up” begins the takeoff is the most advanced step in the developmental sequence for the leg action of the horizontal jump. Lack of coordinated arm action also characterizes beginning vertical and horizontal jumpers. Rather than use their arms to assist in the jumping action, they may use their arms asymmetrically, hold them stationary at the side, or keep them in a high-guard position as a precaution against falling. Arms may wing (extend backward) ineffectively during flight (figure 7.7) or parachute (extend down and out to the side) during landing (figure 7.8). To achieve a proficient jump, the jumper must use the arms symmetrically to lead the jump 243 from a preparatory extended position to an overhead swing. The developmental sequence for the arm action of the standing long jump progresses from no arm action to limited arm swing; to extension, then partial flexion; and finally to extension, then complete arm swing overhead. Proficient Jumping Through these developmental changes, performers can develop a proficient jumping pattern, as shown in figures 7.10 and 7.11. To execute proficient jumps, they do the following: Get into a preparatory crouch that will stretch the muscles and allow the legs to apply maximal force as they fully extend at the moment of liftoff. Take off for a horizontal jump with the heels coming off the ground and with both feet leaving the ground at the same time. Extend the arms backward, then initiate the takeoff with a vigorous arm swing forward to a position overhead. In jumping for height, proficient jumpers do the following: Direct force downward and extend the body throughout flight. If they are to strike an object or touch something overhead, the dominant arm reaches up and the opposite arm swings down. They gain height through a lateral tilt of the shoulders. Keep the trunk relatively upright throughout the jump. Flex the ankles, knees, and hips on touchdown to allow the force of landing to be absorbed. In jumping for distance, proficient jumpers do the following: Direct force down and back by beginning the takeoff with the heels leaving the ground before the knees extend. The trunk appears to tip forward. Flex the knees during flight, then bring the thighs forward to a position parallel with the ground. Swing the lower legs forward for a two-foot landing. Let the trunk come forward in reaction to the thigh flexing, putting the body in a jackknife position. Flex the ankles and knees when the heels touch the ground to absorb the momentum of the body over distance as the body continues to move forward. Web Study Guide Identify developmental differences among long jumpers in Lab Activity 7.2, 244 Assessing the Developmental Levels of Long Jumpers, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 245 Figure 7.10 An advanced vertical jump for the purpose of reaching high. From a preparatory crouch, this basketball player swings his arms forward and up to lead the jump. The hips, knees, and ankles extend completely at takeoff. Near the peak of the jump, one hand continues up while the other comes down, tilting the shoulder girdle to assist in the high reach. Note that the trunk tends to remain upright throughout. © Mary Ann Roberton and Kate R. Barrett. 246 Figure 7.11 An advanced long jump. The feet leave the ground together and touch down together. The legs fully extend at takeoff, beginning with heels up. The knees then flex in flight, followed by hip flexion and finally knee extension to reach forward for landing. The trunk is inclined more than 30° at takeoff, and the jumper maintains this lean in flight until the trunk flexes for landing. The arms lead the jump and reach overhead at takeoff, then lower to reach forward at landing. Developmental Changes in Jumping With practice, then, children can eventually make refinements in their jumping pattern as described. Continuous growth in body size and strength also contributes to quantitative improvements in how far children can jump. During the elementary school years, children average increases of 3 to 5 in. (~8 to 13 cm) a year in the horizontal distance they can jump and approximately 2 in. (5 cm) a year in vertical distance (DeOreo & Keogh, 1980). Qualitative improvements in jumping vary among children. For example, Clark and Phillips (1985) observed that fewer than 30% of the 3- to 7-year-olds they filmed had the same level of leg and arm action. Most had more advanced leg action than arm action, but some had more advanced arm than leg action. If one component was more advanced than the other, it was usually by only one step, but some children were two steps more advanced in one component than in the other. Thus, we see many movement patterns among developing children. The differences between a vertical jump and a standing long jump involve position and movement speed. For example, in the standing long jump, the hips are more flexed than in the vertical jump as the jumper makes the transition from the preparatory crouch to the takeoff. The hips extend faster in the standing long jump, whereas the knees and ankles extend faster in the vertical jump. Other characteristics of jumping remain stable across developmental steps and type of jump. Clark et al. (1989) found that 3-, 5-, 7-, and 9-year- olds and adults all used the same pattern of leg coordination. In addition, all used that same pattern for both standing long jumps and vertical jumps. Specifically, the timing of hip, knee, and ankle joint extension at takeoff was similar in all groups. Perhaps this similarity reflects the mechanics involved in propelling the body’s mass off the ground. The 247 neuromuscular system must use a leg coordination pattern that gets the body off the ground, but limb positions and movement speeds change as the jumper is better able to optimize jumping distance or adapt the jump to a specific task, such as shooting a jump shot in basketball. It is clear that not all persons master jumping in childhood or even in adolescence. Zimmerman (1956) found many inefficient jumping characteristics in college women, including limited arm swing and incomplete leg extension at takeoff. For children and teens to receive assistance from their instructors in perfecting an advanced jumping pattern, instructors must be able to critically observe and analyze jumping performance. Use figure 7.12 “Observation Plan for the Standing Long Jump” to assess the developmental level of the standing long jump takeoff. 248 Rate Limiters in Jumping For children to perform a two-foot jump, they must be able to develop enough force to bring their bodies into the air from a still position. Unlike in walking and running, they cannot take advantage of a “fall and catch” motion but rather must project their entire bodies into the air. Hopping Adults rarely use hopping to move around, yet to become a skillful mover, an individual should develop hopping skills during childhood. To hop, especially repeatedly, one must project and absorb body weight with just one limb and maintain balance on the small base of support that one foot provides. Complex sport and dance skills often incorporate these movement abilities. Characteristics of Early Hopping Children may move through the levels of arm action and leg action at different rates. Look at the two early hoppers shown in figures 7.13 and 7.14. The leg action of the hopper in figure 7.13 is ineffective as a force producer. The child momentarily lifts the support leg from the floor by flexing it rather than projecting the body up by leg extension; the swing leg is inactive. The arms are also inactive, and the child’s leg and arm actions fall into the first developmental step (see table 7.6 for the developmental sequence). The hopper in figure 7.14 has achieved some leg extension; this child is in step 2 of leg action but still in step 1 of arm action. 249 Figure 7.13 An early hopping attempt exhibiting step 1 leg action and step 1 arm action. The support leg is pulled off the floor to produce only momentary flight. The arms are high and are not working in opposition. © Mary Ann Roberton. 250 Figure 7.14 This girl uses some leg extension to leave the ground, but her swing leg is still inactive. She is in step 2 of the developmental levels for leg action. © Mary Ann Roberton. 251 Proficient Hopping To become proficient hoppers, children need to make the following improvements: The swing leg must lead the hip. The support leg must extend fully. The arms must move in opposition to the legs. The support leg must flex at landing to absorb the force of the landing and to prepare for extension at the next takeoff. 252 The hopper in figure 7.15 has made one of these improvements by moving the arm opposite the swing leg in opposition, but the other arm does not move in a consistent way. The advanced hopper in figure 7.16 assists the hop by moving both arms in opposition to the legs. In terms of leg motion, the hopper in figure 7.15 extends the support leg at takeoff, reflecting good force application, and uses the swing leg, but not vigorously. The hopper in figure 7.16 has made this improvement—the swing leg leads the takeoff, allowing the momentum of several body parts to be chained together, and then swings back behind the support leg to lead the next takeoff. 253 Figure 7.15 A more advanced hop: step 3 in the developmental sequence of leg action, step 4 in arm action. The swing leg leads the hop. Although the range of the swing leg is larger, it could increase even more. © Mary Ann Roberton. 254 Figure 7.16 This boy demonstrates step 4 leg action in that the range of the swing is sufficient to carry the swing leg completely behind the support leg. Both arms move in opposition to the legs. © Mary Ann Roberton. Developmental Changes in Hopping Few children under age 3 can hop repeatedly (Bayley, 1969; McCaskill & Wellman, 1938). Developmentalists often cite the preschool years as the time when children become proficient hoppers (Gutteridge, 1939; Sinclair, 1973; Williams, 1983). Yet Halverson and Williams (1985) found that more than half of a group of 63 children (3-, 4-, and 5-year- olds) were at step 2 in the arm and leg action. They observed few attempts that they could classify at the advanced levels, and hopping on the nonpreferred leg was developmentally behind hopping on the preferred leg. Figure 7.17 shows that many more children were at the lowest developmental step when hopping on their nonpreferred leg than when hopping on their preferred leg. Few children were beyond step 2 when hopping on either leg. If the children in this study are representative of this age group, hopping continues to develop well past the age of 5. 255 Figure 7.17 The developmental level of leg action in 3-, 4-, and 5-year-old hoppers on their preferred leg (top) and nonpreferred leg (bottom). Note that more children were at step 1 when hopping on their nonpreferred leg than when hopping on their preferred leg. Only at age 5 were any notable number of the children at step 3. Reprinted by permission from Halverson and Williams 1985. Why do children advance from one developmental level of hopping to another? Several researchers attempted to answer this question by examining the force and stiffness of landing in hopping (Getchell & Roberton, 1989; Roberton & Halverson, 1988). Note that in step 2 the hopper lands flat-footed and holds the swing leg still. By step 3 the hopper uses a softer landing (more leg flexion to cushion the landing, followed by extension to the next takeoff) and swings the nonhopping leg. Researchers confirmed that the force of landing in a step 2 hop rises sharply on landing, whereas in a step 3 hop it rises gradually. To achieve a soft landing, the neuromuscular system probably prepares ahead of time (ahead of the landing) to moderate the force of landing by allowing the leg to “give” (flex). Perhaps, then, once children achieve a step 2 hop, their ability to project the body higher and travel faster—and perhaps their increasing body weight—increases the force of the landing. Once that force reaches a critical value that could cause a damaging, jarring landing, the neuromuscular system changes children’s hopping movements to allow a softer, more cushioned landing. Hence, children advance to the next developmental level. Web Study Guide 256 Identify developmental differences among hoppers in Lab Activity 7.3, Assessing the Developmental Levels of Hoppers, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Part of the rehabilitation process for some injuries to the lower limbs includes hopping on the injured leg (generally later in the rehabilitation process). Imagine you are a physical therapist and a patient has asked the following question: If adults do not hop regularly, why is hopping so important in rehabilitation? Consider your answer in terms of constraints. An Integrated Approach to Understanding Hopping If we dig deeper into understanding the change in developmental levels in the hop, we reveal the remarkable interaction of individual constraints in the body and within the framework of the principles of motion. Let’s consider a child who hops with step 2 action. The swing leg is held in front; therefore, it simply reacts rather than contributes to the hop. For a lightweight child who needs to produce little force to move, a stationary swing leg does not prevent hopping. The child produces force, primarily downward, from the stance leg. As the child grows she adds body weight and size, which increase her inertia (which, in turn, increases the force required to overcome inertia). Projecting force downward from the stance leg no longer suffices for a hop; she adds swing leg movement to provide additional force, as the movement of the swing leg helps push the body down and back. The ground responds by pushing the body up and forward (Newton’s third law), and the child hops into the air. What goes up, however, must come down—and the child returns to earth with a greater amount of force (due to her increased weight and the height of the hop) than in a step 2 hop. To break the fall, the child must “give” to land softly. This illustrates one way in which the body recalibrates and changes movement patterns to account for changing individual constraints. The changes in the swing leg in step 3 are complemented by changes in the stance leg, both of which lead to a higher, safer hop. Observing Hopping Patterns As with the other locomotor skills, a novice observer must practice hopping assessment. Halverson (1983; see also Roberton & Halverson, 1984) suggests a systematic pattern of observation that focuses on the body parts one at a time. As a novice observer, you should observe leg action from the side. Initially, pay attention to the swing leg. Is it active? If so, does it move up and down or swing past the support leg? Next, observe the support leg. Does it extend at takeoff? Does it flex on landing and extend during the next hop? Look at arm action from the side and the front. Watch first to see whether the arm movement is 257 bilateral or opposing. If it is bilateral, you can then categorize arm movement as inactive, reactive, or backward in direction. If arm movement is opposing, note whether one or both arms move synchronously with the legs. Use figure 7.18 “Observation Plan for Hopping” to assess the developmental levels of hoppers. 258 Rate Controllers in Hopping Hopping likely depends on the postural system’s ability to balance the body on one limb for a succession of hops. Also, to hop repeatedly, the individual must be able to generate enough force to lift the body with one limb, recover, and quickly generate enough force to hop again. Running also requires projection and acceptance of weight on one limb; however, in running, legs alternate and are able to regain energy as they swing in a flexed position. In the hop, the leg stays extended—thus, hopping requires more effort than running. Therefore, the ability to generate force can act as a rate controller. Galloping, Sliding, and Skipping Galloping, sliding, and skipping involve the fundamental movements of stepping, hopping, or leaping. Galloping and sliding, both asymmetric gaits, consist of a step on one foot, then a leap-step on the other foot (Roberton & Halverson, 1984; Whitall, 1988). The same leg always leads with the step. The difference between galloping and sliding is the direction of movement. In galloping, the individual moves forward; in sliding, the 259 individual moves sideways. Skipping is a step and a hop on the same foot, with alternating feet: step-hop on the right foot, step-hop on the left foot, step-hop on the right foot, and so on. The movement is usually forward (figure 7.19, a and b). Consider situations or contexts in which locomotor skills other than walking and running are socially acceptable. Do not limit yourself to sport and dance applications. 260 Figure 7.19 (a) Galloping is a step on the lead leg and a leap-step on the trailing leg. (b) Skipping is a step and then a hop on the foot that just stepped followed by a step and a hop on the other foot, and so on, continuing alternatingly (e.g., step-hop left foot, step-hop right foot, and so on). Adapted by permission from Clark and Whitall 1989. Characteristics of Early Skill Patterns Children’s early attempts at these skills are usually arrhythmic and stiff, as shown in figure 7.20. The arms are rarely involved in projecting the body off the floor. Children might hold their arms stiffly in the high-guard position or out to the side to aid their balance. Their stride or step length is short, and they land flat-footed. Little trunk rotation is used, and they exaggerate vertical lift. In early galloping attempts, a child’s trailing leg may land ahead of the lead leg. 261 Figure 7.20 A beginning galloper. The arms are held stiffly, the stride length is short, and vertical movement is exaggerated. Adapted by permission from Clark and Whitall 1989. Proficient Skill Patterns In contrast, children who are proficient at galloping, sliding, and skipping move in a manner that is rhythmical and relaxed, as seen in figure 7.21. Proficiency in these skills includes the following characteristics: The arms are no longer needed for balance. In skipping, the arms swing rhythmically in opposition to the legs and provide momentum. The child can use the arms for another purpose during galloping and sliding, such as clapping. Forefoot or heel-to-forefoot landings prevail. The knees “give” on landing, remaining flexed while they support the body’s weight, and then extend at takeoff, especially when the child is traveling quickly. 262 Figure 7.21 An advanced galloper. The arms move in opposition to the legs. Movements are rhythmic and landings are not flat-footed. Developmental Changes Galloping is the first of these three bipedal patterns to emerge. It develops sometime between 2 and 3 years of age, after the child has firmly established the running pattern (around age 2) and usually before hopping, which occurs at age 3 or 4. Galloping is the first asymmetrical locomotor pattern a child learns. As noted earlier, walking and running have 50% phasing—the legs make the same movement, but the cycle of one leg is halfway behind the cycle of the other. Galloping, in contrast, is uneven. The steps take longer than the leap-steps. Gallopers, regardless of age, tend to use one of two timing patterns: the step takes either approximately twice as long as the leap-step (a 66% to 33% phasing) or 3 times as long (a 75% to 25% phasing) (Clark & Whitall, 1989b; Whitall, 1988). Children master sliding next, and in both galloping and sliding they develop the ability to lead with the nondominant leg much later than the ability to lead with the dominant leg. Skipping is usually the last of the locomotor patterns to emerge, usually between 4 and 7 years of age. A little more than half of 5-year-olds demonstrate skipping (Branta et al., 1984). At first, a child might perform a unilateral step-hop—that is, a skip with the dominant leg and just a running step with the other leg. When the child begins to skip with both legs, occasional breaks, with a step or gallop interjected, are common (Gutteridge, 1939; Wickstrom, 1987). Though no one has validated developmental steps for skipping, several changes are apparent. A beginning skipper uses a high hop and knee lift. The skip appears jerky, which perhaps reflects the need for much effort to project the body off the ground for the hop. Eventually, the child partially extends the leg on the hop and uses a lower but smoother knee lift, making the skip smoother and more rhythmic. Perhaps greater leg strength allows the child to get the body off the ground with only partial leg extension. Several changes occur in arm action. Beginners use the arms inconsistently, often swinging one or both arms up to the side. Skippers then begin to use the arms bilaterally, swinging them sometimes forward and back in circles, sometimes forward and down. Skilled skippers can use their arms in opposition to their legs (Wickstrom, 1987). 263 It is easy to speculate about why skipping is the last fundamental locomotor skill that children develop. The coordination between the legs is symmetrical, but in each leg the pattern of movement is asymmetrical. Girls typically perform these locomotor skills at an earlier age than do boys, perhaps reflecting their slight edge in biological maturity for chronological age, their imitation of other girls, or encouragement from family and friends. Observing Galloping, Sliding, and Skipping Patterns While observing galloping, first take a side view and note where the trailing foot lands in relation to the lead foot. The extent of vertical lift is also clearly visible from the side. Arms can be viewed from any angle. In proficient galloping the trailing foot lands alongside or behind the lead foot, the flight pattern is low, and the arms are free to swing rhythmically, clap, or engage in another activity. Note whether a child can lead with the dominant leg only or with either leg. Sliding is best observed from the front. Focus on the knees to see if they are stiff, as in early sliding, or relaxed so that the child’s steps have the spring characteristic of proficient sliding. Note whether the arms are in an inefficient guard position or are relaxed and free to be used for another task. As with galloping, you should see if a child can slide to the dominant side only or to both sides. When watching skipping, observe whether the child skips with one leg and runs with the other or skips with both. If the child skips with both legs, look at the height of the hop and the knee lift from the side. Lower height and knee lifts characterize a more proficient, smoother skip. Finally, watch the arm pattern to see if it is bilateral or, in a more proficient skipper, in opposition to the leg movement. Rate Limiters for Galloping, Sliding, and Skipping Galloping generally follows running in the development of motor skills. What rate limiters exist for galloping? To gallop, individuals must uncouple their legs from the 50% phasing they use when walking and running. To do so requires rhythmic or coordination changes. At the same time, the two legs are performing different tasks (step vs. leap-step); therefore, they require different amounts of force, which requires changing force coordination (Clark & Whitall, 1989b). Thus, coordination appears to be a rate limiter for galloping. To slide, individuals must also turn to one side. The neuromuscular system may limit the rate at which these two skills required for galloping and sliding develop. The emergence of skipping does not appear to be limited by generation of force for the hop because children hop before they skip. Nor is balance a probable rate limiter because it is more difficult to balance while hopping than while skipping. As mentioned earlier, skipping is the most complex fundamental locomotor pattern. Skipping might not appear 264 until the individual’s neuromuscular system can coordinate the two limbs as they alternately perform asymmetric tasks. 265 Summary and Synthesis Transporting ourselves from here to there is an important part of human life. We consider locomotion to be one of the first signs of an infant’s independence. Infants may creep, crawl, or move on hands and feet as their initial means of getting around. Not long after, they develop the ability to walk, which is the most basic form of upright bipedal locomotion. Walking involves alternating leg motion, with a period of single-foot support following a period of double-foot support. Next, toddlers run. Running is similar to walking, with alternating foot strikes, but has a period of flight rather than double support. Children then develop the ability to jump, gallop, hop, slide, and skip. All of these more complex locomotor patterns have different constraints that affect the timing and sequence in which they emerge. We can trace the changes in these motor skills throughout individual life spans as the forms of the movements change with the changing constraints of adolescence, adulthood, and older adulthood. From crawling through skipping, children acquire fundamental locomotor skills as their bodies and the world around them change. Many individual constraints act as rate limiters to these emerging skills. After an individual acquires these skills, the form of the skills changes as the child becomes more proficient at them. If you look across locomotor skills, you can see similar patterns of change. For example, in all of the locomotor skills, individuals narrow their base of support to increase mobility and widen it (as in infancy and older adulthood) to increase stability. The developmental changes described in this chapter can be used to generally estimate an individual’s developmental status. Of course, the developmental sequences provide specific characteristics of these changes. It is perhaps not surprising that researchers tend to focus on childhood when studying locomotor skills. Not only do children acquire the skills rather quickly, they also use the skills regularly, which is not the case for most adults. Try to remember the last time you galloped. If you can, you will probably remember that you galloped for some purpose, such as performing a dance. Adults generally do not use the entire range of fundamental locomotor patterns, at least not in the United States. What constrains these patterns from emerging? Farley (1997) describes the energetic inefficiency of human skipping; because skipping is “slow, jolting, and tiring,” it makes an unlikely candidate for adult locomotion. In addition, sociocultural attitudes suggest that these motor skills are not appropriate for adults. The question remains: Do adults and older adults hop, gallop, jump, slide, and skip the same way that children do? Reinforcing What You Have Learned About Constraints Take a Second Look 266 Phil Raschker, mentioned at the beginning of the chapter, is not the only senior athlete with exceptional locomotor skills. In 2005, Ginette Bedard ran a marathon in 3:46:18, a time that is within 6 min of qualifying for the Boston Marathon in the 18- to 34-year-old age group. Ginette was 72 years old at the time. Male senior athletes are no slouches either. Ed Whitlock qualified for the Boston Marathon in the 18- to 34-year-old male category in 2004 by running a marathon in 3:08:35 at the age of 73. These athletes demonstrate that people can avoid or delay declines in individual constraints commonly associated with aging (e.g., strength or endurance) for a substantial period of time. Furthermore, once they obtain locomotor skill, adults can improve on performance long into adulthood. Test Your Knowledge 1. Describe the constraints that may act as rate controllers for specific locomotor activities. 2. How can a teacher or therapist manipulate task constraints to help a child acquire the skill of galloping? 3. What are some of the ways in which humans can move from place to place (without equipment)? Which ones are not currently observed in adults? Why are these locomotor forms rarely used? 4. What movement characteristics might you see in an older adult who is galloping? Why? Learning Exercise 7.1 Comparing Preferred and Nonpreferred Hopping Legs What individual constraints could be involved in determining the developmental level of hopping? 1. Observe three people (try to include at least one child). Ask each person to hop on his or her preferred leg (i.e., whichever foot he or she naturally chooses). Assess each hopper’s developmental level using the observation plan provided in chapter 7. 2. Now, ask each person to hop on his or her opposite, or nonpreferred, leg. What happens to the person’s developmental level? 3. A difference of one developmental level often exists between the preferred leg and the nonpreferred leg, particularly in children. Generate a list of possible reasons to account for the difference. 267 Chapter 8 Development of Ballistic Skills 268 Chapter Objectives This chapter identifies developmental changes in throwing, kicking, punting, and striking movements; compares the characteristics of early performers across the various ballistic skills; and notes similar characteristics of proficient performance of ballistic skills. 269 Motor Development in the Real World Tennis’ Grande Dame Is Still Winning at Age 97 Dorothy “Dodo” Cheney, daughter of women’s tennis pioneer May Sutton and doubles champ Thomas Bundy, is a tennis star in her own right. In 1938 she won the Australian Open, and she hasn’t stopped winning since. In fact, she has won more than 300 senior titles—a record in the United States Tennis Association—since the time she turned 40 years old in 1956. In 2004, she was inducted into the International Tennis Hall of Fame, and in 2010, she received a lifetime achievement award from the San Diego Hall of Champions. In 2011, she won her 381st national championship at age 95. Dodo Cheney is proof that ballistic skills can be executed— and executed well—over the life span. Ballistic skills are those in which a person applies force to an object in order to project it. The ballistic skills of throwing, kicking, and striking have similar developmental patterns because the mechanical principles involved in projecting objects are basically the same. The ballistic skill that researchers have studied most is the overhand throw for distance. Much of the discussion on throwing also applies to kicking and striking, which we examine later in this chapter. 270 Overarm Throwing Throwing takes many forms. The two-hand underhand throw (with windup between the legs) and the one-hand underhand throw are both common in young children. There is also a sidearm throw and a two-hand overarm throw. The type of throw that a person uses, especially among children, often depends on task constraints, particularly rules and the size of the ball. Our focus, though, is on the one-hand overarm throw, which is the most common type of throw in sport games and has been studied more widely than other types. Many of the mechanical principles involved in the overarm throw apply to other types of throws as well. Researchers often make product assessments to gauge throwing skill development; that is, they measure the end product, or result, of the throwing movement, such as the accuracy, distance, or ball velocity. However, product measures have several drawbacks. Researchers must often change an accuracy assessment task when working with children of different ages. Young children need a short distance over which to throw to reach the target, but a short distance makes the task too easy for older children, who might all achieve perfect scores. Thus, researchers must either increase the distance or decrease the target size for older groups. In addition, scores on throws for distance often reflect not just throwing skill but also factors such as body size and strength. Two children may have equal throwing skills but quite different distance scores because one child is bigger and stronger. Finally, measuring ball velocity at release requires specialized equipment that may not be readily available. Thus, we could argue that product scores are not as useful to teachers, parents, and coaches as knowing how a child throws. Let’s now turn our attention to the quality of the throwing pattern. Characteristics of Early Overarm Throwing It is helpful to contrast children’s early attempts to throw with an advanced overarm throw. Young children’s throwing patterns, especially those of children under 3 years, tend to be restricted to arm action alone (Marques-Bruna & Grimshaw, 1997). The child depicted in figure 8.1 does not step into the throw or use much trunk action. This child merely positions the upper arm, often with the elbow up or forward, and executes the throw by elbow extension alone. Figure 8.2 shows more movement but little gain in mechanical efficiency. These children demonstrate minimal throwing skill. 271 Figure 8.1 A beginning thrower simply brings the hand back with the elbow up and throws by extending the elbow without taking a step. © Mary Ann Roberton. Key Point In very young children, throwing consists of arm action alone. 272 Figure 8.2 A beginning thrower. Note the trunk flexion, rather than rotation, with the throw. © Mary Ann Roberton and Kate R. Barrett. Proficient Overarm Throwing By studying the characteristics of a proficient throw, we can identify the limitations in early throwing attempts. An advanced, forceful throw for distance involves the following movement patterns: The weight shifts to the back foot; the trunk rotates back; and the arm makes a circular, downward backswing for a windup. The leg opposite the throwing arm steps forward to increase the distance over which the thrower applies force to the ball and to allow full trunk rotation. The trunk rotates forward to add force to the throw. To produce maximal force, trunk rotation is “differentiated,” which means the lower torso leads the upper torso, resulting in a movement that looks like the body “opens up.” The trunk bends laterally, away from the side of the throwing arm. The upper arm forms a right angle with the trunk and comes forward just as (or slightly after) the shoulders rotate to a front-facing position. This means that from the side, you can see the upper arm within the outline of the trunk. The thrower holds the elbow at a right angle during the forward swing, extending the arm when the shoulders reach the front-facing position. Extending the arm just before release lengthens the radius of the throwing arc. The forearm lags behind the trunk and upper arm during the forward swing. While the upper trunk is rotating forward, the forearm and hand appear to be stationary or to move down or back. The forearm lags until the upper trunk and shoulders actually rotate in the direction of the throw (the front-facing position). The follow-through dissipates the force of the throw over distance. The greater portion of wrist flexion comes during follow-through, after the thrower releases the ball. Dissipating force after release allows maximal speed of movement while the ball is in 273 the hand. The thrower carries out the movements of the body segments sequentially, progressively adding the contributions of each part to the force of the throw. Generally, the sequence is as follows: 1. Forward step and pelvic rotation 2. Upper spine rotation and upper arm swing 3. Upper arm inward rotation and elbow extension 4. Release 5. Follow-through The backswing is the backward, or takeaway, movement to put the arm, leg, or racket in a position to move ballistically forward to project an object. Developmental Changes in Overarm Throwing Now that we have discussed the characteristics of an advanced, forceful throw, we can examine how an individual progresses through the developmental steps from initial throwing attempts to advanced throwing skill. Several developmental sequences of overarm throwing have been proposed, beginning with a sequence outlined by Wild in 1938 and including that of Seefeldt, Reuschlein, and Vogel in 1972. Later, Roberton proposed a developmental sequence for the overarm throw using the body component approach. Two of the component sequences, arm action and trunk action, are validated developmental sequences (Roberton, 1977, 1978a; Roberton & DiRocco, 1981; Roberton & Langendorfer, 1980). In fact, Roberton and Konczak (2001) determined that changes in developmental sequences (i.e., a change from level 2 to level 3) accounted for more than half the change in velocity in throwing among 39 children studied over 7 years. Carefully studying the developmental overarm throw sequence outlined in table 8.1 will help you compare these steps with the characteristics of early throwers depicted in figures 8.1 and 8.2 and those of the more advanced throwers shown in figures 8.3 through 8.6. If you were a physical education teacher, what factors would you expect to increase the likelihood that a developing child would reach the advanced steps in each component of throwing? Validated developmental sequences are sequences of advances in the performance of a skill that have been determined by longitudinal study and shown to fall in the same fixed order for all individuals. 274 275 276 Figure 8.3 A thrower with stage 2 arm action. The forearm reaches its farthest point back before the shoulders rotate to front-facing, but the humerus then swings forward before the shoulders; the elbow is consequently visible outside the body outline. Note the right angle between the humerus and the trunk. © Mary Ann Roberton. 277 Figure 8.4 A relatively advanced thrower. Arm, leg, and preparatory action are characteristic of the most advanced step, but the trunk action is characteristic of stage 2, or block rotation, rather than differentiated rotation. © Mary Ann Roberton. 278 Figure 8.5 From the rear you can see that this advanced thrower flexes the trunk laterally away from the ball at release. © Mary Ann Roberton. 279 Figure 8.6 This still drawing of a baseball pitcher captures the forward movement of the hips while the upper trunk is still back. This is called differentiated trunk rotation because the hips and upper trunk rotate at different times. © Mary Ann Roberton. Just as with locomotor skills, you can more easily assess the developmental sequences of throwing using an observation plan (see figure 8.7 “Observation Plan for Throwing”). 280 281 Illustrations © Mary Ann Roberton. Begin the comparison by focusing on the trunk action component. In the first step of the developmental sequence, you do not see trunk action or forward or backward movements before the thrower releases the ball (figures 8.1 and 8.2). In the second step, the thrower proceeds to a block rotation of the trunk. Block rotation occurs between the third and fourth positions in figure 8.4. Distance throwers typically flex the trunk laterally (figure 8.5). The most advanced trunk action—differentiated trunk rotation—is often observable in pictures of baseball pitchers. In figure 8.6, the pitcher has started to rotate the lower trunk toward the direction of the throw while the upper trunk is still twisting back in preparation to throw. Specific parts of the trunk start rotating forward at different times. 282 Block rotation of the trunk is forward rotation of the lower and upper trunk as a unit. In differentiated trunk rotation, the lower trunk (hip section) rotates forward while the upper trunk (shoulder section) is rotating backward, still preparing to rotate forward. To analyze the complexity of arm movements in throwing, first study the preparatory backswing, then the upper arm (humerus) motions, and finally the forearm motions. An unskilled thrower often does not use a backswing (figure 8.1). At the next step in the developmental sequence, a thrower flexes the shoulder and elbow in preparation for elbow extension, as in figure 8.2. A more advanced preparation involves using an upward backswing, but the most desirable backswing for a throw for distance is circular and downward. The thrower pictured in figure 8.4 is using this pattern. As an unskilled thrower begins to swing the upper arm forward to throw, he or she often swings it at an angle oblique to the line of the shoulders—that is, with the elbow pointed up or down. A desirable advancement is to align the upper arm horizontally with the shoulders, forming a right angle with the trunk, as seen in figure 8.3. Even so, the upper arm may move ahead of the trunk’s outline, which results in a loss of some of the momentum the thrower gains from moving the body parts sequentially for a forceful throw. In the most advanced pattern, the upper arm lags behind so that when the thrower reaches a front-facing position, you can see the elbow from the side within the outline of the trunk, as in figure 8.4. It is also desirable for the forearm to lag behind. The thrower in figure 8.3 has some forearm lag, but the deepest lag comes before rather than at the front-facing position. The thrower in figure 8.4 demonstrates the advanced pattern of delayed forearm lag. Key Point Letting distal body sections lag behind more proximal ones allows momentum to be transferred and distal sections to increase speed, providing the movements are well timed. Most unskilled throwers throw without taking a step, like the child in figure 8.1. When a child learns to take the step, he or she often does so with the homolateral leg—the leg on the same side of the body as the throwing arm—which reduces the extent of trunk rotation and the range of motion available for a forceful throw. When the child acquires the advanced pattern of a contralateral step, he or she may initially take a short step, as in figure 8.2. A long step (more than half of the thrower’s height) is desirable. The body component analysis of overarm throwing demonstrates that individuals do not achieve the same developmental step for all body components at the same time. For example, the thrower in figure 8.2 is in step 1 of trunk, humerus, and forearm action but in 283 step 3 of foot action. The thrower in figure 8.4 is in step 3 of humerus, forearm, and foot action but in step 2 of trunk action. Children who are the same age may be moving at various levels of the body component sequences, so they look different from one another as they advance through the developmental sequence. However, not every possible combination of steps within the components is observed. In considering the trunk, humerus, and forearm components, Langendorfer and Roberton (2002) observed just 14 of the possible 27 combinations of developmental steps for these three components. It is likely that structural constraints limit the movements that some body sections can make while other body sections are moving in a particular way. When you observe throwing, then, you will tend to see certain common combinations and you probably will not see others at all. Langendorfer and Roberton (2002) also studied the common combinations of steps within components as children develop. They found a tendency for children to change from no trunk rotation to trunk rotation before the upper arm and forearm advanced to intermediate levels. The shift of the humerus to an advanced level occurred after both the upper arm and forearm advanced to the intermediate level. It is likely that mechanical constraints and neurological development are responsible for these trends; that is, they are likely to be rate controllers in the development of throwing. It is desirable for all individuals to move through the various developmental steps during childhood in order to achieve an advanced throwing pattern that they can use in a number of physical activities, from softball to football to team handball. In fact, several authors noted that children can develop a skillful throwing pattern by age 6 (DeOreo & Keogh, 1980; McClenaghan & Gallahue, 1978; Zaichkowsky, Zaichkowsky, & Martinek, 1980). At least two studies, however, present contradictory results. Halverson, Roberton, and Langendorfer (1982) filmed a group of 39 children in kindergarten and first, second, and seventh grades and classified them according to Roberton’s developmental sequence. Their analysis of upper arm action demonstrated that most of the younger boys were already at step 2 of humerus action, and by the seventh grade, more than 80% of boys had achieved the most advanced level (step 3). In contrast, approximately 70% of the girls were still in step 1 of humerus action when initially filmed. By the seventh grade, only 29% of the girls had reached step 3. This trend was also apparent for forearm action. Almost 70% of the boys demonstrated step 2 forearm action when initially filmed. Some were still at this level by the seventh grade, but considerably more—a total of 41% of the boys—had reached step 3. More than 70% of the girls began in step 1, and the majority—71% of all the girls—were only at the second level in the seventh grade. Sex differences in developmental throwing progress were even more apparent for trunk action. Almost all the boys started in step 2, and 46% advanced to step 3 by the seventh grade. Similarly, almost 90% of the girls were in step 2 in kindergarten, but by the seventh grade all the girls remained in step 2; none had advanced to step 3. 284 Another study (Leme & Shambes, 1978) focused on throwing patterns in adult women. The 18 women were selected because they had very low throwing velocities. All demonstrated inefficient throwing patterns, including block rotation, lack of a step forward with the throw, and lack of upper arm lag. Although these women were unique because of their low throwing velocities, the study certainly demonstrates that not all adults achieve an advanced throwing pattern. Perhaps these women lacked practice opportunities or good instruction in childhood. Together, these two studies suggest that progress through the developmental levels is not automatic and may not ever be completed. Observing Overarm Throwing Patterns Overarm throwing is complex and difficult to observe in detail. The best procedure is to focus on a small number of components, or even a single component, at any one time. Some characteristics are best observed from the front or back: Trunk-to-upper-arm angle Elbow angle Lateral trunk bend Others are best observed from the throwing side: The step Trunk rotation Upper arm and forearm lag Videotaping is particularly valuable in helping you learn to observe the overarm throw. By videotaping, one can go back and review the different arm actions separately and in slow motion. Web Study Guide Identify developmental differences among throwers in Lab Activity 8.1, Assessing the Developmental Levels of Overarm Throwing, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Throwing in Adulthood As we have seen, throwing is a complex skill that requires the coordination of many body segments. To execute a maximum throw, the thrower must move many joints through a full range of motion with precise timing. This makes throwing an interesting skill to study 285 in older adults. For example, we can ask whether older adults coordinate their movements for throwing just as young adults do or use different movement patterns. If they use the same patterns, we can ask whether older adults control those movements as young adults do or vary the extent or speed of movements in ways different from the movements of young adults. Let’s begin with observations of older adults that tell us what movement patterns older adults use. Williams, Haywood, and VanSant (1990, 1991) used the developmental steps in table 8.1 to categorize active older adult men and women between the ages of 63 and 78. Although the developmental steps were identified to monitor change in children and youths, they can be used to describe the movement patterns of throwers at any age. Key Point In comparing younger and older adult throwers, we can observe both the movement patterns used and how the movements are controlled. The older adults were active in a university-sponsored physical activity program but did not practice throwing or participate in activities with overarm movement patterns. The investigators found their throwing movements to be only moderately advanced on the developmental sequences. Most older throwers took a short contralateral step (step 3) and were categorized at step 1 or 2 of humerus action and step 1 or 2 of forearm action. Almost all used block rotation of the trunk (step 2). Sex differences similar to those in children existed; that is, men generally had better form. However, qualitative throwing status also related to childhood and young adult experiences. Those who had participated in sports with overarm movement patterns at younger ages had better throwing form. The ball velocities generated by the older adults were moderate (similar to velocities generated by 8- to 9-year-olds). The men averaged 54.4 ft/s (16.6 m/s), and the women averaged 39.1 ft/s (11.9 m/s). Hence, the older adults also confirmed the sex differences in velocity noted in youths. Because actions during the backswing in ballistic skills generally are related to ball velocity, Haywood, Williams, and VanSant (1991) closely examined the backswing used by older adults. Those who used a circular, downward backswing threw faster than those using an upward (therefore shorter) backswing. Many older adults used backswing movement patterns that seemed different from those that children use. For example, many started the circular, downward backswing (step 4) but did not continue the circle. Instead, they bent the elbow to bring the ball up behind the head. A possible reason for this could be a change in the musculoskeletal system, such as decreased shoulder flexibility or a loss of fast-twitch muscle fibers. Possibly the throwers could not continue arm movement at the shoulder 286 joint, or would experience pain in doing so, and thus they reorganized the movement. The older adults in these studies were not observed when they were young adults, so we do not know whether any or all of them reached the highest developmental level in all the body components when they were younger. We can only hypothesize that their moderate status as older adults reflects at least some change from the movement patterns of their youth. A commonly held notion of skill performance in older adulthood is one of consistent decline with advancing age. To observe throwing with advancing age, Williams, Haywood, and VanSant (1998) observed eight older adults over a period of 7 years. One individual was in her 60s, but most were in their late 70s. In contrast to what many would predict, throwing movements were relatively consistent over the years. Participants were placed in the same sequential step in 80% of the possible observations of body components over all the years. In cases where individuals changed, the change often (though not always) involved a decline. Increased variability was associated with change; that is, if a participant changed their developmental level from a previous session, they would often be inconsistent over the five trials, showing a variety of developmental levels. Williams and colleagues also observed small changes over the years that did not necessarily result in a change in developmental step. These small changes included decreased range of motion and slower movement speeds. This longitudinal observation shows that throwing performance in older adulthood is relatively stable. Small changes are more typical of performance than are large declines. Key Point Throwing movements of older adults are characterized more by stability in the developmental steps than by rapid decline. Change is more often typified by increased variability from throw to throw, a slight slowing of movement, or a more limited range of movement. It’s clear that older adults coordinate their throwing movements as do young adults of moderate throwing skill. Few older adults are observed to use the same movement patterns as the most advanced young adults, but this could reflect the limited number of observations of older adults as well as the constraints imposed by rate-controlling systems. The change observed over time in older adulthood is most likely to be change in the control of movements, especially a slowing of speed or a decrease in range of motion. Though we need more research and more longitudinal observation of older adults, the model of constraints can guide our study of older adult performance. One or more body systems might regress, causing a slowing or limitation of movement, then reach a critical 287 point at which the movement pattern must change. For example, advancing arthritis in the shoulder joint could cause the musculoskeletal system to act as a rate controller for throwing movements. Some movement patterns might be unique to older adulthood because declines in the various body systems that occur with aging might not be exactly the opposite of the advances that occur with physical growth. Others might well be the same movement patterns seen in children and youths as they advance through the developmental sequence. Throwing for Accuracy The developmental sequences constructed for overarm throwing specifically address a throw for distance rather than for accuracy. The model of constraints would lead us to predict that changing the task from throwing for distance to throwing for accuracy results in a change in the movement pattern, and Langendorfer (1990) demonstrated this to be the case. He had young adults and 9- to 10-year-olds throw for distance and accuracy. The accuracy task was to hit an 8 ft (2.4 m) circular target at a distance of 11 yd (10 m) for adults and 6.6 yd (6 m) for children. Male throwers were categorized at significantly lower developmental steps when throwing for accuracy than for distance. Female throwers tended toward lower steps but were not significantly different in the two task conditions. Langendorfer felt that the distance for the accuracy throw for females resulted in a forceful task condition, which suggests that under true accuracy conditions throwers use movement patterns that are different from those for distance conditions. Key Point If the developmental sequences for forceful throwing are used to describe a short throw for accuracy, even the most proficient throwers might not use the most advanced movement patterns. Think of throwing as it is required in three or four sports or games. Do these activities emphasize distance, accuracy, or a combination of both? In which of these conditions would it be appropriate to use the developmental sequences with the perspective that the most proficient throwers would place in the upper steps? Williams, Haywood, and VanSant (1993, 1996) replicated Langendorfer’s study with older adults, asking them to throw for distance and for accuracy to a target 11 yd (10 m) away. Throwing velocity was measured for both task conditions, and the observers found that throwers used a slower velocity in the accuracy condition. As a group, the older adults 288 changed little from one condition to the other, but most individuals adapted their movements in at least one body component. As with Langendorfer’s female throwers, the older adults likely found the 11 yd distance of the accuracy throws to require relatively more force than young men would perceive was necessary. An accuracy condition at a shorter distance might have elicited more differences in the movements used. Of course, in sports and games, throws are rarely made for distance without some accuracy constraint, or for accuracy without the need for force. What this research demonstrates is that different movement patterns arise for different task constraints, even for the same person in the same environment. When we compare movement patterns, then, by using either developmental categories or some other description of a movement pattern, we must recognize that the comparisons are valid only when the task constraints are identical. Even then, the person–task interaction influences the movement. For example, a strong individual could throw a given distance without the need for a step of the contralateral foot, whereas a weaker individual needs a step to reach that same distance. Parents, coaches, teachers, and recreational leaders must keep such factors in mind when comparing throwers. Web Study Guide Compare throwing for force versus for accuracy by doing Lab Activity 8.2, Comparing Throws for Force With Throws for Accuracy, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 289 Kicking Like throwing, kicking projects an object; unlike throwing, however, the kicker strikes the object. Thus, children must have sufficient perceptual abilities and eye–foot coordination in order to execute a kick and consistently make contact with the ball. Teachers and parents can simplify the task for young children by challenging them to kick a stationary ball. A kick is a ballistic strike from the foot. Characteristics of Early Kicking As with throwing, unskilled kickers tend to use a single action rather than a sequence of actions. As you can see in figure 8.8, there is no step forward with the nonkicking leg, and the kicking leg merely pushes forward at the ball. The knee of the kicking leg may be bent at contact, and an unskilled kicker may even retract the leg immediately after contacting the ball. The trunk does not rotate, and the child holds the arms stationary at the sides. The child in figure 8.9 demonstrates more advanced kicking skill by stepping forward with the nonkicking foot, thus putting the kicking leg in a cocked position. Key Point Just as young children throw with arm action alone, young kickers use only leg action. 290 Figure 8.8 A beginning kicker simply pushes the leg forward. © Mary Ann Roberton. 291 Figure 8.9 This kicker has made some improvements compared with the beginning kicker. He steps forward, putting the leg in a cocked position, but the leg swing is still minimal. The knee is bent at contact, and some of the momentum of the kick is lost. © Mary Ann Roberton. Proficient Kicking Compare the characteristics of early kicking with the critical features of advanced kicking shown in figure 8.10. The advanced kicker does the following: Starts with a preparatory windup. The kicker achieves this position, with the trunk rotated back and the kicking leg cocked, by leaping or running up to the ball. As a natural consequence of the running stride, the trunk is rotated back and the knee of the kicking leg is flexed just after the push-off of the rear leg. Hence, the kicker is able to apply maximal force over the greatest distance. Running up to the ball also contributes momentum to the kick. Uses sequential movements of the kicking leg. The thigh rotates forward, then the lower leg extends (knee straightens) just before contact with the ball to increase the radius of the arc through which the kicking leg travels. The straightened leg continues forward after contact to dissipate the force of the kick in the follow- through. Swings the kicking leg through a full range of motion at the hip. Uses trunk rotation to maximize the range of motion. To compensate for the complete leg swing, the kicker leans back at contact. Uses the arms in opposition to the legs as a reaction to trunk and leg motion. 292 Figure 8.10 An advanced kicker. Note the full range of leg motion, trunk rotation, and arm opposition. © Mary Ann Roberton. Developmental Changes in Kicking The study of kicking development in children has not been as extensive as educators would like. Although we know the overall changes children must undergo to perform an advanced kick, the qualitative changes that each body part makes are not well documented. Haubenstricker, Seefeldt, and Branta (1983) found that only 10% of the 7.5- to 9.0-year- old children they studied exhibited advanced kicking form. So we have reason to speculate that, as with throwing, children do not automatically achieve proficient kicking. What other factors can modify the movement pattern used in kicking? Recently, Mally, Battista, and Roberton (2011) investigated the effect of distance on kicking form. Using a dynamical systems perspective (see chapter 2), they postulated that distance acts as a control parameter for kicking, much as the way speed, when scaled up or down, changes a walk to a run or vice versa. Following this logic, individuals will change their kicking pattern once a critical kicking distance is reached. Mally and colleagues tested 19 children at an average age of 8.1 years. The children kicked at five randomly ordered distances (approximately 1.5, 3, 6, 9, and 12 m). They kicked three times each while being videotaped. The authors analyzed the kicks based on key features of kicking, such as the final foot position in the approach, arm position on the final step, and leg and arm action during the forward swing of the leg. Of these features, four changed significantly as a function of distance (number and type of forward steps in the approach, position of the shank in the forward leg swing, leg action in the follow-through). The fact that significant differences existed suggests that distance may act as a control parameter for kicking. This also offers clues into the basis of developmental change in the kick, in that the pattern of changes resembles that seen in prelongitudinal screening of other skills. In other words, children changed movement form when asked to kick longer distances much in the way children improve proficiency over their childhood. This suggests that the ability to generate force may be a key component driving developmental change in kicking. 293 Observing Kicking Patterns To give children adequate instruction in kicking, it is especially important to observe individual children. From the side, a teacher or coach can look for placement of the support foot, range of motion and precontact extension in the kicking leg, range of trunk motion, and arm opposition. Let’s now turn to the development of punting—a special form of kicking for which researchers have hypothesized a developmental sequence. 294 Punting The ballistic skill of punting is mechanically similar to kicking, yet punting tends to be more difficult for children to learn. To punt, a child drops the ball from the hands and must time the leg swing to the dropping ball. A punt is a form of kicking where an object is dropped from the individual’s hands before impact with the foot. Characteristics of Early Punting A beginning punter tends to toss the ball up rather than drop it and will often release the ball after the support leg contacts the ground, if the child even takes a step at all. The arms drop to the sides. The child might rigidly extend the kicking-leg knee or bend it at a right angle, as in figure 8.11. The child typically holds the foot at a right angle to the leg so that the ball contacts the toes rather than the instep, resulting in an errant punt. 295 Figure 8.11 A beginning punter takes only a short step and flexes the kicking-leg knee 90° at contact (step 1). The ball is dropped from waist height (step 3), but the arms drop to the sides at contact (step 1). © Mary Ann Roberton. Proficient Punting To execute a sound punt, as shown in figure 8.12, a child must extend the arms forward with the ball in hand before dropping it as the final leg stride is taken; move the arms to the side after releasing the ball, then move into an arm opposition pattern; leap onto the supporting leg and swing the punting leg vigorously up to contact the ball, such that the body leaves the ground with a hop of the supporting leg; and keep the kicking-leg knee nearly straight and the toes pointed at time of contact. 296 Figure 8.12 An advanced punter. The last step is a leap, the ankle is extended (plantar flexed) at ball contact, and the punt is completed with a hop on the support leg (step 3). The ball is dropped early from chest height (step 4), and the arms abduct and move in opposition to the legs (step 3). © Mary Ann Roberton. Developmental Changes in Punting Roberton (1978b, 1984) hypothesized a developmental sequence for punting (table 8.2). Arm action is divided into two sequences, one for the ball-release phase and one for the ball-contact phase. The ball-release sequence outlines progress that moves from tossing the ball up to begin the punt, to dropping the ball late, and finally to timing the drop appropriately. The ball-contact sequence shows that the arms make a transition from nonuse to bilateral movement, then to the arm opposition pattern that characteristically accompanies forceful lower trunk rotation. 297 298 The leg action sequence reflects a developmental transition from a short step of the nonkicking leg to a long step and finally to a leap. At contact, the ankle of the kicking leg changes from a flexed to an extended position. Observing Punting Patterns Observing a punter from the side offers you a view of the ball drop, the arm position, and the foot position (see figure 8.13 “Observation Plan for Punting”). You can clearly see the degree of foot extension at ball contact from this position. 299 300 Sidearm Striking Although many sports and physical activities incorporate striking, research data on the development of striking is sparse. Striking encompasses numerous skills. It can be done with various body parts, such as the hands or feet. People can also use a variety of implements in various orientations, such as swinging a bat sidearm, a racket overhand, or a golf club underhand. In our discussion, we focus on one-hand sidearm striking with an implement and one-hand overarm striking with an implement. Sidearm striking is a form of striking where the arm remains at or below shoulder level. One example of sidearm striking is a person swinging a baseball bat. Of the basic skills we’ve discussed so far, striking involves the most difficult perceptual judgment. Success in meeting a moving object is limited in early childhood; therefore, it is difficult to assess striking of a moving object by young children. For this reason, teachers often adapt striking tasks for young children by making the ball stationary. Researchers often base the developmental sequences on striking a stationary ball so that they can describe the changes in young children’s movement patterns. We can apply the mechanical principles and developmental aspects of one-hand striking of a stationary object to other types of striking tasks. Keep this in mind as we examine the development of the striking pattern. Table 8.3 shows the developmental sequence of the sidearm strike. 301 Characteristics of Early Sidearm Striking A child’s first attempts to strike sidearm often look like unskilled attempts to throw overhand. The child chops at the oncoming ball by extending at the elbow, using little leg and trunk action. As in figure 8.14, the child often faces the oncoming ball. 302 Figure 8.14 This young girl executes a striking task with arm action only. She faces the ball and swings down rather than sideways. © Mary Ann Roberton. Proficient Sidearm Striking An advanced sidearm strike incorporates many of the characteristics of an advanced overarm throw. Such characteristics include the following: Stepping into the hit, thus applying linear force to the strike. The step should cover a distance more than half the individual’s standing height (Roberton, 1978b, 1984). The preparatory stance should be sideways to allow for this step and the sidearm swing. Using differentiated trunk rotation to permit a larger swing and to contribute more force through rotary movement. Swinging through a full range of motion to apply the greatest force possible. Swinging in a roughly horizontal plane and extending the arms just before contact. Linking or chaining the movements together to produce the greatest force possible. The sequence is as follows: backswing and forward step, pelvic rotation, spinal 303 rotation and swing, arm extension, contact, and follow-through. Developmental Changes in Sidearm Striking Researchers have not validated a completed developmental sequence for sidearm striking, but we can apply the sequences for foot and trunk action in the overarm throw to striking (see figure 8.15, “Observation Plan for Sidearm Striking”). In addition, we know some of the qualitative changes individuals make in the arm action for sidearm striking. The arm action for sidearm striking is distinct from that for overarm and underarm (as in the golf swing) striking, but all three forms share many of the same mechanical principles. We discuss sidearm striking first, but keep in mind that many of the qualitative changes in the arm action for sidearm striking, as well as the mechanical principles involved, apply to overarm striking as well. The first obvious change in sidearm striking from the technique shown in figure 8.14 occurs when a striker stands sideways to the path of the incoming ball. By transferring the weight to the rear foot, taking a step forward, and transferring the weight forward at 304 contact, a striker is able to improve striking skills. The child pictured in figure 8.16 turns sideways but has not yet learned to step into the strike. 305 Figure 8.16 This girl has made improvements as compared with the beginning striker in that she stands sideways and executes a sidearm strike. However, she does not yet involve the lower body. © Mary Ann Roberton. A second beneficial change is the use of trunk rotation. In a developmental sequence similar to that for throwing, individuals first use block rotation before advancing to differentiated (hip, then shoulder) rotation. A skilled striker who uses differentiated rotation appears in figure 8.17. 306 Figure 8.17 An advanced striker. The swing arm moves through a full range of motion. The striker steps into the swing and uses differentiated trunk rotation. © Mary Ann Roberton. Strikers also progressively change the plane of their swing from the vertical chop seen in figure 8.14 to an oblique plane and finally to a horizontal plane, as seen in figure 8.16. Eventually, they obtain a longer swing by holding their elbows away from their sides and extending their arms just before contact. A beginning striker frequently holds a racket or paddle with a power grip, where the handle is held in the palm like a club (figure 8.18, a and b; Napier, 1956). With this grip the striker tends to keep the elbow flexed during the swing and to supinate the forearm, thus undercutting the ball. Although children tend to use the power grip with any striking implement, they most often adopt it when given implements that are too big and heavy for them. Educators can promote use of the proper “shake-hands” grip by giving children striking implements that are an appropriate size and weight (Roberton & Halverson, 1984)—that is, by scaling the size and weight of the implement to the size and strength of the child. 307 Figure 8.18 (a) Beginners often use a power grip, causing them to undercut the ball. (b) A “shake-hands” grip is desirable for sidearm striking. Observing Sidearm Striking Patterns As with many of the skills we’ve looked at thus far, studying a child’s swing from more than one location yields the most information. From the “pitching” position (i.e., directly in front of the child, at a safe distance away and in a location where you can administer a pitch) you can observe the direction of the step, the plane of the swing, and arm extension. From the side you can check the step, the trunk rotation, and the extent of the swing. 308 Overarm Striking One can execute overarm striking without an implement, as in the overarm volleyball serve, or with an implement, such as in the tennis serve. We focus on overarm striking with an implement. Overarm striking is a form of striking where the arm travels above the shoulder level. One example of overarm striking is a person swinging a racket in a tennis serve. Characteristics of Early Overarm Striking A beginning striker demonstrates limited pelvic and spinal movement, swings with a collapsed elbow, and swings the arm and racket forward in unison, as in figure 8.19. If the striker is receiving a pitched ball, the collapsed elbow leads to a low point of contact between the racket and the ball. The movement pattern of early overarm striking, then, is similar to that of early overarm throwing and early sidearm striking. 309 Figure 8.19 Beginning overarm striking. Trunk movement is minimal. The elbow is collapsed, and the arm and racket move together. Proficient Overarm Striking A person who is skilled at overarm striking, as depicted in figure 8.20, does the following: Rotates both the pelvis and the spine more than 90°. Holds the elbow at an angle between 90° and 119° at the start of forward movement. Lets the racket lag behind the arm during the forward swing. Racket lag is consistent with the open kinetic chain principle, where force is generated by a correctly timed sequence of movements. The humerus and forearm lag is an example of an open kinetic chain: The humerus lags behind trunk rotation, the forearm lags behind the humerus, and the racket lags behind the forearm to create the chain of sequential movements. 310 Figure 8.20 Proficient overarm striking. Trunk rotation is obvious. The racket lags behind the arm during the swing. Developmental Changes in Overarm Striking Langendorfer (1987) and Messick (1991) proposed developmental sequences for overarm striking. Both sequences are based on cross-sectional studies; neither has been validated with longitudinal research. Overarm striking is similar to overarm throwing and sidearm striking, but it also has unique features. Langendorfer identified eight component sequences from a study of children 1 to 10 years old. The trunk, humerus, forearm, and leg sequences are similar to those for overarm throwing (table 8.1). Sequences unique to overarm striking include pelvic range of motion, spinal range of motion, elbow angle, and racket action (table 8.4). Messick observed 9- to 19-year-olds executing tennis serves. She identified elbow angle and racket sequences similar to those that Langendorfer identified, except that extending the forearm and racket up to contact the ball was characteristic of the tennis serves. She also noted a developmental sequence of preparatory trunk action in tennis overarm striking. This appears in table 8.4. 311 Neither Langendorfer nor Messick found the developmental sequences for foot action in throwing to apply to overarm striking, although they observed age differences in weight shifting—older performers shifted their weight more than younger ones. Perhaps overarm striking requires a different sequence that has not yet been identified. This may be especially true in the context of tennis, where the rules specify that the server must not step on or over the baseline before striking the ball. Observing Overarm Striking Patterns Observation of overarm striking is similar to that of sidearm striking. You might prefer, though, to watch from behind rather than from the “pitching” position, in addition to watching from the side. Web Study Guide Identify developmental differences among strikers in Lab Activity 8.3, Assessing the Developmental Levels of Striking, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 312 Older Adult Striking As active middle-aged and older adults such as Dodo Cheney make sports news, we know that ballistic skills can be lifetime skills. The research on active adults performing ballistic skills is limited but likely to increase as larger numbers of seniors maintain an active lifestyle involving sports that require ballistic skills. It is not surprising that tennis and golf are two of the contexts for older adult research because both have a large senior following and established senior programs. The tempo and rhythm of a short iron shot in golf have been compared in younger (19- to 25-year-old) and older (60- to 69-year-old) males who are experienced golfers (Jagacinski, Greenberg, & Liao, 1997). This task, of course, emphasizes accuracy more than distance. As a group, the older golfers had a slightly faster tempo, or overall speed of the shot. Differences in rhythm also existed. Older golfers reached peak force earlier in the swing, whereas younger players reached it just before impact. Older golfers also had larger force changes in later phases of the swing. This might indicate that the older golfers exert relatively more force to execute this short iron shot than younger golfers. In terms of accuracy, 3 of 12 older golfers made less than 10% of their shots, but the remainder were as accurate as the younger golfers. Increased variability in accuracy among members of the older group was found, then, and many older golfers showed no decrement. We should keep in mind that the strength and flexibility demands of this task were relatively low; thus, losses of strength and flexibility with advancing age would not have constrained the older golfers compared with younger ones. What might be the rate controllers causing older adults to reorganize their movement patterns for striking? Would these controllers differ for those who remain active in a “striking” sport, as compared with those who are sedentary? Haywood and Williams (1995) observed older adult tennis players as they executed an overarm serve. These older adults played tennis an average of 2.7 times per week. They were divided into a younger group (from 62 up to 68 years old) and an older group (69 up to 81 years old). The developmental steps described earlier for preparatory trunk action, elbow action, and forearm and racket action were used to categorize the servers by movement pattern. Ball impact velocity was also measured. The younger and older servers did not differ in any of these measures, nor did male and female servers differ. Most of the servers used moderate-level trunk, forearm, and racket movements, but the older group was somewhat more advanced in elbow action. So, the investigators found little evidence of significant decline for a population continuing to use a striking skill. 313 Key Point Many older performers can be as accurate as younger performers when strength and flexibility demands are not high, but it is likely that some performers in a group of elders will not be as accurate as the young. Key Point Well-practiced movement patterns might be well maintained over the life span. The investigators measured static shoulder flexibility in the senior tennis players to determine whether a decline of flexibility might act as a rate controller for overarm striking movements. However, there was no difference in flexibility between the two age groups. Of course, as with the older throwers described earlier, these tennis players were not observed longitudinally, and we do not know whether they ever used more advanced movement patterns. Two of the servers, one man and one woman, were placed into the highest developmental category of each body component observed; both servers were former tennis teaching professionals. This investigation therefore suggests that well-practiced movement patterns tend to be maintained over the older adult years and perhaps even from younger years. 314 Summary and Synthesis Proficient performance in the ballistic skills exhibits movements that obey mechanical principles for maximizing force and speed (as discussed in chapter 3). As children and youths improve their performance of ballistic skills, we see changes that make their movements more and more consistent with those mechanical principles. Examples include a forward step that transfers momentum into the direction of the throw or strike; rotary motions of the trunk, usually sequenced as lower trunk followed by upper trunk for arm throws and strikes; and sequential movement of the projecting limb to allow distal body components and striking implements to lag behind larger and more proximal body components so that momentum is transferred and speed is increased. We know that transition to the most efficient movement patterns is not automatic. Some adults continue to use movement patterns that produce moderate results when maximal ones are desired. Because little observation of life span striking performance is available, it is difficult to know the amount of decline in the throwing and striking performance of older adults. Active older adults, however, appear to maintain movement patterns fairly well, especially when the patterns are well practiced. Task conditions and the interaction between the person and the task are important in determining what movement patterns emerge in performance. In assessing how youths are progressing or whether seniors are declining, we must not only consider the rate controllers possibly influencing those changes but also acknowledge the particular task conditions for the movement observed. An individual throwing a short distance for accuracy would not necessarily need to use the movements characteristic of the most advanced level in each body component. Reinforcing What You Have Learned About Constraints Take a Second Look Dorothy “Dodo” Cheney has integrated changing constraints in order to remain successful at tennis over eight decades. She has adapted to changes in her own body, winning matches in every age group in which she has played. She has also won on every playing surface on which she has competed (environmental constraints) and in singles, doubles, and mixed doubles (task constraints)! Test Your Knowledge 1. What distinguishes kicking from punting? 2. Identify four of the major qualitative changes in the development of each of the 315 following ballistic skills: throwing, punting, and overarm striking. 3. What qualitative developmental changes are shared by throwing and overarm striking? Why might both skills change in these ways? Learning Exercise 8.1 Overarm Throwing: Changes in Form Related to Changes in the Throwing Arm What individual constraints could be involved in determining the developmental level of overarm throwing for force? 1. Observe three people (try to include at least one child). Ask each person to throw with his or her preferred arm (i.e., the arm he or she naturally chooses). Assess each thrower’s developmental level using the observation plan provided in chapter 8. 2. Now, ask each person to throw with the opposite, or nonpreferred, arm. What happens to the developmental level? 3. There will often be a difference of at least one developmental level between the preferred and nonpreferred throwing arms. Generate a list of possible reasons to account for the difference. 316 Chapter 9 Development of Manipulative Skills 317 Chapter Objectives This chapter documents a transition in infancy from the use of power grips to pick up objects to the use of precision grips; demonstrates how the size of an object, relative to the size of the hand, can influence the grip used to pick up the object; examines the role of vision in reaching for objects; identifies developmental changes in catching; and considers how catchers are able to intercept objects. 318 Motor Development in the Real World Helping Hands In January of 2013 Matthew Scott celebrated the 14th anniversary of his hand transplant, which was the first in the United States and the most successful in the world to date. Scott, who is left handed, lost his left hand in a firecracker accident in 1985. He received the transplant from a cadaver in a 15 h operation (Handfuls of Happiness, 2000). A year and a half later, Scott was able to sense temperature, pressure, and pain in his new hand as well as turn pages, tie shoelaces, and throw a baseball. At his 8-year checkup he could hold a 15 lb weight with his transplanted hand and pick up small objects. Those of us who have had an arm or wrist in a cast can probably begin to imagine what it would be like to attempt certain tasks or sports with one hand or without hands. Our hands allow us to perform a wide range of skills, from handling small, delicate objects to steering large ships. Today the use of cell phones to send text messages is commonplace, and most teens probably consider typing text messages to be a fundamental skill! As with any other movement, we expect limb movements to arise from the interaction of individual, task, and environmental constraints. Consider the task of lifting a heavy crystal bowl from a table, as well as the question of whether an individual should pick it up with one hand or two. The environment plays a role because gravity acts on the object. Crystal is heavier than plain glass. The task is a factor in several ways. Consider the shape of the bowl. Does the shape and the weight—that is, the interaction of environment and task—afford lifting the bowl with one hand, or does it require two? Now consider the person’s strength. Does this individual structural constraint interact with task and environment to afford lifting with one hand or two? With growth and aging, many individual structural constraints change. The length and size of the limbs change with growth, as does strength. On the other hand, when we age, conditions such as arthritis can make manipulative skills difficult or even painful. Thus, just as with other types of skills, the performance of manipulative skills changes with growth and aging. 319 Grasping and Reaching When a skilled adult wants to obtain a small object, the arm reaches forward and then the hand grasps the object. The reach and grasp form a smooth movement unit. To simplify our study of the development of reaching and grasping in infancy, however, we’ll consider grasping, or prehension, first. Prehension is the grasping of an object, usually with the hand or hands. Grasping In 1931, H.M. Halverson published a classic description of grasping development, the 10 phases of which are summarized in figure 9.1. Halverson filmed infants between 16 and 52 weeks of age grasping a 1 in. (2.5 cm) cube. In early grasping, the infant squeezes an object against the palm without the thumb providing opposition. Eventually, the infant uses the thumb in opposition but still holds the object against the palm. Such grips are collectively called power grips. Halverson observed that after about 9 months of age, infants began to hold objects between the thumb and one or more fingers; these are called precision grips. Thus, the first year is characterized by a transition from power grips to precision grips. 320 Figure 9.1 A developmental progression of grasping. Hohlstein (1982) later replicated Halverson’s study, but in addition to the 1 in. cube used in the original study, she provided objects of different sizes and shapes. The transition from power to precision grips was still evident, but shape and size of the object influenced the specific type of grasp used. In fact, by 9 months of age infants reliably shape their hand in anticipation of an object’s shape as they go to grasp it (Lockman, Ashmead, & Bushnell, 1984; Piéraut-Le Bonniec, 1985). Key Point The first year sees a transition from power grips to precision grips, but the particular grip used is influenced by the shape and size of the object grasped. Through Halverson’s early work, developmentalists viewed prehension as a behavior acquired in steps. Maturationists of Halverson’s era viewed these age-related changes in the same vein as motor milestones. They linked each progression to a new stage with neuromotor maturation, especially maturation of the motor cortex. However, the finding that shape and size of the object to be grasped influence the grip used suggests that the individual, the environment, and the task interact in prehension movements. Halverson studied only one set of environment and task characteristics. More variety of movement grips is observed with changing environment and task characteristics in the early months of life. 321 For example, Newell, Scully, McDonald, and Baillargeon (1989) watched 4- to 8-month- old infants grasp a cube and three cups of different diameters. They found that infants used five types of grip 95% of the time. The specific grip seemed to depend on the size and shape of the object. The investigators even observed precision grips with the smallest cup at a younger age than Halverson observed. Because we can observe this precision grip at such a young age, it’s clear that the neuromotor system must be mature enough at this young age to control the precision grip. Lee, Liu, and Newell (2006), who observed prehension longitudinally from age 9 to 37 weeks, also found that the grasp used by infants depended on the properties of the object. Clearly, then, neuromotor maturation is not the only structural constraint involved in grasping. Key Point After infancy, visual information and body size appear to constrain the shape of the hands in grasping and the number of hands used to grasp a particular object. Newell, Scully, Tenenbaum, and Hardiman (1989) suggested, based on observations of older children, that the grip used to obtain any particular object depends on the relationship between hand size and object size. That is, the movement selected by individuals is related to their hand size compared with an object’s size, or movements reflect body scaling. Butterworth, Verweij, and Hopkins (1997) tested this idea by having infants between 6 and 20 months of age pick up cubes and spheres of different sizes. They confirmed Halverson’s general trend from power to precision grips. By early in the second year, precision grips predominated. Younger infants tended to use more fingers to grasp the objects than older infants did. Object size greatly influenced the grip selected, and shape had somewhat less influence on the grip. Butterworth and colleagues observed all but Halverson’s inferior-forefinger grasp in the youngest infants, 6 to 8 months of age, so infants use a greater variety of grips than we would assume from Halverson’s work. Thus, neuromotor development for grasping movements must be more advanced than those in Halverson’s day thought. Task and environmental constraints clearly play an important role in infants’ adaptations of their movements. Body scaling is adapting characteristics of the task or environment to the overall body size or to the size of a body component. The same movement or action can be carried out by individuals of different sizes because the ratio of body size to object or dimension is the same—that is, a body-scaled ratio. Think about making and eating your breakfast in the morning. How many objects do you grip, and how does the configuration of your hand change for each one? Have you ever broken your wrist and found it was difficult to do 322 such tasks? It so happened that the infant boys in Butterworth and colleagues’ study had longer hands than the girls had. The hypothesis of Newell, Scully, Tenenbaum, and Hardiman predicted that these boys and girls would use different grips, but this was not the case. Thus, the influence of object size on grip used supports the idea that the ratio between hand size and object size is important, but the lack of a difference between the boys and girls does not. Therefore, more research is needed before we know whether infants do in fact use body scaling in selecting a grip. Still, this work affirms that interactions between the infant, the environment, and the task are important even in early grasping and that changes in movement patterns related to changing structural constraints occur even in infancy. Is body scaling used at older ages? Do older children and adults use the ratio of hand size to object size in selecting a grip? Newell, Scully, Tenenbaum, and Hardiman (1989) observed 3- to 5-year-olds and adults. They found that a relatively constant ratio of hand size to object size determined when individuals chose to use two hands to pick up an object instead of one, no matter what their age. Thus, the ratio was consistent even though the adults had larger hands. The same has been found to be true in children 5, 7, and 9 years of age (van der Kamp, Savelsbergh, & Davis, 1998). The interaction of the individual’s structural constraints with environmental and task constraints, then, gives rise to either a one-hand or a two-hand grasp. Vision also plays a role in this type of task. From a young age on, we select the grip appropriate for the size, weight, and shape of the object to be obtained (figure 9.2). Butterworth and colleagues (1997) observed that infants often knocked an object before actually grasping it. In contrast, adults configure, or shape, their hands for a particular object before making contact with it. Adults also make a decision about whether to reach for an object with one hand or two before making contact with it. That is, visual information is used in preparation for the grasp. During childhood, individuals acquire the experience with objects needed to precisely configure the hand. Kuhtz-Buschbeck et al. (1998) noted that 6- and 7-year-olds were more dependent than adults on visual feedback during the reach to shape their hand for the grasp. Similarly, Pryde, Roy, and Campbell (1998) observed 9- and 10-year-olds slowing down more than adults at the end of a reach, presumably taking more time to use visual information for grasping the object. Older children, in the 5- to 10-year-old range, and adults appear to use visual information to anticipate the grasp and optimize accuracy (Smyth, Katamba, & Peacock, 2004; Smyth, Peacock, & Katamba, 2004). 323 Figure 9.2 The type of grip one uses to pick up an object depends in part on the size and shape of the object. An adult configures, or shapes, the hand for the object to be grasped before making contact. If indeed body scaling determines grip selection starting in childhood, grasping quickly becomes a well-practiced skill, with growing hand size and arm length taken into account in the body-scaled ratio. Grasping doesn’t need to be relearned with increases in growth. Thus, we might expect it to be a very stable skill over the life span. Only conditions such as arthritis or a loss of strength in old age would influence hand configuration. Indeed Carnahan, Vandervoort, and Swanson (1998) found that young adults (average age 26 years) and older adults (average age 70 years) accurately adapted the opening of the hand to grasp moving objects of different sizes. Of course, most manipulative tasks are not just a 324 matter of grasping an object but rather of bringing the hand to an object so that it can be grasped. We now examine the development of reaching over the life span. Think about your experience in grasping objects. Have you ever said, “This is heavier than it looks”? Or, “This is lighter than it looks”? What does this tell you about the role of vision in grasping? Web Study Guide Identify different grasping techniques during infancy in Lab Activity 9.1, Observing Grasping Development, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Reaching Infants make a transition during their first year from random arm movements to reaches that allow them to grasp objects. The questions we ask about the developmental processes that bring about this change in reaching are similar to those asked about early leg movements in chapter 6. In the case of reaching, what is it that drives the change from random and reflexive arm movements (prereaching) in the first few months after birth to eventual successful reaching of objects? Beginning with Piaget (1952), many developmentalists proposed that reaching and grasping required seeing both the object and the hand in the visual field so that vision and proprioception could be matched. Bruner (1973; Bruner & Koslowski, 1972) further suggested that infants build a system of visually guided arm movements from initial, poorly coordinated movements. Others considered reaching development to be a process of fine-tuning abilities that are already in place (Bower, Broughton, & Moore, 1970; Trevarthen, 1974, 1984; von Hofsten, 1982). It now seems, though, that there is not a continuous change from prereaching to reaching —that is, it seems that infants are not learning to match vision of the hand and arm with proprioception of the movement (von Hofsten, 1984). Infants are very good from the start at reaching in the dark when they cannot see their hand (Clifton, Muir, Ashmead, & Clarkson, 1993; McCarty & Ashmead, 1999). This is not to say that vision is not important to the task. Infants later rely on vision to refine the path of the reach and, as we said earlier, configure the hand to the object (figure 9.3). Rather, it might be the case that learning to reach is, more than anything else, a problem of learning to control the arm. 325 Figure 9.3 Displaced vision tasks demonstrate the role of vision in reaching. Prisms placed in front of the eyes shift the apparent location of an object from its real position. Individuals reach but grasp nothing because they reach directly for the location they identified by vision. They do not wait to see their hand in the visual field and then guide it to the location. Reprinted by permission from Hay 1990. Thelen et al. (1993) examined this issue by recording the arm movements of four infants longitudinally from 3 weeks to 1 year of age. Their decision to conduct a longitudinal study was important because some of their findings would have been masked in a cross-sectional study that averaged the movement of groups of infants. Thelen et al. observed that infants made the transition from prereaching to reaching at 3 to 4 months of age. The infants began with ballpark reaches—movements of exploration and discovery—but each infant found his or her own means of controlling reaches based on the movements he or she already used. Two of the infants preferred fast, oscillating movements that they had to dampen when they reached to get near a toy. The other two infants had to apply muscle force to their preferred slow movements to reach a toy. After several months of practice, the infants became quite good at reaching for a toy, but each addressed a different biomechanical problem and generated a different solution to make the transition from crude initial reaching attempts to consistently successful reaches. The period of improvement was characterized by times when reaches improved and then regressed before improving again. Key Point 326 To reach objects, infants learn to control their arms; they learn by doing. Thelen et al. believed their longitudinal study of infant reaching demonstrated that infants learn by doing. Rather than infants’ central nervous systems planning a trajectory of arm movement, one that would carry the hand to a toy, infants adjust the tension in the arms and apply muscle energy to get the hand close to the toy. By repeating the reaching, infants found increasingly efficient and consistent reaching patterns, yet they found these movement solutions individually, adapting their actions based on perceptions of their self- generated movements. Hand–Mouth Movements Another type of arm movement brings the hand, with or without an object, to the mouth. Between 3 and 4 months, infants become more consistent at bringing the hand to the mouth rather than to other parts of the face. At 5 months, they begin to open the mouth in anticipation of the hand’s arrival (Lew & Butterworth, 1997). The role of vision in these movements has not yet been studied, nor has the relationship between hand–mouth movements and reaches for objects in the same infants. Key Point Infants exhibit bimanual reaching during the first year but cannot perform complementary activities with two hands until the second year. Bimanual Reaching and Manipulation The reaches we’ve discussed thus far are unimanual, or one-arm, reaches, but infants also acquire bimanual reaching and grasping (Corbetta & Mounoud, 1990; Fagard, 1990). Skilled performers know to use two hands when grasping objects that are too large for one hand, and they can use one hand to complement the other. For example, they might use one hand to hold a container and the other to open the lid. Key Point Infants in their first year alternate between periods when unimanual reaches predominate and periods when bimanual reaches predominate. Newborns’ random arm movements are asymmetrical (Cobb, Goodwin, & Saelens, 1966). 327 The first bilateral movements—extending and raising the arms—are observed at approximately 2 months of age (White, Castle, & Held, 1964). Within a few months, infants can clasp their hands at the body midline. At approximately 4.5 months, infants often reach for objects with both arms (Fagard, 1990), but reaches begun with two hands usually result in one hand reaching and grasping the object first. Corbetta and Thelen (1996) studied the bimanual reaches of the infants described earlier (Thelen et al., 1993). During the first year, reaching fluctuated between periods of unimanual reaching and periods of bimanual reaching (figure 9.4). Infants tended to make synchronized nonreaching arm movements during periods of bimanual reaching but exhibited no pattern during periods of unimanual reaching. The four infants did not necessarily experience shifts from one kind of reaching to the other at the same ages. Manual activities might be influenced by postural control, yet it might also be the case that no single factor influences the predominance of unimanual or bimanual reaching. Rather, changing constraints can push infants to particular movement patterns. 328 Figure 9.4 Obtaining large objects necessitates bimanual reaching. In young infants, one hand might reach the object before the other. Infants older than 7 months reach either unimanually or bimanually, depending on the object’s characteristics. After 8 months, infants start to dissociate simultaneous arm activity so they can manipulate an object cooperatively with both hands (Goldfield & Michel, 1986; Ruff, 1984). Late in the first year, infants learn to hold two objects, one in each hand, and often bang them together (Ramsay, 1985). By 12 months, they can pull things apart and insert one object into another. Soon infants can reach for two objects with different arms simultaneously. Not until the end of the second year, however, can infants perform complementary activities with the hands, such as holding a lid open with one hand while withdrawing an object with the other (Bruner, 1970). Infants early in their second year can use objects as tools. Barrett, Davis, and Needham (2007) noted that infants with prior experience with a tool, such as a spoon, persisted in holding the handle of the spoon even when a task was better accomplished by holding the bowl. Infants could use a novel tool flexibly, though, and could be trained on what end of a tool to hold. Key Point Early in the second year, infants can use tools flexibly. The Role of Posture 329 Postural control is important in reaching. Consider that as adults we often lean forward or twist as we reach for an object. Infants typically sit independently by around 6 to 7 months. Before this, their trunks must be supported for them to achieve a successful reach. Reaching improves when infants are able to maintain postural control (Bertenthal & von Hofsten, 1998). Even at 4 months of age, infants adjust their posture as they reach, and improvements in these adjustments during the first year continue to facilitate reaching (Van der Fits & Hadders-Algra, 1998). One of the lab activities at the end of this chapter provides an opportunity for you to observe the type of grasp that a particular infant uses to pick up various objects. Manual Performance in Adulthood The ability to reach and grasp remains an important motor skill throughout the life span. Many careers involve manipulation, and in older adults the ability to perform some activities of daily living—such as bathing and dressing, preparing meals, and making phone calls—can dictate whether the individual is able to live independently. We discussed earlier some of the changes in individual constraints that accompany aging. It is easy to see that some of these might be a significant factor in the performance of large motor activities, but would they also affect manipulative skills? Would fine motor skills be better maintained over the life span than large motor skills? Let’s consider some of the research on manipulation in adulthood. Kauranen and Vanharanta (1996) conducted a cross-sectional study of men and women between 21 and 70 years of age. A test battery was administered that included reaction time, movement speed, tapping speed, and coordination of the hands and feet. Scores declined on all of the hand measures after age 50; the reaction, movement, and tapping times slowed, and coordination scores declined. What about manual performance at older ages? Hughes et al. (1997) observed older adults with an average age of 78 over a 6-year time span. Every 2 years, these adults completed the Timed Manual Performance Test and a grip strength measure. The Timed Manual Performance Test consists of 22 manipulative tests, 17 from the Williams board tests of manual ability and 5 from Jebsen’s test of hand skills. Each subject’s score consisted of the total time in seconds needed to complete the test. Generally, more individuals at older ages went over the time threshold on the performance test, and grip strength declined with advancing age. Declining manual performance was associated with loss of strength and upper joint impairment resulting from musculoskeletal disease. Also, in reaching for objects, older adults slow down more than young adults at the end of the reach, presumably to make more corrections in their trajectory (Roy, Winchester, Weir, & Black, 1993). Contreras-Vidal, Teulings, and Stelmach (1998) observed younger (20s) and older (60s and 330 70s) adults making handwriting movements. These movements, of course, do not demand speed, nor do they require great precision. Compared with the younger adults, the older adults could control force well but did not coordinate their finger and wrist movements as well. Loss of speed in movement with aging is a common finding for large and fine motor movements. Additionally, the research studies reviewed here indicate that movements might not be as finely coordinated with advancing age. We can see, however, that disuse and disease are every bit as important in the loss of manipulative skills as in the loss of locomotor or ballistic skills. Greater loss can be expected among those who curtail manipulative activities as they age, contributing in turn to a loss of strength, which may further hurt performance. In fact, compensatory strategies are adopted by those who continue activities over the life span. One example is that of older transcriptionists who type out documents from shorthand notes. It seems that older, experienced typists look further ahead than younger adults to give themselves more time to respond (Salthouse, 1984). It is clear that the interaction of individual, task, and environmental constraints is as important in fine, manipulative motor skills as it is in large motor skills. Over the age span, changing individual constraints bring changes in the interaction with environmental and task constraints, thus causing changes in movement. Key Point Some aspects of older adults’ reaches slow down, putting them at a disadvantage in making sequential movements, but accuracy of manipulation is stable, especially on well-known tasks. Think about the increased use of e-mail, which involves typing on a standard- size keyboard, and of text messaging, which involves typing on a very small keyboard. Do certain age groups prefer one or the other? Why? Is preference related to experience? Rapid Aiming Movements In some complex motor skills, participants make rapid aiming movements. Such arm movements involve an initiation and acceleration phase from the start of the movement to the point when peak velocity of the arm movement is reached, then a deceleration and termination phase from peak velocity to the end of the movement. 331 Young adults tend to make this movement symmetrically; that is, the acceleration and deceleration phases are equal. In contrast, older adults do not begin the movement as forcefully or travel as far in the acceleration phase. They tend to have a longer deceleration phase to compensate because they need more adjustments in the final phase, especially when the aiming movement needs to be very accurate (Vercruyssen, 1997). Rapid aiming movements are involved in tasks requiring monitoring and manipulation of complex displays such as cockpits. In critical tasks, many such movements can be required in sequence, and any slowing effects can accumulate. Age differences, then, might not be important in single, simple, or self-paced arm movements but may be critical when many sequential movements are needed in a short time. The interaction of individual and task constraints is evident in this type of skill. Practice is important to older adults. They can compensate for some slowing when they know the location of buttons or levers very well. 332 Catching Several manipulative skills are basic to sport performance. In these skills, a performer must gain possession or control of an object by reaching to intercept a moving object or stopping it with an implement. The most common manipulative skill is catching. Fielding in hockey also allows a player to control the ball or puck, such that it remains in the player’s control rather than bouncing or rolling away. Of these reception skills, we know the most about the development of catching. Baseball trivia buffs never tire of recounting great outfield catches. Perhaps the greatest was Willie Mays’ over-the-shoulder catch in the 1954 World Series. The score was tied 2-2, with two runners on base and no outs. Vic Wertz hit a long drive to right center field. Mays turned and ran full speed, his back to home plate. Just a few feet from the wall (which was particularly deep at New York’s Polo Grounds), he was able to stretch his arms and catch the ball, then make a tremendous throw back to the infield. The Cleveland Indians weren’t able to score that inning, and the New York Giants went on to win the game and sweep the series. Catching is relatively difficult as a developmental task. During early childhood we see children throw and kick, even if their movement patterns are not yet proficient. But if young children catch a ball, it often reflects the skill of the thrower in getting the ball to arrive in outstretched arms. It is the interception aspect of catching that makes it difficult. With this in mind, let’s consider how catching develops and then examine interception in catching. The goal of catching is to retain possession of the object you catch. It is better to catch an object in the hands than to trap it against the body or opposite arm because if the object is caught in the hands, the catcher can quickly manipulate it—usually by throwing it. A child’s initial catching attempts involve little force absorption. The young child pictured in figure 9.5 has positioned his hands and arms rigidly. Instead of catching the ball in his hands, he traps it against his chest. It is common to see children turn away and close their eyes in anticipation of the ball’s arrival. The next section discusses characteristics of proficient catching and then examines how children typically develop proficient catching. 333 Figure 9.5 Beginning catching. This young boy holds his arms and hands rigidly rather than giving with the arrival of the ball to absorb its force gradually. © Mary Ann Roberton. Proficient Catching In moving from novice to proficient catching skills, as shown in figure 9.6, a child must learn to catch with the hands and give with the ball, thus gradually absorbing the ball’s force; master the ability to move to the left or the right, or forward or back, to intercept the ball; and point the fingers up when catching a high ball and down when catching a low one. 334 Figure 9.6 Proficient catching. The ball is caught with the hands, and the hands and arms give with the ball. Developmental Changes in Catching It is more difficult to identify developmental sequences for catching skills than for most locomotor or ballistic skills because the sequence is specific to the conditions under which the individual performs the skill. Many factors are variable in catching—for example, the ball’s size, shape (e.g., a round basketball vs. a football), speed, trajectory, and arrival point. Haubenstricker, Branta, and Seefeldt (1983) conducted a preliminary validation of a developmental sequence for arm action in two-hand catching. They used progressively smaller balls as children demonstrated better skill. Table 9.1 summarizes the sequence, which was originally outlined by Seefeldt, Reuschlein, and Vogel (1972). At 8 years of age, most of the boys and almost half of the girls tested were at the highest level of arm action. Virtually all of the children had passed through steps 1 and 2 by this time. Slightly higher percentages of boys than girls performed at higher levels at any given age, but overall this group demonstrated well-developed arm action by age 8. Table 9.1 also suggests the key observation points that can help you place performers at a developmental level. Key Point Catching is specific to environmental and task constraints. 335 Strohmeyer, Williams, and Schaub-George (1991) proposed developmental sequences for the hands and body in catching a small ball (table 9.1). A unique feature of this work is that it is based on catching balls thrown directly to the catcher as well as balls thrown high or to the side of the catcher. These sequences suggest that as catchers improve, they are better able to move their bodies in response to the oncoming ball, adjust their hands to the anticipated location of the catch, and catch the ball in their hands. The investigators tested their sequences on a cross section of children between 5 and 12 years old. All of the children over 8 years old made some adjustment in body position in response to the oncoming ball, and 11- to 12-year-olds successfully adjusted their body positions about 80% of the time. In contrast, this older group could properly adjust their hand positions in response to the ball only 40% of the time if the ball was thrown directly to them and less than 10% of the time if it was thrown to various positions around them. Think of your own skill level in catching. What kinds of catching tasks do you find easy? Is there a catching task you find difficult? 336 Catching, like striking, involves anticipating where a ball can be intercepted as well as the ability to complete the movements that position the hands at that location. As we would expect, children better predict the ball flight as they get older, especially when the viewing time (path of the ball) is short (Lefebvre & Reid, 1998). The anticipatory aspects of manipulative skills are discussed in more detail elsewhere in this chapter. Observing Catching Patterns Catching can be observed from the front, allowing you to toss the ball, or from the side. It is easy to assess the product in catching tasks. One can simply record a percentage of balls successfully caught, noting the task constraints, including the size and type of ball used, the throwing distance, and the trajectory of the ball. Key Point To assess catching skill, environmental and task constraints such as ball size and ball trajectory must be tracked and replicated. Parents, teachers, and coaches often want to know about the movement process used in catching. Figure 9.7 “Observation Plan for Catching” provides a suggested developmental sequence that indicates which step the catcher demonstrates for each body component. For example, if you observe a child who extends her arms, palms up, and scoops a large ball thrown to her, trapping it against her chest, all without moving her feet, the developmental levels would be step 3 for arm action, step 1 for hand action, and step 1 for body action. 337 Illustrations © Mary Ann Roberton. Web Study Guide Identify developmental differences among catchers in Lab Activity 9.2, Assessing the Developmental Levels of Catchers, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 338 Anticipation It is clear that many manipulative tasks and interception skills involve anticipation. The ball or other moving object can approach at different speeds, from different directions, and along different trajectories and may be of varying size and shape. To be successful, performers must initiate movements well ahead of interception so that the body and hands (or implement, such as a hockey stick) can be in the proper position when the object arrives. In fact, the manipulative component of reception skills (e.g., positioning and closing the hands on the ball) is often perfected before an individual develops the ability to be in the right place at the right time. Some developmentalists have researched this aspect of reception skills through the use of coincidence-anticipation tasks. With this approach, it is easy to vary task characteristics and observe the effect on performance. Variations in task characteristics influence not only the product of performance—a hit or catch versus a miss—but also the process, or movement pattern, used in the task. For example, children who are capable of catching small balls in their hands may choose to scoop very large balls with their arms, perhaps as a surer means of retaining them (Victors, 1961). Thus, a task can be defined as requiring a simpler or more complex movement response, and the characteristics of the ball can be varied to further constrain the movement. Yet many research studies on coincidence anticipation are conducted in a laboratory setting with an apparatus that allows factors such as ball speed, trajectory, and direction to be varied and are not very similar to the real-world task of catching a ball. Therefore, it is important to recognize that these studies might tell us more about the perceptual limits of performers for a specified interception task than for real-world catching. Coincidence-anticipation tasks are motor skills in which one anticipates the completion of a movement to coincide with the arrival of a moving object. Let’s consider some of the task constraints in coincidence-anticipation tasks. Several researchers have found that coincidence-anticipation performance improves throughout childhood and adolescence (Bard, Fleury, Carriere, & Bellec, 1981; Dorfman, 1977; Dunham, 1977; Haywood, 1977, 1980; Lefebvre & Reid, 1998; Stadulis, 1971; Thomas, Gallagher, & Purvis, 1981). However, the exact pattern of improvement with advancing age depends on task constraints: Young children are less accurate as the movement required of them gets more complex (Bard et al., 1981; Haywood, 1977). So, response complexity is one task characteristic that influences how well children perform on interception tasks. Children’s accuracy decreases if the interception point is farther away. For example, McConnell and Wade (1990) found that the number of successful catches and the efficiency of the movement pattern used decreased if children 6 to 11 years old had to 339 move 2 ft instead of 1 ft, left or right, to make a catch. Young children are more successful at intercepting large balls than small balls (Isaacs, 1980; McCaskill & Wellman, 1938; Payne, 1982; Payne & Koslow, 1981). A high trajectory also makes interception more difficult for young children because the ball changes location in both horizontal and vertical directions (DuRandt, 1985). Some ball color and background combinations influence young children’s performance. Morris (1976) determined that 7-year-olds could better catch blue balls moving against a white background than white balls against a white background. The effect of color diminished with advancing age. The speed of the moving object affects coincidence-anticipation accuracy but not in a clear pattern. A faster speed makes interception more difficult, especially when the object’s flight is short. But researchers often note that children are inaccurate with slow velocities because they respond too early (Bard et al., 1981; Haywood, 1977; Haywood, Greenwald, & Lewis, 1981; Isaacs, 1983; Wade, 1980). Perhaps children prepare for the fastest speed an object might travel and then have difficulty delaying their responses if the speed is slow (Bard, Fleury, & Gagnon, 1990). Also, the preceding speeds might influence young children more than they do older performers. If the previous moving object came quickly, young children judge the next object to be moving faster than it really is (Haywood et al., 1981), just like baseball batters are fooled by a pitcher’s “change up” or slow pitch. Educators should be aware, then, that children can have difficulty adjusting their responses when the speed of an object in an interception task varies greatly from one repetition to the next. This is particularly true if the object’s flight is short or the response required is complex. Imagine you’re an elementary school physical education instructor. Recall the sports that involve moving objects, then identify the many ways in which pitchers and servers in various sports change the pitch or serve to make interception of the ball more difficult. In contrast, how would you throw a ball to small children to increase the likelihood that they will catch it? Key Point Interception success is often related to ball size, speed, trajectory, and other task and environmental constraints. What underlies these age-related trends in coincidence anticipation? Early studies of these skills took an information processing perspective. That is, performers were thought to receive visual and kinesthetic information and perform “calculations” on that data, much like a computer, to project the future location of the moving object in order to intercept it. 340 The perception–action perspective, in contrast, holds that all the needed information is in the environment and that no calculations are necessary. Meaningful information in the environment specifies the action or movement possibilities of that environment and for specific events. This relationship is called an affordance. For catching, two important characteristics of the person–environment system concern constant patterns of change, called invariants, and the expanding optic array. The optic array refers to the visual picture falling on our retinas as we approach an object or as a moving object approaches us. That picture expands in size on the retinas with approach and constricts with retreat. An affordance is an action or behavior provided or permitted for an actor by the places, objects, and events in an environment; it is often related to the relative sizes of the actor and the objects. Invariance is stability in the kinematic values of a set of movements (i.e., keeping patterns in the environment constant). An optic array consists of the light waves reverberating from surfaces in the environment— in other words, the stimulus for visual perception. When movement of environmental objects or of the viewer in the environment occurs, the optic array expands when the movement is “toward” and constricts when the movement is “away.” From the perception–action perspective, it is possible that we use the rate of expansion of this image on our retinas to know when arrival or collision will occur (Lyons, Fontaine, & Elliott, 1997). Insects that intercept prey and birds that fold their wings back before diving into water demonstrate perfect timing in these interceptions, and, given their small brains, it is more likely that they perceive aspects of the environment directly than that they perform complex calculations to predict arrival times. Van Hof, van der Kamp, and Savelsbergh (2008) investigated whether infants between 3 and 9 months could act on an approaching ball. They placed a ball on a mechanical apparatus so that the ball came directly over the right shoulder of a seated infant at various speeds. Infants aged 3 to 5 months often did not even reach for the balls and were not very accurate when they did. Individual differences occurred in age of improvement, but by 8 to 9 months the infants were relatively accurate. They also were more likely not to attempt to catch fast-moving balls that they were unlikely to intercept. Key Point To reach objects, the expansion rate of the image of a directly approaching ball on the retina could be used to time an interception. Several research teams have recently used the perception–action perspective to study the “real world” task of catching a ball projected in a high trajectory, much like the task faced by an outfielder in baseball. Such researchers are now able to track the position of both the ball and the catcher by using video technology. McLeod and Dienes (1993, 1996) 341 demonstrated that catchers could intercept a directly approaching high-trajectory ball by keeping a ratio, based on the angle of gaze, at or near zero. (For those with a mathematical background, the ratio is the second derivative of the tangent of the angle of gaze.) If the ratio’s value is positive, the ball will land behind the catcher; if it is negative, the ball will land in front. By keeping the ratio near zero, the catcher knows whether to move forward or backward and how quickly to move (figure 9.8). Oudejans and colleagues (Michaels & Oudejans, 1992; Oudejans, Michaels, Bakker, & Dolne, 1996) similarly demonstrated that catchers could keep the vertical optical acceleration of the ball close to zero. Unlike McLeod and Dienes’ notion that the catcher focuses on angle of gaze, in this approach the catcher focuses on the ball’s acceleration in the vertical plane as the catcher views the ball. Both approaches tell a catcher whether to move forward or backward. 342 Figure 9.8 The catcher might intercept the approaching ball by keeping a ratio, based on the angle of gaze, at or near zero. When the ball and catcher are in position 1, this ratio is a negative number. The catcher must move forward to bring the ratio closer to zero. At position 2 the ratio is closer to zero but still not zero. The catcher continues to move until the ratio is zero, and both the catcher and the ball arrive at position 3. Adapted by permission from McLeod and Dienes 1996. Of course, many catches require sideways movement. A strategy proposed for this situation is keeping the lateral position of the ball constant with respect to the catcher. This is called the constant bearing angle strategy (figure 9.9) (Lenoir, Musch, Janssens, Thiery, & Uyttenhove, 1999). For example, a soccer goalie could keep this angle constant by moving sideways to intercept a ball. McBeath and colleagues (McBeath, Shaffer, & Kaiser, 1995; Shaffer, 1999; Shaffer & McBeath, 2002) identified a relationship that incorporates both elevation of gaze and horizontal angle of gaze, as would be important for catching a high- trajectory ball projected to the side of a catcher. If this relationship is kept constant, a catcher can arrive at the correct place to catch the ball. The specific mathematics of the relationship are not important here; what matters for us is that we can see that invariant relationships are available to catchers in the environment and that it is possible to arrive at the correct place without our brains calculating a landing point based on the early part of the trajectory. Practically, a fielder (as long as he or she can move fast enough) can adopt the unconscious strategy of continuously moving to stay under the balls’ trajectory as viewed. If the ball’s path appears to arc up and past the catcher, the ball will land behind the catcher. If the ball’s path appears to arc down, the ball will land in front. So, the catcher can adjust to maintain the proper visual appearance and adapt to changes in the path due to ball spin, air resistance, or wind in order to arrive at the right place. Key Point Catchers are able to intercept balls by keeping certain relationships between themselves and the ball constant. 343 Imagine you are a baseball or softball coach. Keeping in mind the perception– action approach, how might you help a young fielder who is having trouble catching fly balls? Key Point Children learn to catch fly balls from their different experiences—a catch when one relationship with the ball was seen, but a miss when any other relationship was seen. 344 Figure 9.9 A catcher moves to the side to intercept a ball by keeping the bearing angle formed by the dashed line constant. Redrawn from Lenoir et al. 1999. How Do Children Learn to Arrive at the Right Place? From an information processing viewpoint, children must learn to make more precise calculations to become proficient catchers. Errors made in early attempts serve as informative feedback that can be used to refine the calculation process. From a perception– action viewpoint, however, what children need is to subconsciously discover an invariant. When children begin catching by standing still, for example, McLeod and Dienes’ ratio is zero for balls that land in their arms and something else for balls that do not land in their arms. With sufficient exposure, children discover the relationship between the ratio and the “catchability” of a ball and eventually use the relationship when they begin moving for a ball. From the perception–action perspective, then, one role of parents, teachers, and coaches is to help children discover the various sources of perceptual information that constrain movement in interception tasks. They can do so by manipulating informational constraints during the exploratory process of practice. Bennett, Button, Kingsbury, and Davids (1999) recently demonstrated that 9- and 10-year-olds who were asked to practice one-hand catching with a restricted view of the ball later benefited when learning a catching task under new conditions. Thus, highlighting the useful sources of information by varying task constraints during practice can be helpful. Some attempts to improve anticipatory sports skills with training in novice adults have been ineffective (Wood & Abernethy, 1997). Abernethy, Wood, and Parks (1999) suggest that training must be sport specific (environment and task specific) and must focus on the factors known to limit novice performance. They demonstrated that novice adults can benefit from such training; the novice adults performed a laboratory task similarly to experts after training. It is important to identify the perceptual information that constrains movement. This research suggests that whether the individual is a child or a novice adult, manipulating constraints to help the performer identify the important information in the environment subconsciously facilitates the movements that result in success. More information, however, is needed about the relative merits of simple exploratory practice and 345 instruction. Imagine you are a teacher and design an activity that helps children learn to catch fly balls. Catching in Older Adulthood Little research-based information is available about catching by older adults. We might suspect that experienced older adults know the invariant patterns that provide information about intercepting balls. Factors that might change, however, could include the quickness with which movement is initiated, the maximum speed that could be achieved in moving to the ball, and the extent of reach if the “catchability” of a given ball were at the limit for an individual’s speed in moving. All of these factors might contribute to an older adult being unable to catch as many balls as a younger adult could. Coincidence-anticipation research provides some information about the anticipatory aspects of skills such as catching. Older adults are somewhat less accurate and more variable in their performance than younger performers, and the differences are greater when the moving object moves faster and when the older adults are sedentary rather than active (Haywood, 1980, 1982; Wiegand & Ramella, 1983). Wiegand and Ramella (1983) observed that older adults improved with practice at the same rate as younger adults. Over a 7-year period, from an average age of 66.9 years to an average age of 73.5 years, active adults demonstrated improvement in performance on a coincidence-anticipation task (Haywood, 1989). Thus, repetition of such skills probably is important for maintaining skill. The task constraints on the movement response in the coincidence-anticipation tasks, however, were minimal. When task constraints are such that larger, more complex movements or movements over distance in a short time are required, a higher number of older adults might be less successful at these tasks given their individual constraints. Driving and Piloting Although only a portion of older adults participate in sports involving interception, a large number drive automobiles. In fact, the issue of whether an older adult should continue driving is often an emotional one because driving often represents a considerable degree of independence and freedom. Driving is a complex perceptual-motor skill involving manipulation. Skillful driving depends on vision (and sometimes audition, or hearing), attentional focus, experience, speed, and coordination, all under occasionally stressful conditions. Older adults have more difficulty than younger adults in dividing their attention and 346 performing two tasks at once in driving situations (Brouwer, Waterink, Van Wolffelaar, & Rothengartter, 1991; Ponds, Brouwer, & Van Wolffelaar, 1988). Older adults also take longer to plan movements and are slower in executing movements, especially when speedy movement is needed (Goggin & Stelmach, 1990; Olson & Sivak, 1986). Goggin and Keller (1996) examined whether aging differentially affects the sensory-cognitive or motor functions in driving. They had older adult drivers take a written test about a videotape of 15 driving situations. The older drivers also made driving responses to the same videotaped situations on a driving simulator. Goggin and Keller reasoned that if older adults had difficulty only on the written test, aging likely affected sensory-cognitive functions, but if they had difficulty only with the simulator responses, aging likely affected motor functions. The adults performed less well on the written test and better on the simulator. Thus, sensory-cognitive factors such as attention and decision making might be more significant factors in poor performance of driving-related motor skills. Think about the older adults in your family. How are their driving skills? Do any of them compensate for a loss of driving skill? How? The effects of aging on airplane piloting performance have also been studied (see Morrow & Leirer, 1997, for a review). Mandatory retirement ages for commercial pilots are also an emotional issue for those involved. Like driving, piloting is affected more as task complexity increases. Perceptual aspects of piloting, attention, and working memory are particularly affected by aging. Expertise on familiar tasks, however, offsets the effects of aging, and highly practiced skills are well maintained. Applying the model of constraints, we can see that an increased number of constraints on a task adds to its complexity, and when individual constraints change with aging, the interaction of constraints can quickly cause the difficulty of driving and piloting tasks to reach a critical point. As mentioned earlier in regard to rapid aiming tasks, experience with a set of environmental and task constraints allows older adults to compensate for slowing of manipulative movements. Thus, continued practice with tasks, whether sport or driving tasks, is important for maintaining skill. Eventually, however, decrements in sensory- cognitive systems, as well as in speed of movement, lead to a loss of skill. 347 Summary and Synthesis Manipulative skills set humans apart from other species. Whether executing sports skills or tasks of everyday living, people need to reach, grasp, and maneuver objects. Infants become skilled at reaching and grasping early in life, during the first year, although using the two hands in complementary ways comes a little later. Children can become accomplished catchers by 11 or 12 years of age, but in all catching tasks at any age, the farther the catcher must travel, the more difficult the catch. Aging probably affects a catcher’s ability to get to a ball more than the ability to know where to be in order to catch the ball. Changing structural constraints influence the speed with which manipulative and locomotor movements can be initiated and completed. When tasks demand great speed, older adults are disadvantaged compared with younger adults. Children need practice in order to learn, even if subconsciously, the information available in the environment that is important for catching success. Adults need practice in order to maintain their skills, especially in demanding conditions. Thus, at any age, a person’s skill in completing challenging manipulative tasks often reflects experience and practice in dealing with the individual, task, and environmental constraints involved. Reinforcing What You Have Learned About Constraints Take a Second Look As the case of Matthew Scott teaches us, being able to manipulate objects with two hands is important in activities of daily living and many activities that are uniquely human and enjoyable for us. When faced with the loss of manipulative skills, it is worth extraordinary effort to restore the ability to perform those skills. The development of manipulative skills certainly emphasizes the importance of task and environmental constraints in performance. As Matthew Scott undertook the journey to use his transplanted hand, he was challenged by environmental and task constraints that many of us take for granted. With continued therapy, he has been able to use his new hand while dealing with an increasing range of environmental and task constraints. Manipulative skills are among the fundamental motor skills. The perception–action perspective in particular holds that the environment provides individuals with much of the information they need in order to intercept objects. Thus, manipulative skills such as catching do not improve just because individual constraints change. The changing interaction of individual constraints with task and environmental constraints is an important aspect of the development of manipulative skills. This interaction is equally important in the maintenance of manipulative skills in older adulthood. Experience with 348 numerous interactions of our existing individual constraints and environmental and task constraints gives us the ability, eventually, to successfully manipulate objects even when we have not previously seen a particular ball trajectory and speed—or used a particular keyboard. Test Your Knowledge 1. How does the size of an object affect the grip that an infant uses? How might this factor influence where an infant falls on Halverson’s prehension sequence? How does the shape of an object affect the grip used? 2. Do infants learn to reach for objects by better matching their hand position to the seen location of the object, or by better controlling their arms? Explain why you chose your answer. 3. How does manipulative skill change in older adulthood, and how can older adults adapt to these changes? 4. What are the major developmental trends we see in children as they become increasingly proficient in catching? 5. When balls do not come directly to the catcher, what situations (environmental and task constraints) make catching success difficult for children? For adults? 6. Explain, from the information processing perspective and the perception–action perspective, how children learn to go to the proper place to catch a ball not traveling directly toward them. 7. What changing individual structural constraints might affect an older adult’s skill in driving or piloting? 8. Think about the term coincidence anticipation. Explain why someone who prefers the perception–action approach might consider this to be a misnomer for interception skills. Learning Exercise 9.1 Investigating Infant Reaching Place an infant between 6 and 12 months of age in an upright sitting position in front of a table or tray. One at a time, place six small objects in front of the infant; the objects should vary in size, weight, and shape (all should be small enough for an infant to pick up with one hand). Consult figure 9.1 and note the type of grip the infant uses for each object. Be careful not to let the infant put small objects in his or her mouth because they can pose a choking hazard. Next, repeat the process to see whether the infant uses the same grip on each object as in the first round. Finally, prepare a report on the different grips you observed for the various objects; be sure to distinguish between power grips and precision grips. Discuss whether or how the grips changed as the weight or shape of the object changed. 349 Part IV Perceptual-Motor Development Infants and toddlers experience dramatic changes in perception. Infants learn, for example, the names and locations of their body parts as well as the relationships between objects, such as “in front of” or “behind.” Such changes play a large part in the cognitive and physical skills that infants and toddlers can execute. There is no doubt that the perceptual systems, as individual structural constraints, interact with the task and environment to give rise to movement; in fact, the interactions between the perceptual systems and the environment are very rich. The study of perception and its relationship to movement, or action, has been at least as controversial as any aspect of motor development. Certainly, professionals have adopted varying perspectives on the role of perception in motor development. Perhaps the most recent and significant debate involves the notion that movement drives perceptual development as much as perceptual development permits new movements. On the horizon, too, is a research frontier concerned with movement and exercise facilitating growth in the neurologic system over the life span. Thinking about both of these areas of research, we can see that the model of constraints is useful because it highlights the interaction of individual, environment, and task in giving rise to movement. We begin our discussion by reviewing the age-related changes in sensation and perception in the vision, kinesthetic, and audition systems in chapter 10. Also discussed is the development of integration between and among the sensory-perceptual systems. Then in chapter 11 we examine how the perception and action are linked with special emphasis on how postural control and balance highlight that link. 350 Suggested Reading Dent-Read, C., & Zukow-Goldring, P. (Eds.). (1997). Evolving explanations of development: Ecological approaches to organism–environment systems. Washington, DC: American Psychological Association. Gottlieb, G., & Krasnegor, N.A. (Eds.). (1985). Measurement of audition and vision in the first year of postnatal life: A methodological overview. Norwood, NJ: Ablex. Kellman, P.J., & Arterberry, M.E. (1998). The cradle of knowledge: Development of perception in infancy. Cambridge, MA: MIT Press. Konczak, J. (1990). Toward an ecological theory of motor development: The relevance of the Gibsonian approach to vision for motor development research. In J.E. Clark & J.H. Humphrey (Eds.), Advances in motor development research (Vol. 3, pp. 201–224). New York: AMS Press. Lynch, A., & Getchell, N. (2010). Using an ecological approach to understand perception, cognition, and action coupling in individuals with Autism Spectrum Disorder. International Public Health Journal, 2(1), 7–16. Ratey, J.J. (2008). Spark: The revolutionary new science of exercise and the brain. New York: Little, Brown. 351 Chapter 10 Sensory-Perceptual Development 352 Chapter Objectives This chapter reviews developmental changes in the vision, audition, and kinesthetic systems; discusses changes in visual, auditory, and kinesthetic sensation that occur with aging; traces the development of visual perception—in particular, perception of space, objects, and motion; provides an overview of the development of kinesthetic perception, especially perception of tactile location, the body, limb movements, spatial orientation, and direction; describes the development of auditory perception; and studies the process whereby environmental objects and events perceived in different modalities are perceived as the same object or event. 353 Motor Development in the Real World Your Environment Most of us have had the opportunity to see an IMAX movie on a large surround screen. In recent years, there has been an upsurge in the number of three-dimensional IMAX theaters. With big-budget movies such as Life of Pi, The Hobbit trilogy, and The Hunger Games: Catching Fire, Hollywood filmmakers have embraced the IMAX experience. Many of these movies include the view seen as if flying from the cockpit of an airplane. The visual information is so rich that oftentimes people who tend to get airsick or seasick cannot watch such movies without getting sick. These movies serve as a reminder that sensory and perceptual information—especially visual information—is an important part of our experience in, and interaction with, our environment. We exist only in an environment. How we operate in that environment is a function of how we sense and perceive it. In many ways, almost every motor act can be considered a perceptual-motor skill. Human movement is based on information about the environment and one’s position or location in it. For example, a softball infielder sees the location of the pitch, the batter striking the ball, and the ball bouncing on the ground; hears the hit and perhaps sees a runner on the base path; and feels the position of her body and arms. The infielder uses this information to decide where and when she can intercept the ball, where to move, and how to position her body. It might seem that not all movements are so dependent on sensing and perceiving the environment. Yet even an experienced platform diver, blindfolded and wearing earplugs, must feel how gravity pulls his body and know where his trunk and limbs are relative to one another to execute his dive. As the motion sickness effect of a three-dimensional movie demonstrates, sensory information and perceptual information are highly integrated. We typically experience events in multiple sensory systems. We are uncomfortable if the information from one sense contradicts the information from another—we stagger, fall, or feel sick. If we are denied information from one sense we can compensate by attending to information from another, but we might not be as accurate in our perceptions. Moreover, our sensory- perceptual selves and the environment are interactive systems. We do not simply receive information from the environment; rather, we act to obtain information. For example, we turn our ears toward a sound or reach to feel the texture of a surface. Thus, we must keep in mind the highly integrative nature of sensation, perception, and movement even as we discuss individual systems or types of perceptual discrimination. Sensation is the neural activity triggered by a stimulus that activates a sensory receptor and 354 results in sensory nerve impulses traveling the sensory nerve pathways to the brain. Perception is a multistage process that takes place in the brain and includes selecting, processing, organizing, and integrating information received from the senses. Individuals with normally functioning sensory receptors can attach different meanings to the same stimulus, and even the same individual can interpret a single stimulus in different ways. You might remember seeing in a general psychology class some visual displays that have this effect. Remember the one that can be seen as two facial profiles or as a vase (figure 10.1)? Thus, perception is the process whereby we attach meaning to sensory stimuli. How individuals interpret sensory stimuli is the fascinating topic of perceptual development. For individuals to move or act in an environment, they must perceive that environment. In fact, some views of perception and action see them as so interactive as to be inseparable. The environment influences what movements are possible or efficient, and moving through the environment informs us about the nature of the environment and our interactions with it. No study of motor development is complete without the study of the relationship between perception and action. 355 Figure 10.1 This drawing can be seen as two facing facial profiles or as a vase. Reprinted, by permission, from G.H. Sage, 1984, Motor learning and control: A neuropsychological approach (Dubuque, IA: Brown), 111. ©The McGraw-Hill Companies. The sensory-perceptual systems are, of course, individual structural constraints to movement and to other activities such as reading. Earlier chapters discussed the development of many of the structural systems, such as the skeletal and muscular systems. This chapter discusses the development of visual, auditory, and kinesthetic sensation and perception. 356 Visual Development Vision plays a major role in most skill performance. To better understand this role, we need to examine age-related changes in visual sensation and visual perception (figure 10.2). 357 Figure 10.2 The human eye. An axial length that is too short or too long results in farsightedness or nearsightedness, respectively. An imperfect curvature of the cornea also causes blurred vision, a condition known as astigmatism. Visual Sensation Several aspects of vision determine how clearly one can see objects. Here, we confine our discussion to visual acuity. During the first month of life, the visual system provides the infant with functionally useful but unrefined vision at a level approximately 5% of eventual adult acuity, or 20/400 on the Snellen scale of visual acuity (20/20 is desirable) (figure 10.3). The newborn’s resolution of detail is such that she can differentiate facial features from a distance of 20 in.; beyond this, she probably cannot see objects clearly (Kellman & Arterberry, 1998). 358 Figure 10.3 A Snellen chart. Sharpness of sight is measured by whether observers can distinguish letters that differ only by whether a small gap is filled, such as F and P, or C and O. Children must know letters to be measured in this manner. Alternative methods are available for testing infants and toddlers. Acuity is sharpness of sight. At about 6 months of age, as infants’ motor systems are ready to begin self-propelled locomotion, their visual systems perceive adequate detail to assist them in the task. From the ecological perspective, vision is another system that must develop to an adequate level to facilitate locomotion. Visual sensation continues to improve during childhood. Five-year-olds have visual acuity of about 20/30, and by age 10 children without a visual anomaly score at the desired level of 20/20. It is likely that visual experience is necessary for the development of vision because deprivation of vision during development is known to induce refractive errors in animals (Atkinson & Braddick, 1981). As a person ages, changes in the visual system occur naturally, and some conditions and diseases become more prevalent, especially in older adults. These changes may affect the quality of the visual information that reaches the central nervous system and may have implications for the performance of skills as well as tasks of everyday living. 359 Key Point Vision reaches adult levels around 10 years of age, but any refractive errors resulting from imperfections in the axial length of the eye can be corrected with glasses or contacts. For example, the condition termed presbyopia (from presbys for “old man” and ops for “eye”) becomes clinically significant at around age 40. It affects the ability to see nearby images clearly. The resting diameter of the pupil also decreases with aging, typically reducing retinal illuminance (the amount of light reaching the retina) in a 60-year-old to one-third of that in a young adult. The lens also yellows with age, further reducing the amount of illuminance reaching the eye and making glare a problem for older adults. Visual disturbances that are more prevalent in older adults include the following: Cataracts Glaucoma Age-related maculopathy Presbyopia is the gradual loss of accommodation power to focus on near objects. It accompanies advancing age. Age-related maculopathy is a disease affecting the central area of the retina that provides detailed vision. People who work with children or older adults can look for certain signs that may indicate a visual problem. These include squinting, under- or overreaching for objects, and performing unusual head movements to align one’s gaze with a particular object. Activity leaders should make sure that activity areas are well lit but without glare, and they should encourage performers to wear any corrective lenses prescribed for them (Haywood & Trick, 1990). Because vision provides so much of the perceptual information that people need in order to perform skills successfully, efforts to enhance the visual information that the central nervous system receives should also enhance visual perception and thus skill performance. An excellent way to remind ourselves of how much we rely on sensory information is to close our eyes while trying to perform a routine activity. Visual Perception People depend heavily on visual perception in the performance of most skills. The 360 development of visual perception is a topic for texts in and of itself, so only major aspects of visual perception are highlighted here. Perception of Space One of the fundamental perceptions is that of three-dimensional space. Almost all movements—reaching and grasping, locomotion, and complex skills such as driving a car or piloting a plane—depend on a perception of three-dimensional space. Visual sensations are received by sensory receptors in the retina in approximately a two-dimensional format. So how do people interpret the world in three dimensions? To perceive space in three dimensions, individuals must perceive depth and distance. The visual system has numerous sources of information about distance and depth perception. One source is retinal disparity. Because an individual’s two eyes are in different locations, each eye sees the visual field from a slightly different angle (figure 10.4). The information needed for judging depth comes from a comparison of the two slightly different pictures. Depth perception is aided by good visual acuity because a sharper picture from each eye provides more information for the comparison. 361 Figure 10.4 Retinal disparity. The images on the left retina are closer together than the images on the right retina. The observer sees the two rods in depth. Reprinted by permission from by Kaufman 1979. Depth perception is a person’s judgment of the distance from self to an object or place in space. Retinal disparity is the difference in the images received by the two eyes as a result of their different locations. Viewers have other sources of information about depth. By moving the head or moving through space, they receive depth cues from motion parallax. Objects in space change locations on our retinas, and nearer objects overlap more distant objects as the head moves. Gibson (1966) suggested that this transformation of the optical array, which he called optic flow, provides much information about the three-dimensional nature of our environment. This direct means of perceiving the environment likely guides locomotion, controls posture, and helps us anticipate contact with objects and surfaces (Crowell & Banks, 1993; Johansson, von Hofsten, & Jansson, 1980; Warren & Wertheim, 1990). Motion parallax is the change in optical location for objects at different distances during viewer motion. Optic flow is change in the pattern of optical texture, a transformation of the optic array, 362 as a viewer moves forward or backward in a stable environment. Viewers with experience in the world also use an assumption of physical equality to judge depth. That is, when two like objects can be expected to have the same size but project different relative sizes on the retina, we assume that the object with the larger retinal size is closer to us. Similarly, if we look down a roadway, we assume it is the same width across even though the curbs appear to get closer to one another (the curbs form converging lines). If you were a physical education teacher, what types of activities might you teach that would require depth perception? Infants have functional vision and, therefore, the mechanics for retinal disparity and motion parallax as sources of depth perception. From about 1 month of age, infants blink more often when shown a display that appears to be approaching than when shown a display that does not (Nanez & Yonas, 1994), demonstrating that they perceive the object to be moving toward them, not merely increasing in size. In the well-known visual cliff experiments of Walk and Gibson (1961; Gibson & Walk, 1960; Walk, 1969), infants between 6 and 14 months of age, placed on one side of an apparent drop-off (a piece of glass over the drop-off prevented it from being a real cliff), stopped at the edge even though their mothers beckoned them from the other side. These studies demonstrate that even young infants have some level of depth perception. Yet, children may err in judging depth until near-adult levels are reached in early adolescence (Williams, 1968). Key Point Cues about depth and distance in our environment are often derived from the two eyes being in different locations or from movement of the head. Behavioral experiments on depth perception are consistent with work on maturation of the visual cortex of the cerebrum. At birth, the cells in layer four of the cortex receive neural input from both eyes. By 6 months of age, these neural inputs separate into alternating columns, receiving input from the right and left eyes, respectively (Held, 1985, 1988; Hickey & Peduzzi, 1987). Disparity information would depend on knowing which eye is sending what information, so this aspect of neurological maturation might well be crucial in the onset of depth perception through retinal disparity. More older adults than younger adults fail depth perception tests, but thresholds for distinguishing depth change little if at all (Wright & Wormald, 1992; Yekta, Pickwell, & Jenkins, 1989). Higher failure rates probably reflect an increase with advancing age in the 363 number of viewers with visual problems. Although perception of space is an important aspect of visual perception, our environment also includes objects. Thus, it is equally important to perceive objects, their attributes, and their relationships to oneself and to others. Perception of Objects Among the important attributes of objects are size, shape, and motion. The concept of “object” is relative. An airplane pilot might consider a runway to be an object, whereas a person standing on the runway considers it a surface. With growth, what infants initially perceive as a surface might become an object. For example, the floor of a playpen is a surface to an infant, whereas the playpen is an object to an adult, who can fold it and carry it away. Adults use a variety of diverse sources of information in perceiving objects (Kellman & Arterberry, 1998). For example, it is likely that we detect edges (discontinuities in the visual display) and decide whether they are object boundaries. If we see a person standing in front of a car, we assume that the nearer object (in this case, the person) has a boundary and that the car continues behind the person. We do not think the car stops at one edge of the person and then starts again at the other edge. Depth and motion cues help in these perceptions. The perception of edges and boundaries helps us extract an object or figure from the background environment (figure 10.5). You might recall doing puzzle pages of embedded figures. In these pages, an artist embeds familiar objects such as a ball or candy cane in a line drawing. The type of perception that allows us to find the embedded objects is figure- and-ground perception. The perception of edges and boundaries also helps us distinguish whole objects from parts of an object, called whole-and-part perception. For example, if you are driving down the street and see half a bicycle tire protruding from a row of parked cars and a child’s head above it, you are not puzzled. You immediately perceive that a child on a bicycle is pulling into your path, and you slow down. 364 Figure 10.5 A test plate from the Figure-Ground Perception Test of the Southern California Sensory Integration Test. A child must identify which of the six objects in (b) are present, or embedded, in picture (a). Reprinted by permission from Ayres 1972. Figure-and-ground perception is the ability to see an object of interest as distinct from the background. Whole-and-part perception is the ability to discriminate parts of a picture or an object from the whole, yet integrate the parts into the whole, perceiving them simultaneously. We know little of infant perception of edges and boundaries. Some research indicates that infants rely more on depth and motion cues than on edges in perceiving objects (Granrud et al., 1984; von Hofsten & Spelke, 1985). Children improve in figure-and-ground perception tasks between 4 and 6 years of age (Williams, 1983) and again between 6 and 8 years (Temple, Williams, & Bateman, 1979). 365 Very young children have difficulty integrating objects that form a whole. For example, you might recall seeing sculptures comprising familiar objects, such as a stick person constructed from nuts and bolts. Pictures of this nature are used to assess whole-and-part perception (figure 10.6). Children under 9 years of age typically report seeing only the person, only the nuts and bolts, or both but at different times. After 9, most children can integrate parts and the whole into the total picture (Elkind, 1975; Elkind, Koegler, & Go, 1964). Realize, though, that adult levels of sensitivity to object perception cues far exceed what is necessary to perceive objects in typical environments (Kellman & Arterberry, 1998). So, infants may still perceive objects fairly well, even though not at adult levels. Think about driving a car around a busy city. When and how often is space and object perception involved in your actions? 366 Figure 10.6 A typical picture that could be used to assess whole-and-part perception. Very young children typically report seeing a face or the pieces (e.g., banana, strawberry, pumpkin). Older children and adults typically report seeing a face made from pieces of fruit. The perception of distances influences our perception of objects in the environment and their properties. We must perceive that an object has constant size even though it might vary in distance from us (figure 10.7). Slater, Mattock, and Brown (1990) showed that newborns have size constancy. They demonstrated first that infants look more at objects with a larger projection size on the retina. Next they familiarized the infants with cubes (large and small) whose size remained constant but whose distance from the infants was varied. When a familiar object with constant size was shown to the infants along with an object of novel size (at distances to make both projected sizes equal), they looked more at the novel-sized object, indicating that they detected the difference in size (see the “Assessment of Infant Perception” sidebar). 367 Figure 10.7 Size constancy. The image of an object halves in size with each doubling of the distance of the object from the eye, but the object does not appear to shrink. We assume that the object is constant in size and is changing distance from us rather than changing in size and remaining at a constant distance. Evidence of newborn sensitivity to shape or form—that is, shape constancy—has also been found through the habituation method. If a newborn becomes familiar with one shape and is then presented that shape along with a new one, the newborn will spend more time looking at the new shape if shape differences are perceived. One type of form perception is face perception, and even 4-day-old infants spend a longer time looking at their mothers’ faces than at a female stranger’s (Bushnell, 1998; Bushnell, Sai, & Mullin, 1989). They probably use the outer contour of faces to perceive patches of light and dark as faces. To perceive form, viewers must either attend to or ignore the spatial orientation of objects, depending on whether this information is relevant to the task at hand. In some cases, it is crucial to recognize that two objects are identical even if one is tipped to one side, upside down, or rotated. In other situations, the differing orientation of an object or symbol is critical to its meaning; such is the case with the letters d and b. Size constancy is the perception of actual object size despite the size of its image as projected on our retina. Shape constancy is the perception of actual object shape despite its orientation to a viewer. Habituation is the state of having adapted to a stimulus. Spatial orientation is the orientation or position of objects as they are located in space or in a two-dimensional drawing. Imagine you are an early childhood teacher and identify all the letters that can become another letter if their orientation is changed. Key Point Infants are sensitive to the size and shape of objects. 368 Children seem better able to attend to spatial orientation of an object than to ignore it (Gibson, 1966; Pick, 1979). Children at 3 and 4 years of age can learn directional extremes such as high and low, over and under, and front and back, but they often call intermediate orientations the same as the nearest extreme. By age 8, most children have learned to differentiate obliques (various angles) and diagonals (45°) but may still confuse left and right (Naus & Shillman, 1976; Williams, 1973). Assessment of Infant Perception Because infants are not able to describe what they perceive, researchers must devise other ways to discover what infants perceive through observing their actions and responses. One of the clever ways this is done is through preferential looking. Infants tend to look at objects or events that are new, surprising, or different from those they are familiar with. Similarly, the attention of an infant tends to wander away from objects and events to which they are exposed continuously or repeatedly. In the latter case, we say the infant has habituated to the object or event. To determine what infants perceive with this method, researchers first expose an infant to an object or event, typically for a certain length of time or number of presentations. When the infant habituates to this stimulus and becomes so used to it that the infant’s attention wanders elsewhere, the researcher presents another object or event that is different in some dimension. For example, the researcher changes the size of the object if she is interested in perception of size, or the shape if she is interested in perception of shape. The infant attends to the new stimulus if he perceives it as different. The infant shows little interest if the object is perceived to be the same as the familiar one. The researcher could vary the amount of change to see how much difference the infant perceives. Researchers also habituate an infant to an object and then present it along with a novel object. Usually, one object is placed on the infant’s right and the other on the left. The researcher, positioned directly in front of the infant, records the amount of time the infant looks at each object. The infant presumably prefers the object that is novel, if indeed the infant perceives a difference, hence the term preferential looking. If much more time is spent looking at the novel object, the researcher concludes that the infant perceives the new object as different from the familiar one. If there is no difference in looking time, the researcher concludes that the difference is not perceived. To learn more about this method, see Bornstein (1985). 369 Preferential looking is a research technique in which two stimuli are presented to a subject, who turns to look toward the preferred stimulus. Perception of Motion Motion perception is of particular interest in the study of motor development. We know that dedicated neurological mechanisms exist for detecting motion. Specifically, individual cortical cells fire according to the direction, location, and speed of an object on the retina, and the medial temporal area of the visual cortex is dedicated to processing motion signals (Kellman & Arterberry, 1998). Therefore, it is not surprising that infants perceive motion. Early in infancy, however, infants lack adult sensitivity to motion. Direction of motion is not well perceived until 8 weeks of age (Wattam-Bell, 1996a,b). Thresholds for detecting velocity are higher in newborns than in adults; however, by 6 weeks of age, only extremely slow velocities of nearby objects are difficult for the infant to perceive (Aslin & Shea, 1990; von Hofsten, Kellman, & Putaansuu, 1992). Older adults have difficulty perceiving motion at the detection thresholds (Elliott, Whitaker, & Thompson, 1989; Kline, Culham, Bartel, & Lynk, 1994). It is not clear whether this has practical significance in real-world conditions; further research is needed. A detection threshold is the point on a continuum at which the energy level is just sufficient for one to register the presence of a stimulus. This discussion of visual perception has been necessarily brief. Overall, however, the evidence discussed here indicates that basic visual perceptions provide even infants with a great deal of information about the environment. As a child grows, perception at thresholds of detection improves to adult levels. It is likely that attention plays a role in the performance of visual perception tasks and that performance is improved by attention to the important parts of the environment (Madden, Whiting, & Huettel, 2005). However, perception at thresholds will most likely show decrements in older adulthood. Far more information is needed on what significance performance at thresholds has for daily tasks in the real world. Key Point Infants perceive motion, but direction and velocity are better perceived with advancing age. 370 Kinesthetic Development The kinesthetic system might be described as the system that gives us “body sense.” It is certainly vital to our ability to position ourselves and move in our environment. To know that this is true we need only recall walking through a haunted house at a fair or circus, where our visual system and our kinesthetic system are given conflicting information. Kinesthetic Sensation The kinesthetic, or proprioceptive, system is important to skill performance because it yields information about the relative position of the body parts in relation to each other, position of the body in space, body’s movements, and nature of objects that the body comes into contact with. Unlike the visual system, which relies on the eyes as sensory receptors, kinesthetic information comes from various types of receptors throughout the body called proprioceptors (table 10.1). Those proprioceptors located in the muscles, at the muscle– tendon junctions, in joint capsules and ligaments, and under the skin are called somatosensors; those located in the inner ear are called the vestibular apparatus (figure 10.8). 371 Proprioceptor is the collective name of the various kinesthetic receptors located in the periphery of the body; the two types of proprioceptors are the somatosensors and the vestibular apparatus. Somatosensors are the receptors located under the skin, in the muscles, at muscle–tendon junctions, and in joint capsules and ligaments. The vestibular apparatus houses the receptors located in the inner ear. 372 Figure 10.8 Structures of the inner ear. Sensory receptors are located in the utricle and saccule. Many infantile reflexes are stimulated through kinesthetic receptors. Therefore, the onset of a reflex indicates that the corresponding kinesthetic receptor is functioning. The first prenatal reflex that can be elicited is opposite-side neck flexion through tactile stimulation around the mouth at just 7.5 weeks after conception. Key Point Kinesthetic sensation comes from a variety of sensory receptors throughout the body. Researchers have used tactile stimulation of other body parts to determine that cutaneous receptor development proceeds in an oral, genital–anal, palmar, and plantar (sole of foot) sequence. This developmental sequence follows the cephalocaudal and proximodistal growth directions we discuss in chapter 4. At birth, infants clearly respond to touch. They can also identify the location of touches, especially in the region of the mouth and the face (Kisilevsky, Stach, & Muir, 1991). We know, too, that the vestibular apparatus is anatomically complete at approximately 9 to 12 weeks of prenatal life, but its functional status before birth is unclear. The labyrinthine righting reflex appears around the second postnatal month (Timiras, 1972), and that fact provides some evidence of vestibular function. Therefore, the system for kinesthetic 373 sensation is functional in early life. What are some of the ways parents might use touch to communicate with their infants? We have anecdotal information but little research data on age-related changes in kinesthetic sensation (Boff, Kaufman, & Thomas, 1986). Indications are that absolute thresholds increase and that at least some older adults experience decreased sensitivity (Kenshalo, 1977). Far more objective research is needed on the aging of kinesthetic receptors. Tactile Localization Although newborns feel touches, an individual must know where on the body a touch occurs as well as the nature of that touch; this knowledge is termed tactile localization. Making fine discriminations about where one has been touched, sight unseen, and whether the touch involves one point or two in close proximity, is an ability that develops in childhood. Children are less accurate at 4 years of age than at 6 to 8 years in locating a touch on the hands and forearms; performance on this type of task does not improve significantly between ages 6 and 8 (Ayres, 1972; Temple et al., 1979). Based on this limited data, then, the perception of tactile localization on the hands and arms seems to be relatively mature by age 6. Tactile localization is the ability to identify without sight the exact spot on the body that has been touched. Threshold discrimination—detecting the smallest gap between two points that touch the skin—varies in different areas of the body (figure 10.9); we do not know whether it also varies with age (Van Duyne, 1973; Williams, 1983). Ayres (1966) reported that only half of a group of 5-year-olds could consistently discriminate a touch on different fingers, though average performance improved through 7.5 years of age (the oldest age tested). 374 Figure 10.9 Tactile point perception includes accurate judgment of the number of simultaneous touches on the skin. As two touch points get closer together, it is more difficult to discriminate between a single touch and two touches. Key Point Children improve in their ability to locate touches, but little is known about threshold discriminations for touch. Recognizing unseen objects and their characteristics by feeling them with the hands is the kinesthetic perception parallel to the visual perception of objects. In infants, such manipulation is often more accidental than purposeful. Yet by age 4, the average child can handle objects purposefully, and by age 5 a child can explore the objects’ major features. Manual exploration becomes systematic—that is, children become more methodical—at about age 6 (Van Duyne, 1973), and in the next 2 years, haptic (cutaneous) memory and object recognition also improve (Northman & Black, 1976). Research by Temple et al. (1979) indicates that children also increase their speed of tactile recognition during this time. Perception of the Body (Body Awareness) To carry out everyday activities and to perform complex skills, it is necessary to have a sense of the body, its various parts, and its dimensions. One aspect of body awareness is the identification of body parts. As children get older, more of them can label the major body 375 parts correctly (DeOreo & Williams, 1980), and they can name more detailed body parts (Cratty, 1979). The rate at which an individual child learns body part labels is largely a function of the amount of time parents or other adults spend practicing with the child. Probably two-thirds of 6-year-olds can identify the major body parts, and mistakes are rare in all typically developing children after age 9. Body awareness is the recognition, identification, and differentiation of the location, movement, and interrelationships of body parts and joints; it also refers to a person’s awareness of the spatial orientation and perceived location of the body in the environment. Imagine you are a youth sports coach in one of your favorite activities. What skills in this activity rely at least in part on body awareness? What cues can you use to draw a beginner’s attention to body position? Children also need a sense of the body’s spatial dimensions, such as up and down. They usually master the up–down dimension first, followed by front–back, and finally side. A high percentage of 2.5- to 3-year-olds can place an object in front of or behind their bodies, but more of them have difficulty placing an object in front of or behind something else. By about age 4, most children can do the latter task as well as place an object to the side of something (Kuczaj & Maratsos, 1975). Laterality Although children typically master up–down and front–back awareness before age 3, they develop an understanding that the body has two distinct sides, or laterality, at approximately 4 to 5 years of age (Hecaen & de Ajuriaguerra, 1964). The child comes to realize that even though his two hands, two legs, and so on are the same size and shape, he can position them differently and move them independently. Eventually, the child is able to discriminate right and left sides—that is, to label or identify these dimensions. Laterality is a component of body awareness—specifically, the awareness that one’s body has two distinct sides that can move independently. An age-related improvement in the ability to make right–left discriminations occurs between age 4 or 5 and age 10; most children respond almost perfectly by age 10 (Ayres, 1969; Swanson & Benton, 1955; Williams, 1973). However, children can be taught to label right and left at younger ages, too, even as young as 5 (Hecaen & de Ajuriaguerra, 1964). Young children also have difficulty executing a task when a limb must cross the midline of the body, such as writing on a chalkboard from left to right. This ability improves between ages 4 and 10, but some 10-year-olds still have difficulty with such tasks (Ayres, 1969; Williams, 1973). 376 Imagine you are an early childhood educator. What activities could you use to help children learn to discriminate between left and right? Lateral dominance Interest in lateral dominance, especially hand dominance, dates back to Aristotle. Over the centuries, some have favored a nativist view of dominance suggesting that lateral dominance is inborn, especially after Broca’s studies suggested asymmetries between the right and left halves of the brain. Broca was a French surgeon who first reported in 1861 that individuals with loss of language abilities had lesions in a specific area on only one side of the brain. This finding first suggested that an entire function, in this case speech, might be controlled by only one side of the brain. Today we know that “Broca’s area” is not the only site in the brain involved in speech. Lateral dominance is the consistent preference for use of one eye, ear, hand, or foot instead of the other, although the preference for different anatomical units is not always on the same side. Other behavioral scientists have favored a “nurture” perspective (see chapter 2), holding that preference for writing and tool use can be changed by training. In recent years the study of lateral dominance has been tied to the study of language development, which has hindered rather than helped our understanding of handedness (see Hopkins & Ronnqvist, 1998, for a discussion). Asymmetries in hand use have been widely observed in infants. Infants younger than 3 months of age grasp objects longer, make a fist longer, and are more active with one hand than the other (Hawn & Harris, 1983; Michel & Goodwin, 1979; Michel & Harkins, 1986). These asymmetries are not consistently predictive of adult hand dominance (Michel, 1983, 1988), but a link may exist between early asymmetries and later hand dominance because the asymmetries tend to follow orientation. Infants who prefer to turn their heads to the right seem to prefer reaching with their right hands, and vice versa. These self- generating experiences may facilitate eye–hand coordination of one hand more than the other (Bushnell, 1985; Michel, 1988). When infants begin to reach after 3 months, they also demonstrate a hand preference (Hawn & Harris, 1983). Unimanual manipulation appears at approximately 5 months, and by 7 months infants show a preference for manipulating with a particular hand (Ramsay, 1980; table 10.2). A hand preference is evident approximately 1 month after bimanual manipulation first appears, even as both hands hold an object (Ramsay, Campos, & Fenson, 1979). Infants typically prefer the same hand in unimanual and bimanual handling; that is, they use either the right or left hand in both types of manipulation 377 (Ramsay, 1980). Although these early preferences might change, usually the hand that emerges as preferred in early childhood, most often by age 4, remains the dominant hand in youth and adulthood (Sinclair, 1971). It is important to remember that in certain environmental situations children might find it convenient to use their nonpreferred limbs (Connolly & Elliott, 1972). By adulthood, individuals typically use their dominant limbs even if it is more awkward to do so. Think about your daily activities and the instruments you use. Do any of them favor right handedness or left handedness? 378 In addition to hand preferences, we come to favor one of our eyes, ears, and feet over the other. If the favored parts are all on one side of the body, the dominance is termed pure; otherwise, it is called mixed. In the past, developmentalists have suggested that pure dominance is preferable, the implication being that one side of the brain is clearly dominant. For example, in the 1960s, a popular perceptual-motor theory proposed by Doman and Delacato (Delacato, 1966) held that pure dominance is necessary for proper neurological organization. Those with mixed dominance could anticipate problems in perceptual-motor performance, reading, speech, and other cognitive abilities. Research has never shown this to be the case. Studies have failed to show any real cognitive advantage for individuals with more lateralized brains (Kinsbourne, 1988, 1997). Key Point Although some researchers have suggested that pure dominance is necessary for proper neurological organization, no objective evidence indicates that having a more lateralized brain is advantageous. Limb Movements You can assess a child’s perception of the extent of movement at a joint by asking the child to accurately reproduce a limb movement or to relocate a limb position without looking. Children improve in this task between ages 5 and 8; little improvement is noted after age 8 (Ayres, 1972; Williams, 1983). Spatial Orientation Kinesthetic spatial orientation involves perception of the body’s location and orientation in space, independent of vision. Temple et al. (1979) tested this perception by asking children to walk a straight line while blindfolded and measuring their deviation from the straight path. Performance improved between 6 and 8 years of age; 8-year-olds were the oldest age group included in the study. Investigations of spatial orientation over a wider age range are necessary. 379 Direction Directionality is often linked to laterality, an awareness of the body’s two distinct sides. Children with a poor sense of laterality typically also have poor directionality. Although this relationship seems intuitively logical, deficiencies in laterality are not known to be the cause of deficiencies in directionality (Kephart, 1964). Directionality is the ability to project the body’s spatial dimensions into surrounding space and to grasp spatial concepts about the movements or locations of objects in the environment. Individuals obtain most information for directional judgments through vision, so these judgments rely on integration of visual and kinesthetic information. Long and Looft (1972) suggested that children improve their sense of directionality between ages 6 and 12. By age 8, children typically can use body references to indicate direction. They are able to say correctly, “The ball is on my right” and “The ball is to the right of the bat.” At age 9, children can change the latter statement to “The ball is to the left of the bat” when they walk around to the opposite side of the objects. They can also identify right and left for a person opposite them. Such improvements in directional references continue through age 12. Long and Looft noted that some refinement of directionality must take place in adolescence because many 12-year-olds are unable to transpose left and right from a new perspective, such as when looking in a mirror. Kinesthetic Changes With Aging We know very little about how aging affects the kinesthetic receptors themselves, but researchers have identified age-related changes in kinesthetic perception. Some, but not all, older adults lose cutaneous sensitivity, vibratory sensitivity, and sensitivity to temperature and pain (Kenshalo, 1977). Older adults experience some impairment in judging the direction and amount of passive lower limb movement (in which someone else positions the limb) (Laidlaw & Hamilton, 1937). However, they remain fairly accurate in judging muscle tension produced by differing weights (Landahl & Birren, 1959). 380 Auditory Development Although it is not as important to skill performance as vision or kinesthesis, auditory information is still valuable for accurate performance. People often use sounds as critical cues to initiate or time their movements. Auditory Sensation Hearing involves the external ear, the middle ear, and the cochlea of the inner ear. The inner ear develops first and is close to adult form by the third prenatal month. By midfetal life, the external ear and middle ear are formed as well (Timiras, 1972). Fetuses reportedly respond to loud sounds, but perhaps this response is actually to tactile stimuli—that is, vibrations (Kidd & Kidd, 1966). A newborn’s hearing is imperfect partly because of the gelatinous tissue filling the inner ear. The absolute threshold is about 60 decibels higher for a newborn than for an adult. So, a newborn can detect only an average speaking voice when an adult can detect a whisper (Kellman & Arterberry, 1998). Newborns also do not discriminate changes in the intensity of sounds (differential threshold) or in sound frequencies as well as adults can. Absolute threshold is the minimal detectable sound that a hearer can sense at least half of the time a signal is sounded. Differential threshold is the closest that two sounds can be yet still allow the hearer to distinguish them at least 75% of the time. The gelatinous material in the inner ear is reabsorbed during the first postnatal week so that hearing improves rapidly (Hecox, 1975; Timiras, 1972). By 3 months, infants hear low- frequency sounds (500–1,000 Hz) very well but do not hear high-frequency sounds (4,000 Hz) quite as well. Because human speech generally is under 5,000 Hz, this level of hearing permits the infant to sense speech. In fact, infants might be predisposed to listen to speech (Vouloumanos & Werker, 2007) and process their own mother’s voice faster than that of others as early as 4 months of age (Purhonen, Kilpelainen-Lees, Valkonen-Korhonen, Karhu, & Lehtonen, 2005). The infant can hear low- to mid-pitched voices better than high-pitched voices. By 6 months, however, infants’ hearing is similar to that of adults, including hearing of high-frequency sounds (Spetner & Olsho, 1990). Key Point Infants can hear human speech. 381 More older adults than younger adults suffer from presbycusis (from presbys for “old man” and okousis for “hearing”), but the source of this loss varies among individuals. Some hearing loss might result from physiological degeneration, but hearing loss often results from lifelong exposure to environmental noise (Timiras, 1972). Presbycusis is a loss of hearing sensitivity. The absolute threshold for hearing pure tones and speech increases in older adults, meaning that sounds must be louder for older adults to hear them. Differential thresholds also increase for pitch and speech discrimination (Corso, 1977). As a person ages, the ability to hear high-frequency sounds is particularly affected. One result is that older adults cannot hear certain consonant sounds well; they might report that they hear someone talking but cannot understand the message. Older adults are also at a distinct disadvantage in adverse listening situations, such as attempting to listen to one person in a room crowded with talking people (Stine, Wingfield, & Poon, 1989). Think of your older relatives and acquaintances. What activities of daily living can present difficulties for seniors with presbycusis? Auditory Perception It is easy to overlook the amount of information we get from sound. For example, we can determine the location of an event by sound, whether something or someone is leaving or approaching, who or what made a sound, and even the material from which an object is made. Although we think of vision and kinesthesis as more important to skill performance, auditory perception gives us much information about the environment in which we move. The aspects of auditory perception that we discuss here are location, differences between similar sounds, patterns, and auditory figure and ground. Notice that several of these aspects are parallel to types of visual and kinesthetic perception. Location We locate a sound by determining its direction and distance from us (figure 10.10). Newborns turn in the direction of a sound, and they rapidly improve in their ability to locate sound during the first year. When presented two sounds, each in a different location, the minimum angle between the two locations that 6- and 7-month-olds can detect as being two different locations is in the range of 12° to 19° (Ashmead, Clifton, & Perris, 1987; Morrongiello, 1988b), compared with 1° to 2° in adults. Infants determine the direction of nearby sounds better than that of distant sounds, but improvement is continuous such that by age 3 children can determine the direction of even distant sounds (Dekaban, 1970). Infants between 4 and 10 months also can distinguish temporal patterns 382 in sounds (Lewkowicz & Marcovitch, 2006). 383 Figure 10.10 Sound localization. The more a sound deviates from the straight-ahead position, the greater the time difference in the arrival of the sound at each ear. Reprinted by permission from Bower 1977. Think of your living routines. What are some tasks that involve localizing sound? It is more difficult to determine how well infants perceive the distance of a sound. Clifton, Perris, and Bullinger (1991) found that 7-month-olds reached for an object that was the source of a sound significantly more often when it was within reach than when it was out of reach. It is possible they choose not to reach because they know it is out of reach. It seems that from birth, individuals have some sense of the location of sounds in the environment and rapidly improve in determining both the direction and distance of sounds. Older adults with presbycusis show a notable decrement in the ability to localize sound (Nordlund, 1964). This is not surprising given that localization depends on accurate auditory sensations related to the time at which sounds arrive at the two ears and on the intensity differences in sound. In fact, older adults who demonstrate good speech discrimination abilities also show normal sound localization, whereas those with poor speech discrimination show poor sound localization (Hausler, Colburn, & Marr, 1983). Key Point Auditory acuity is not as good in infants as in adults. 384 Differences The perception of sound differences by children is often studied by means of discrimination tasks. For example, children are asked to distinguish two sounds that are similar in pitch, loudness, or speech sound, such as d and t or b and p. Infants as young as 1 to 4 months can discriminate between basic speech sounds, such as p, b, and m (Doty, 1974), but children between 3 and 5 years old experience increasing accuracy in recognizing differences in sounds (DiSimoni, 1975). Temple et al. (1979) found a further improvement in auditory discrimination between 6 and 8 years, as did Birch (1976) between 7 and 10 years, in an auditory matching task. A similar trend apparently exists for discrimination of pitch (Kidd & Kidd, 1966). In general, it appears that by 8 to 10 years of age, children have greatly improved their ability to detect differences in similar sounds, but they continue to refine their auditory discrimination skills until they are at least 13. If we could set aside age-related changes in sensitivity for hearing pure tones, the deficits in hearing speech sounds among older adults would be minimal (Helfer, 1992; Lutman, 1991; van Rooij & Plomp, 1992). Therefore, age deficits in speech perception are largely a function of decline in pure-tone sensitivity. Interestingly, older adults often make better use of context cues to help their recognition of speech than young adults do (Craig, Kim, Rhyner, & Chirillo, 1993; Holtzman, Familant, Deptula, & Hoyer, 1986). Patterns For speech and music to be more than just noises, individuals must perceive relationships between sounds. Of course, we perceive patterns in other senses. Visual pattern perception has long interested developmentalists, but attention to auditory pattern perception is more recent. Auditory patterns are nonrandom, temporally (time-) ordered sound sequences. Three properties of sound give rise to auditory patterns: 1. Time 2. Intensity 3. Frequency (Morrongiello, 1988a) Speech and music have a temporal pattern, an intensity (loudness or softness) pattern, and a frequency (high pitch or low pitch) pattern simultaneously. Developmentalists usually study one characteristic at a time. Infants as young as 2 to 3 months old react to changes in the temporal pattern of a tone sequence, showing that they perceive temporal patterns (Demany, McKenzie, & Vurpillot, 1977). Young infants, however, perceive only pattern changes involving the number of 385 groups of tones (e.g., changing nine tones from three groups of three to two groups, one of five and one of four) (Morrongiello, 1984). At 12 months, infants can perceive changes in the number of groups and in the number of tones in each group (figure 10.11). Thus, by the end of the first year, infants can perceive sound on the basis of temporal pattern, which is probably a prerequisite for language development. 386 Figure 10.11 Auditory stimulus patterns presented to infants. Young infants can detect a change in the number of groups from what is familiar to them, but it is not until they are 12 months old that they can detect changes in both the number of groups and the number of tones in a group. Time between tones in a group was 0.2 s, and time between groups was 0.6 s. Based on Morrongiello 1988, The development of auditory pattern perception skills. In Advances in infancy research, Vol. 6, edited by C. Rovee-Collier and L.P. Lipsitt (Norwood, NH: Ablex). Infants between 5 and 11 months can discriminate intensity changes for vowels in a syllable (Bull, Eilers, & Oller, 1984), but we know little else about infants’ intensity perception. Infants younger than 6 months can discriminate frequency relationships in a simple, short sequence. Not until the end of their first year, though, can infants perceive frequency relationships between the tones in a long, complex sequence (Morrongiello, 1986; Trehub, Bull, & Thorpe, 1984). The same is true of speech patterns. Children between 4 and 6 years can discriminate the frequency features of six-tone melodies played at normal speed (Morrongiello, Trehub, Thorpe, & Capodilupo, 1985). Infants progress rapidly in auditory pattern perception during their first year. These advances probably are prerequisites to language development. Preschool children make further progress in perceiving patterns in increasingly longer and more complex contexts. What systems might limit the development of auditory pattern perception? Obviously, the auditory system must be developed, and, as mentioned earlier, auditory sensation is quite mature within days of birth. The sensory cortex of the brain, however, is still maturing rapidly over the first few years of life. With continuing development, it probably permits conceptualization of patterns and of the identity of transformed patterns (Morrongiello, 1988a)—for example, the same rhythmic pattern played at different tempos. Cognition also must advance because in order to perceive patterns, an individual must be able to remember and process information, especially for long and complex sequences. In addition, the environment in which infants develop might “tune” the developing auditory system to recognize certain features of language and music. In this way, we might learn to prefer the perceptual patterns prevalent in our native language and the music of our culture (Morrongiello, 1988a; Swingley, 2005; Trehub & Hannon, 2006). 387 Auditory Figure and Ground Often a person must attend to certain sounds while ignoring other irrelevant sounds in the background. In this auditory parallel to visual figure-and-ground perception, the figure is the sound of interest and the ground is distracting noises in the background. For example, try listening to someone talk to you on the telephone (figure sounds) while music is playing and several people in your room are talking (background sounds). Young infants can detect sounds amid ambient noise (Morrongiello & Clifton, 1984), but some children have more difficulty than others in separating auditory figures from the background. We would benefit from more research on the processes underlying these differences. Think about driving an automobile. What clues for driving safely might you miss if music is playing loudly or you are wearing earphones? Older adults commonly report difficulty with hearing amid background noise (see Tun & Wingfield, 1993, for a review). It is possible that this problem reflects changes in the sensory system or in parts of the neurological system related to hearing. It may also reflect changes in attention mechanisms—that is, older adults might have more difficulty attending to a particular sound source in the midst of other sounds. As noted previously, development of auditory perception is rapid. Not long after birth, infants can already perceive the location of and differences in sounds. An individual’s ability to make fine discriminations improves in childhood. Perceptions of auditory events in the environment in adulthood are likely disturbed only when age-related changes in sensation, including injury and disease, affect detection of sounds. Sensation, Perception, and Perceptual-Motor Development Summary It is clear that some aspects of visual, kinesthetic, and auditory perception exist in infancy. Developmental trends continue throughout childhood, especially in the finer discriminations. By the time children are 8 to 12 years old, aspects of their visual perception have developed to near-adult levels. It seems that kinesthetic perception typically develops to near-adult levels somewhat earlier, usually by about age 8, although this generalization is based on limited research. Young children can perceive the location of sound, and by age 10 they perform at near-adult levels on many auditory discrimination tasks. Refinement of auditory skills continues through the early teens. Some aspects of auditory perception have not been studied in children. In general, children between the ages of 8 and 12 approach adult levels of performance on 388 many perceptual tasks, and only small refinements in perceptual skills are yet to be made. Assessments of perceptual-motor development have been designed, and educators and therapists sometimes screen young children for deficits in perceptual-motor development. Some aspects of perceptual development are not well documented; further research is needed. In addition, little is known about what causes changes in perceptual processes as people age, but it is known that decremental changes in the sensory systems reduce the quality of the sensory information reaching the central nervous system, potentially affecting perception. At any age, limited sensory information could serve as a rate limiter to performance by influencing perception of needed information. The perceptual systems do not operate in isolation of one another. To complete this discussion of perceptual development, the following section explores how information perceived in one sense, or modality, is related to that perceived in other modalities. Web Study Guide Administer some simple perceptual-motor test items in Lab Activity 10.1, Testing Perceptual-Motor Development, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 389 Intermodal Perception Events occur in the environment and are often sensed and, therefore, perceived in different modalities, or senses. If a jar slips from our hands while we are trying to open it, we feel it slipping, we see it falling, we hear it hit the floor and break, and we might even smell the released contents. This event can be perceived through vision, kinesthesis, and audition. Developmentalists have considered perception through different modalities from two very different perspectives. From the first, or integrational, perspective, the energy reaching the different senses is of different forms—light, sound, temperature, and so on—and each sensory system yields a unique sensation. The task of a developing infant, then, is to learn how to integrate the separate systems—that is, to learn how those unique sensations are related to one another. The second, or unified, perspective sees the senses as united in bringing information about events, but through different modalities. The nervous system is structured for multimodal perception so that, from the start, perceptions are coherent in time and space (Damasio, 1989; Stein & Meredith, 1993). The perceptual systems extract patterns; many patterns are similar across the modalities. For example, events occur at a point in time, so the temporal properties of an event are not unique to any one modality. In a sense, these patterns are amodal invariants. We see a drummer strike a drum and we hear the drum’s sound, but we also perceive the rhythmic pattern that existed across vision and audition. In this model, the central task of development is to learn about events in the world with the information coming through various sensory systems (Kellman & Arterberry, 1998). Research in neurophysiology during the 1980s and 1990s lends some support to this unified perspective of intermodal perception. This research has identified areas of the brain containing neurons that receive input from different modalities (Stein, Meredith, & Wallace, 1994), thus calling into question views of separateness among the sensory-perceptual systems. Amodal invariants are patterns in space or time that do not differ across the sensory- perceptual modalities. The unified perspective is more consistent with the ecological perspective of perception and action. The integration perspective is more consistent with an information processing perspective. Earlier chapters acknowledge that much of the research on perception has been done from the information processing perspective. Keep this in mind as we review research in the following areas of intermodal perception: Auditory-visual Visual-kinesthetic Auditory-kinesthetic Spatial-temporal 390 Key Point One view emphasizes the development of infants’ abilities to integrate separate perceptual systems. The other emphasizes the perception of patterns about an environment unified across the systems. Auditory-Visual Intermodal Perception Newborn infants have been observed to move their eyes in the direction of a sound. Morrongiello, Fenwick, Hillier, and Chance (1994) played a 20 s recording of a rattle to newborns. They placed the loudspeaker at varying angles from the infant’s midline. The farther the speaker from the midline, the farther the newborns turned their heads. In some trials, the investigators also shifted the sound from one loudspeaker to another, and the infants adjusted their heads correspondingly. Although this is a rudimentary response, it appears that even newborns seek to match their visual fixation to the spatial origin of a sound. Key Point Newborns link visual and auditory events, and children improve in finer visual-auditory intermodal discriminations. More challenging auditory-visual perceptions show a developmental trend in childhood (see figure 10.12). Goodnow (1971b) tapped out a sequence [* ***], then asked children to write the sequence using dots and spaces to picture where the taps occurred. She also reversed the auditory-visual (A-V) task by asking children to tap out a pictured sequence (V-A). Children around age 5 did not perform the A-V sequence as well as children at age 7. A trend toward improved performance on the V-A task was also found in children between ages 6.9 and 8.5 years. This and similar studies indicate that visual and auditory intermodal perception improves between ages 5 and 12 (Williams, 1983). Young children find A-V tasks more difficult than V-A tasks, but this difference diminishes after age 7 (Rudel & Teuber, 1971). 391 Figure 10.12 Musicians play notes in the rhythm notated in their sheet music to create sounds corresponding to the notated pattern. Imagine you are a youth coach in your favorite sport. Is the integration of auditory and visual information involved in any part of the sport? How about kinesthetic and visual information? What might you do to help beginners use this information to improve performance? Visual-Kinesthetic Intermodal Perception Visual-kinesthetic perception is the coordination between seen and felt properties of objects. Because infants do not reach for and manipulate objects before 4 to 5 months of age, the study of visual-kinesthetic perception at young ages centers on mouthing of objects. Meltzoff and Borton (1979) found that 1-month-olds looked longer at the type of pacifier that they had mouthed but had not seen, either one that was a cube with nubs or one that was a smooth sphere. At some level, then, infants relate oral and visual information. Visual-kinesthetic perception involving manipulation of objects has been explored at later stages of infancy, and research study outcomes have been variable. Goodnow (1971a) studied visual and kinesthetic integration in children. She presented five shapes (Greek and Russian letters) by either sight or feel to three age groups (5.0- to 5.5-year-olds, 5.6- to 6.8- year-olds, and 9.0- to 10.0-year-olds). She then presented these five shapes, along with five new ones, again by sight or feel, and challenged the children to identify the familiar shapes. Four presentation patterns were possible: 1. Visual presentation–visual recognition (V-V) 2. Kinesthetic presentation–kinesthetic recognition (K-K) 3. Visual presentation–kinesthetic recognition (V-K) 392 4. Kinesthetic presentation–visual recognition (K-V) Goodnow found that children, especially the youngest ones, had more difficulty in the K-K pattern than in the V-V pattern. This performance discrepancy narrowed in the older age groups. The K-V task proved more difficult for the children than the V-K task. Goodnow noted that the scores of the youngest group in the kinesthetic conditions were extremely variable. This study and others using similar tasks and different age groups lead us to the conclusion that a developmental trend exists in visual-kinesthetic intermodal perception during childhood. When the kinesthetic task involves active manipulation of an object, 5- year-olds can recognize the shapes relatively well, but slight improvement continues until age 8. If passive movements are involved, performance is not as advanced, and improvements continue through age 11 (Williams, 1983). Key Point Children have more difficulty in intermodal visual and kinesthetic perception if their initial exposure is through the kinesthetic system. Auditory-Kinesthetic Intermodal Perception The amount of research conducted on auditory-kinesthetic integration is small compared with that involving vision. Temple et al. (1979) included the Witeba Test of Auditory- Tactile Integration in a test battery administered to 6- and 8-year-olds. In this test, an experimenter twice tells a child the name of an object or shape. The child then feels a number of objects or shapes, attempting to select the one that matches the auditory label. The investigators found that 8-year-olds performed this task much better than 6-year-olds did. This experimental method is based on children understanding the label given to the object or shape. Thus, it is possible that this age-related difference in performance resulted from younger children not knowing, misunderstanding, or not remembering the auditory label in addition to, or instead of, their auditory-kinesthetic intermodal perception. With this limitation in mind, we can tentatively conclude that auditory-kinesthetic integration improves in childhood. Spatial-Temporal Intermodal Perception You may recall the earlier discussion of amodal invariants—patterns that might be invariant across modalities—such as space and time. For example, when children viewed the dot pattern in Goodnow’s experiment, they were dealing with a spatial stimulus—the 393 arrangement of dots in space. When they listened to an auditory pattern, they were attending to a temporal (time-based) stimulus. They were perceiving, then, a pattern that crossed space and time as well as vision and audition. Sterritt, Martin, and Rudnick (1971) devised nine tasks that varied the number of perceptual integrations to be made as well as the type of integration, including spatial- temporal characteristics. For example, a child must integrate a short pause between two tones (temporal) with a short space between two dots (spatial). They presented the nine tasks to 6-year-olds. The easiest task for the children was the V-V spatial (intramodality) task. Children had some difficulty with tasks that required them to integrate visual-spatial stimuli with visual-temporal or auditory-temporal stimuli. They had more difficulty integrating two temporal patterns, whether the task was intra- or intermodal. While progressing in intermodal perception, then, children also improve their ability to integrate spatial and temporal stimuli as well as their ability to integrate two sets of temporal stimuli. Key Point Temporal patterns are more difficult than spatial patterns for children to integrate. Difficult or subtle aspects of intermodal perception might continue to develop during adolescence. Intermodal perception most likely is stable in adulthood and older adulthood. Any decrements are probably a function of changes in the sensory-perceptual systems— changes that affect the amount of information available. For example, if cataracts cause a viewer to miss seeing the details of an object, the visual information for an integration may not be available. On the other hand, older adults might be able to use their experience as a compensatory mechanism and use information from one modality to compensate for information not available in another because of age-related change in the sensory or neurological systems. Certainly, more research is needed on this topic. Web Study Guide Observe children’s intermodal perception of small objects seen and manipulated in Lab Activity 10.2, Development of Intermodal Perception, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Intermodal Development Summary 394 Intermodal coordination begins at birth, but there appears to be a developmental trend in childhood and adolescence for tasks that involve matching and subtle aspects of integration. The accuracy of children’s performance is related to the order of presentation. That is, presenting the visual pattern or object first yields better performance than presenting the auditory information first. Also, children first master spatial-spatial integration tasks, followed by mixed spatial and temporal tasks, and finally temporal-temporal tasks. 395 Summary and Synthesis The visual, kinesthetic, and auditory systems function at birth and continue to improve throughout infancy and childhood. The level of function in infancy appears adequate for the learning tasks facing infants, and the rate of improvement seems consistent with the learning tasks facing toddlers. By late childhood, the senses function at levels similar to those of adults. Detecting stimuli, however, is not the same as knowing what they mean; sensation is not the same as perception. In chapter 11 we consider the advancement of perception and perceptual-motor behavior. Aging is accompanied by change in the sensory systems, although we know more about the changes in vision than about those in kinesthesis or audition. Decrements tend to vary widely among older adults. Compensation for some of these losses is possible; for example, seniors can wear eyeglasses or hearing aids. Conditions that accentuate differences are also helpful to older adults. Examples include the provision of good lighting and the reduction of background noise. Of course, a decrement in sensation can make for difficulty in perception, so just as with the early portion of the life span, it is important to study age- related change in perception. Traditionally, much of the study of perceptual and perceptual-motor development has focused on the changing capacity of each individual system regardless of the environment. Even study of how the perceptual systems put together information received by each system about the same object or event assumed that integration was a step that followed perceptual processing. More ecological views of perception have been around for some time, but the difficulty of studying a perceptual system and the environment as an ecosystem has limited the data available to inform this perspective. Reinforcing What You Have Learned About Constraints Take a Second Look When we watch an IMAX movie, the visual sensations and perceptions can be so real that even the kinesthetic system responds as if we were having the real experience. Those inclined to get airsick or carsick might actually do so watching the movie. The interactions of the sensory-perceptual systems are demonstrated even in the somewhat artificial experience of watching an IMAX movie. The model of constraints we use throughout our discussion of motor development stresses interactions. Thus, the ecological viewpoint is a better fit with our model of constraints. If we extend the ecological viewpoint a step further, perceptual and motor or perception–action become inseparable. Each continually informs the other so that there is a continuous interaction of perceiving the world and moving in it. We can see how important it is for teachers, therapists, coaches, and trainers 396 to understand sensory and perceptual systems and how these systems might change over the life span. As the final part of this discussion of perceptual-motor development, chapter 11 explores perception–action from this viewpoint. Test Your Knowledge 1. What changes in visual sensation occur during infancy? During childhood? 2. Describe how humans perceive depth in the space around them. 3. Describe the various aspects of visual object perception. 4. What changes in kinesthetic sensation occur during infancy? During childhood? 5. What are the various aspects of kinesthetic perception, and at what age does each likely reach near-adult levels? 6. What changes in auditory sensation occur during infancy? During childhood? 7. What are the various aspects of auditory perception, and what information about the sound source does each provide? 8. What changes occur in the visual, kinesthetic, and auditory receptors in older adulthood? 9. What is the typical method for studying intermodal perception? 10. What are amodal invariants? Learning Exercise 10.1 Limited Sensation We can gain an appreciation of the information that comes to us through our sensory- perceptual systems by artificially limiting that information. Working with a partner, try these activities: 1. Play catch in an old pair of sunglasses with tape covering one lens. 2. Walk down a hallway while blindfolded (with your partner guiding you for safety). 3. Facing away from your partner, carry on a conversation while wearing a pair of earplugs as your partner slowly increases the distance between the two of you. Afterward, discuss what tasks were difficult or impossible and what the implications would be for daily living—for example, attending a university, driving a car, or playing intramural sports. 397 Chapter 11 Perception and Action in Development 398 Chapter Objectives This chapter reviews historical perspectives on the role of action in perceptual development; surveys contemporary views on perceptual-motor programs and the links between the cognitive, perceptual, and motor systems; examines differences in perception between infants with and without experience in self-produced locomotion; and studies the interaction between perception and action in maintaining balance after infancy. 399 Motor Development in the Real World Redshirt Your Kindergartener? The term redshirt was borrowed from a practice in athletics in which first-year college players are held out of athletic participation for a year in order to give them additional time to grow and mature. The practice has been particularly popular in sports such as football. In a similar vein, a segment on the television show Today examined the fact that some parents delay their children’s entry into kindergarten for a year (termed an academic redshirt), especially if their children would be among the youngest starting in a given year. Parents have always been concerned about their children’s readiness to start school. They want their children to reach a maturity level that will enable them to succeed in school once they start rather than risk them being behind the majority of their classmates. This parental concern has encouraged some individuals to offer special programs that claim to help children develop the perceptual skills necessary for classroom success. Certainly, success in the classroom depends on a level of perceptual development, and some of the readiness programs promoted to parents emphasize perceptual-motor development. So far, this text has emphasized the sensory and perceptual systems used in motor skills. But might movement actually play a significant role in the development of sensory-perceptual systems? Are perception and movement more tightly coupled than our discussion thus far has acknowledged? Certainly some believe this to be the case, although a variety of views exist on the exact nature of the relationship. This chapter discusses the interrelationship between perception and movement actions. It begins with the notion that movement has a role in, and perhaps is even necessary for, perceptual development. The chapter then examines an aspect of everyday life in which perception and action are linked: maintenance of posture and balance. 400 The Role of Action in Perception Developmentalists have long suspected that movement is extremely important to the development of perception. That is, they suspect that movement through the environment is vital to the coupling of perceptions and purposeful movements in the environment. The exact role of motor activity in the development of perception is difficult for researchers to study. The ideal experiment on this topic, of course, would be to deprive some individuals of movement and compare them with others who were allowed to move through the environment. Because this isn’t possible, our information tends to come from animal studies and other research situations in which movement experiences vary through naturally occurring circumstances. Today, however, new research frontiers are being established through the increasing use of imaging technology, such as magnetic resonance imaging and positron-emission tomography scans. Historical Views The exact nature of the perception–action link, especially in the early years, is so elusive that it has spawned controversies among developmentalists. During the 1960s, a number of individuals proposed perceptual-motor theories, screening tools, and remedial programs. Many of these developmentalists were interested in perceptual-motor activity because they realized that perceptual development is as important to the development of cognition as it is to the development of skilled movement. For example, individuals must perceive the difference in spatial orientation between a circle and a line when they are arranged to form the letter d and when they are arranged to form the letter b. If individuals cannot perceive this difference, they will have difficulty reading. Some earlier developmentalists saw perception as a precursor to both movement and cognition, and they proposed that children with learning disabilities had deficits in perceptual development. Further, they hypothesized that perceptual-motor activity programs could remediate deficits. By practicing to improve perceptual-motor responses, children would overcome perceptual deficiencies, and cognitive activities reliant on perception would benefit as much as motor activities. Among the more popular theories in the mid-20th century were the neurological organization theory of Delacato (1959, 1966); the physiological optics program of Getman (1952, 1963); the visual perception tests and program of Frostig, Lefever, and Whittlesey (1966); the sensory-integration tests of Ayres (1972); the movigenics theory of Barsch (1965); and the perceptual-motor theory of Kephart (1971). Early on, there appeared to be empirical support for these theories and programs as children placed in the remedial programs showed improved classroom performance. The evaluations of these programs, however, were often flawed because they did not account for other factors that could 401 contribute to improvement, such as the increased attention that children received in the remedial programs. Eventually, sufficient information from well-designed evaluations failed to show that participation in a perceptual-motor program produced improvements in readiness skills, intelligence, classroom achievement, or language (Goodman & Hamill, 1973). Perceptual-motor programs did help in the development of motor skills. Piaget (1952) also recognized the importance of movement. He proposed that reality is constructed by relating action to sensory information in well-defined developmental stages during infancy, childhood, and adolescence. For Piaget, neither perception nor action is well organized in infancy. Contemporary Views Today, educators and therapists are cautious about claims that participation in a perceptual-motor program can remediate learning deficiencies. Yet they realize that such participation is beneficial for children with and without learning difficulties. Perceptual- motor programs at least provide valuable experience in performing skills based on key perceptual characteristics of a task. As such, they can contribute to a positive outlook on one’s ability to perform. Perceptual-motor activities can also reinforce concepts needed for motor and cognitive tasks, such as shapes and directions. Most current physical education curricula for young children devote significant time to perceptual-motor activities. In addition, supplemental perceptual-motor programs are frequently offered to special groups. A recent hypothesis posits that physical activity triggers brain activity, which facilitates learning for a time after the period of activity (Ratey, 2008). Let’s first consider the nature of perceptual-motor programs, then consider some contemporary views on links between perception, cognition, and motor activity (action). Perceptual-motor programs can be either comprehensive or focused on certain aspects of perception. They can also be designed for young children in general, for groups with a characteristic deficiency, or for individuals (based on their particular deficits). Table 11.1 illustrates this variability among perceptual-motor programs. Parents and educators alike must act as critical consumers in assessing the value of any program and its claim for success. Until we understand more about the links between perception, cognition, and action, the most realistic claims are those focused on the development of motor skills. The best programs do not advocate a single approach to the exclusion of others. 402 Scanning techniques that allow imaging of the brain and its specific areas are giving rise to new viewpoints. Diamond (2000) reviewed newer evidence of brain function and development to suggest that motor development and cognitive development may be more interrelated than previously thought, even to the extent of being fundamentally intertwined. She pointed to the following findings: 403 The prolonged development of the prefrontal cortex (involved in complex cognitive operations) has been emphasized, but the development of the cerebellum (involved in motor functions), which is also prolonged, has not (see figure 5.11). Similarly, although many complex cognitive skills are recognized as developing into adolescence, it is overlooked that many complex motor skills also develop into adolescence. Functional imaging of the brain (e.g., magnetic resonance imaging while an individual performs a task) has demonstrated that the dorsolateral area in the prefrontal cortex and the neocerebellum in the contralateral hemisphere are coactivated during performance of cognitive tasks. Similar task characteristics activate both of these areas, such as difficult (rather than easy) cognitive tasks; a new task; or a task requiring a quick response, concentration, or greater memory demands. The prefrontal cortex may play a role in motor activity through connections with the cortical and subcortical areas that are important in motor control. The caudate nucleus in the basal ganglia (important in movement control) and dopamine, a neurotransmitter, are involved in neural circuits of both motor and cognitive functions. About half of children with attention-deficit/hyperactivity disorder have motor coordination problems, and some studies report that they have smaller cerebella. Children with dyslexia or specific language disorders frequently have motor deficits. Children with autism frequently have motor impairments. Recent work has focused on a group of brain proteins called factors, especially brain-derived neurotrophic factor (BDNF), which is involved in building and maintaining the infrastructure of the nervous system. It both stimulates growth of neurons and protects against neuron loss. Moreover, BDNF strengthens connections between neurons. Animal studies have demonstrated increases in BDNF in rodents that exercised; the increase was in the hippocampus, an important center in the brain for learning and memory processes (Cotman & Berchtold, 2002). In addition, group of hormones called growth factors are released when circulation increases, as with exercise. These factors work with BDNF, and one of them stimulates capillary growth in the brain (Ratey, 2008). These growth factors and BDNF have a role in neurogenesis and in strengthening connections between neurons, which in turn is necessary for memory. Production of BDNF and growth factors declines with aging. Chapter 14 discusses a link between aerobic exercise and cognitive function in older adults. All of these observations indicate greater interdependence of the brain in cognitive and motor tasks than was previously emphasized. Both thinking patterns and movement, once learned, are stored in primitive areas of the brain that were once thought to control only movement. This process allows higher brain centers to continue adapting to new experiences. Ivry and Keele (Ivry, 1993; Ivry & Keele, 1989; Keele & Ivry, 1990) also 404 proposed that the lateral hemispheres of the cerebellum are involved in critical timing functions that are crucial to sensory, cognitive, and motor tasks. It is interesting to note that children with dyslexia have difficulty with bimanual tasks requiring timing precision (Wolff, Michel, Ovrut, & Drake, 1990). Several contemporary educators report success in promoting active learning for both normally developing and exceptional learners. Active learning is the notion that movement activates the brain and facilitates learning, in contrast to passive learning environments that require learners to sit quietly, watching and listening to a teacher (Hannaford, 1995; Jackson, 1993, 1995, 2000). Finally, Sibley and Etnier (2003) conducted a meta-analysis of studies on the relationship between physical activity and cognition in children; these studies included a variety of physical activities and a variety of cognitive assessments. A significant positive relationship was found between physical activity and cognitive functioning; the largest effects for cognitive assessment were seen specifically with perceptual skills tests. Key Point Exercise increases metabolic substances in the brain that can help build new neurons, especially in brain areas that are important to learning and memory. Imagine you are a teacher. As we learn more about the connection between movement and learning, would your approach to instruction include teacher- centered activities that require students to simply follow directions or more student-centered activities such as movement exploration? Why? Even with their common link to perceptual development, motor development and cognitive development have been studied separately for decades, if not centuries, and have been treated as distinct systems. Their conceptualization mimicked a belief that the brain centers for thought and the brain centers for controlling movement were more separate than, as we now know, they really are. Our thinking has been colored by this view. Momentum is building to approach the study of the cognitive, perceptual, and motor systems in more integrated ways, and we hope this approach will allow us to one day better understand the nature of the links between them. Key Point Motor development and cognitive development appear to be fundamentally interwoven. 405 For now, the ecological view of development embraces the notion of a close link between perception and action (Kellman & Arterberry, 1998). Ecological developmentalists believe that the task of starting with very little perception and poor motor control and matching them through trial and error is too monumental for infants to achieve in a matter of months. Instead, the ecological view holds that the newborn infant perceives the environment and many of its properties before the onset of purposeful movements. Thus, the infant has a somewhat limited perception, which guides a movement, which in turn generates additional perceptions. The cycle is repeated and the infant eventually refines perception. This sequence is termed a perception–action loop (Gibson, 1966, 1979). The difficulty with this view is that we don’t observe behaviors in infants that appear to be perception–action loops. Developmentalists do not yet know whether we simply have not found a way to measure the behavior or whether these loops do not exist. Thinking back to our discussion of recent research in perceptual development, a slightly different view emerges: Perception develops ahead of movement skills. In infancy, new motor skills are acquired with guidance from the information obtained through perception. New actions in turn make new information available, and perceptual exploration is further refined (Kellman & Arterberry, 1998; von Hofsten, 1990). With this current perspective in mind, we now examine self-produced locomotion and its role in the refinement of perceptual abilities. Self-Produced Locomotion If action facilitates perceptual development, then some types of perception would be evident only after an infant has begun performing a specific action. Researchers typically have observed perception in infants of the same age but with varying locomotor experience so that differences would be related to experience and not age. As mentioned earlier, researchers must use research paradigms in which experience varies naturally or use animal studies in which they can control conditions. In 1963, Held and Hein studied early motor activity in kittens. These researchers restricted the movements of some newborn kittens and permitted others to move. They kept the visual experience identical for all the kittens by placing them in pairs in a merry-go-round apparatus. One of the pair was harnessed but could walk around (active kitten), whereas the other was restricted to riding in a gondola (passive kitten) (figure 11.1). The passive kittens later failed to accurately judge depth and failed to exhibit paw placing or eye blinking when an object approached. Evidently, self- produced movement is related to the development of behavior depending on visual perception. There is also evidence of more brain growth and more efficient nervous system functioning in young animals when researchers provided them with perceptual-motor stimulation over and above the norm (Williams, 1986). 406 Figure 11.1 The apparatus Held and Hein used for equating motion and consequent visual feedback for an actively moving (A) and a passively moved (P) animal. Reprinted by permission from Held and Hein 1963. Key Point Animal studies tend to support the notion that movement is necessary for normal perceptual development. The visual cliff studies described in chapter 10 suggest that depth perception is present early in life. Other studies have suggested that avoidance of heights develops between 6 months and 1 year as a result of self-produced locomotor experience. Bertenthal, Campos, and Barrett (1984) found that prelocomotor infants given artificial locomotor experience by use of a baby walker (a seat in a frame that has wheels) responded to heights, whereas infants of the same age but without this artificial locomotor experience did not. In addition, one infant whose locomotor skills were delayed as a result of wearing a heavy cast did not respond to the visual cliff until self-produced locomotion began. Finally, infants who averaged 41 days of creeping experience were much more likely to avoid the visual cliff than were infants with 11 days of experience, even at identical ages. Thus, self-produced locomotion appears to facilitate development of depth perception. Once infants learn to avoid the visual cliff drop-off when crawling, they maintain the avoidance when learning to walk (Witherington, Campos, Anderson, Lejeune, & Seah, 2005). Imagine you’re a therapist working with a toddler who cannot walk. What 407 activities could you do that would facilitate the child’s perceptual development? Kermoian and Campos (1988) also investigated the link between infants’ self-produced locomotion and their perception of spatial relationships by studying the infants’ strategies in searching for objects. They gave infants a set of progressively more difficult searching tasks (called object permanence tasks) ranging from retrieving a half-hidden object to retrieving objects under one of several cloths after the passage of time. Three groups of 8.5- month-old infants performed the tasks: 1. Prelocomotor infants 2. Prelocomotor infants with walker experience 3. Locomotor (creeping) infants The more locomotor experience infants had, the better they scored. Other studies support the suggestion that locomotor experience facilitates development of spatial perception. Lockman (1984) found that a basic ability to detour around a barrier is present in 12- month-old infants. By testing infants longitudinally starting at age 8 months, Lockman identified a sequence of improvements in spatial perception: Infants first learn to retrieve an object hidden behind a cloth; they become aware that objects still exist even if they are hidden behind a barrier. Some weeks after developing this ability, infants can reach around a barrier to obtain their goal. Infants can move themselves around a barrier to obtain their goal. On average, several weeks pass between success in reaching around a barrier and success in traveling around it. Most infants can successfully detour around an opaque barrier before they can travel around a transparent barrier. Transparent barriers initially puzzle infants because visual and kinesthetic (tactile) cues conflict. Spatial perception is the perception that enables one to deal effectively with spatial properties, dimensions, and distances of objects and object relations in the environment. Thus, infants are able to deal with spatial relationships at increasing distances from their bodies. McKenzie and Bigelow (1986) further demonstrated that infants become more efficient by taking the shortest path around a barrier (figure 11.2) and that they can better adapt to a relocated barrier by 14 months of age. Hence, with increasing movement experience in the environment, infants perceive spatial relationships even at a distance from their bodies. Web Study Guide 408 Observe the developmental status of spatial perception in an infant in Lab Activity 11.1, Development of Spatial Perception, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 409 Figure 11.2 Room layout for a detour task. Infants can take the most efficient route around a barrier to their mothers (whom they can see over the barrier) by 14 months of age. By this age, they can also adapt when the barrier is relocated against the left wall. Younger infants usually take less efficient routes, such as approaching the barrier, then traveling along it, sometimes turning the wrong way and backtracking, or going to where the opening was before the barrier was relocated. Another line of research involves perception of surfaces. In these studies, researchers were interested in how locomotor experience influences the actions of infants when presented with different surfaces. For example, Gibson et al. (1987) presented infants with crawling experience and walking experience with a rigid surface (cloth over plywood) and a “deforming” surface (cloth over a waterbed). All of the infants traversed the surfaces, but the walkers hesitated to cross the deforming surface. They first stopped to explore the deforming surface, through both vision and touch, and eventually crossed the deforming surface by crawling. When presented the opportunity to cross either surface (rigid or deforming), the crawlers showed no preference but the walkers chose the rigid surface. Key Point Locomotor experience facilitates depth and spatial perception. Key Point Locomotor experience facilitates perception of surface texture and slope. Adolph, Eppler, and Gibson (1993) also noted that walkers were more sensitive than crawlers to surface slopes. Crawlers, with less locomotor experience, almost always 410 attempted to crawl up and down slopes even if the slopes were too steep for them. Walkers again tended to explore the surface, by patting it with their hands or feet or by stepping onto the sloped surface and rocking back and forth over their ankles. All walked up slopes of 10°, 20°, 30°, or 40°. They often refused steep descending slopes or used another form of locomotion, such as crawling down backward. Thus, the walkers had enough experience with surface slopes that they could immediately perceive which slopes did not afford walking. They quickly chose another form of locomotion appropriate for the surface slope. Perception of Affordances Recall that the ecological view of perception and action is based on direct perception of the environment rather than indirect perception. Indirect perception is derived from an assessment of environmental characteristics, cognitive calculations, and projections based on those characteristics. Consistent with the notion of direct perception, developmentalists with an ecological perspective believe that we directly perceive what the objects and surfaces in the environment permit us to do, given our own capabilities. That is, we perceive affordances (see chapter 2). Affordances are the actions or behaviors provided for or permitted to an individual by the places, objects, and events in and of an environment. Key Point Affordances incorporate our body scale, which is our size relative to the environment. Stair climbing provides a good example of an affordance. A set of stairs with an 8 in. (20 cm) rise between steps does not afford alternate-step climbing for an 18-month-old, as it does for an adult. A 24 in. (61 cm) rise does not afford alternate-step climbing for the average adult. As an individual grows and develops, her perception of affordances might change as her action capabilities change, even though an object’s physical properties remain the same. Taking action, then, is a critically important aspect of the development of the perception–action system. Interaction with the environment is valuable for the perception of affordances. If we perceive affordances rather than object characteristics, then individuals must be sensitive to the scale of their bodies. For example, perhaps individuals must be sensitive to their leg length in order to judge the “climbability” of any set of stairs. Warren (1984) tested this notion with adults and found that individuals perceived stairs with a riser height of more than 88% to 89% of their leg length to be “unclimbable” by means of alternate stepping. This model did not apply to older adults, whose affordances for stair climbing 411 related more to strength and flexibility than to leg length (Konczak, Meuwssen, & Cress, 1988, cited in Konczak, 1990). Nor did the model apply to infants and toddlers. Infants in a study chose smaller step heights than did toddlers, but no anthropometric measurements related to the choice of step height (Ulrich, Thelen, & Niles, 1990). Imagine you are a parent or a therapist in an average home environment. Aside from stairs, what actions do certain objects or structures afford? How do these differ for older children, older adults, or individuals with disabilities? Key Point If we perceive affordances rather than object characteristics, then the size of one’s body in relation to environmental objects plays a role in that perception. Conversely, the work of Gibson et al. (1987) and Adolph, Eppler, and Gibson (1993), described earlier, indicates that infants can perceive affordances. Infants seem to remember the results of their own previous actions with surfaces and slopes. Also, Bushnell and Boudreau (1993) found that infants can perceive the properties of objects in the following order: size and temperature, texture and hardness, and weight and shape. These findings are consistent with infants’ manual abilities to explore objects. Perception of size and temperature requires only clutching, whereas perception of texture and hardness requires rubbing and poking, and perception of weight and shape requires finger, hand, and arm movement. This is the same order in which these manipulations are acquired. The perception and the action proceed together. Lockman (2000) suggested that infant tool use has its origins in perception–action routines repeatedly used by infants during the first year (figure 11.3). Through trial and error, infants gradually explore forms of tool use, relating objects to other objects and surfaces. In so doing, infants detect affordances. These are not affordances of individual tools, but affordances of the relationships between objects, marking a sharp contrast with the approach previously taken on infant tool use. In this still-prevailing perspective, tool use is assumed to be discontinuous, and the initial use of a tool is assumed to reflect a new level of representational thinking by the infant—an insight, so to speak. In the perception–action perspective, on the other hand, potential relationships between objects are detected from information directly perceived in the environment. Tool use depends on properties of both the tool and the surface or another object. So, trial-and-error tool use can be viewed as self- generated opportunity for perceptual learning (Lockman, 2000). 412 Figure 11.3 The ability to reach, grasp, and control objects provides an infant with the opportunity to explore relationships with and detect affordances of these objects. Individuals may use many types of body scales, and the important scales may change throughout life. Perhaps changes in a person’s various body systems influence which scale he or she uses. Our sensitivity to body scaling has implications for skill instruction. For example, if a child cannot swing a big, heavy, adult tennis racket with one hand, the child cannot use adult technique in the sport. The large racket does not afford the adult style of movement. Either the racket must be scaled down to fit the child or the child might need to use two hands to swing the racket. Gagen, Haywood, and Spaner (2005) found that strength as well as size might be an important consideration in scaling equipment for ballistic tasks. Key Point Infants have an increasing ability to detect affordances. Body scaling helps us appreciate how important it is for those interested in motor development to understand the course of growth and aging. Continued research is necessary to determine the reference scales that individuals use for particular tasks. Such research would help determine whether it is indeed an affordance or individual characteristics of objects that are perceived. If you were an early childhood or elementary physical education teacher, how 413 would you use knowledge of body scales to select equipment for your students? Evidence indicates that movement facilitates continued perceptual development. Recall the earlier discussion of neurological development. Greenough, Black, and Wallace (1987) hypothesized that, initially, an excess number of synapses forms among neurons. With continued development, some survive whereas others do not. Connections that are activated by sensory and motor experience survive; unused connections are lost. Synaptic proliferation prepares an organism for experiences—presumably the experiences common to all members of a species. Undergoing experiences during this sensitive period of development both promotes survival of the synaptic connections and strengthens those connections (Ratey, 2008). Key Point A neurological basis exists for positing the necessity of movement experience to perceptual development. These theories need more experimental verification, but they provide a plausible explanation for the role of action in perceptual development (Bertenthal & Campos, 1987). They also imply that deprivation of action experience puts an individual at risk of deficient perceptual development. Another way of looking at the interplay of perception and action is to examine the development of postural control and balance. Action must be coupled with perception so that individuals can deal with events or movements that disturb their posture and balance. 414 Postural Control and Balance Postural control and balance are perfect examples of perception and action as an ecosystem. To control our posture in order to sit, stand, or assume any desired position, we must continually change our motor response patterns according to the perceptual information that specifies the environment and our bodies’ orientation in it. Several perceptual systems are involved in maintaining posture and balance. Vision tells us how our bodies are positioned relative to the environment. Kinesthetic input from our bodies’ proprioceptors tells us how our limbs and body parts are positioned relative to each other. Kinesthetic input from the vestibular system provides information about our head position and movement. Even the auditory system can contribute information about balance (Horak & MacPherson, 1995). We must maintain posture and balance in an almost infinite number of situations. Sometimes we balance when stationary (static balance) and sometimes when moving (dynamic balance). We must also balance on a variety of body parts, not just two feet. Think of all the body parts on which gymnasts must balance in their various events. Sometimes we need to balance on surfaces other than the ground, such as a ladder. We might even have to balance without all the information we would like—for example, when we have to walk in the dark. Given the number of perceptual systems involved in balance and the wide range of environmental and task constraints that are possible for any given balance task, the triangular model of constraints provides a good perspective on the development of balance. A developmental trend for a certain set of task and environmental constraints might differ from the trend for another set of constraints. In fact, movement scientists recognized some time ago that performance levels on various types of balancing tasks are specific to that task (Drowatzky & Zuccato, 1967). We discuss postural control and balance in infants in chapter 5. Now let’s consider the development of balance in childhood through older adulthood. Key Point The timing of developmental trends in balance is related to the type of balance task under consideration. Balance in Childhood Balance performance improves on a variety of balance tasks from 3 to 19 years of age 415 (Bachman, 1961; DeOreo & Wade, 1971; Espenschade, 1947; Espenschade, Dable, & Schoendube, 1953; Seils, 1951; Winterhalter, 1974). The exact nature of the improvement trend depends on the task. For example, on some tasks we might see a plateau in performance for several years. This could reflect the way we measure improvement on that particular task; perhaps the child is improving in a way not detected by our measurement. It is also possible that children begin to rely more on kinesthetic information and somewhat less on visual information for balance. Children 4 to 6 years of age have been observed to regress on moving platform tests, and children 3 to 6 years have shown both adultlike and nonadultlike postural responses to a moving room (Schmuckler, 1997). They take longer to respond than younger children and vary greatly in the way they respond (i.e., in how the various muscles are activated to regain balance). This finding does not seem to be accounted for by changes accompanying physical growth (e.g., changes in limb and trunk proportion and mass), which leads to the suspicion that shifts in reliance on different perceptual systems are perhaps involved (Woollacott, Debu, & Mowatt, 1987). By the time children reach the 7- to 10-year-old range, however, they show adultlike postural responses (Nougier, Bard, Fleury, & Teasdale, 1998; Shumway-Cook & Woollacott, 1985; Woollacott, Shumway-Cook, & Williams, 1989). Key Point As children grow, they rely more on kinesthetic information for balance and less on visual information. Balancing during locomotion is a challenging task. When we walk or run, for example, we must maintain our stability yet propel the body forward in order to travel. To do so, we probably use two frames of reference. One is the supporting surface, and the other is gravity. Another challenge is to control the degrees of freedom of movement at the various body joints. On one hand, individuals might stabilize the head on the trunk in order to minimize the movement they must control. On the other hand, they might stabilize head position in space and use the orientation of the head and trunk to control their equilibrium. Assaiante and Amblard (1995; Assaiante, 1998) proposed a model to explain the development of balance in locomotion over the life span. The model describes four important periods. The first covers birth to the onset of standing and is characterized by a cephalocaudal direction of muscle control. The second includes the achievement of upright stance to about 6 years of age; during this time, coordination of the lower and upper body must be mastered. The third period, from about age 7 to sometime in adolescence, is characterized by the refinement of head stabilization in balance control. The fourth and last period, which begins in adolescence and extends through adulthood, is characterized by refined control of the degrees of freedom of movement in the neck. Thus, the task of 416 childhood is to learn how the different frames of reference complement one another during movement. This is an intriguing model that may well stimulate future research on the development of dynamic balance. Think of your own experience in visiting an amusement park attraction or “haunted house” that used visual displays to confuse you. How did the visual display conflict with your other senses? Which of your systems were put into conflict? How did you maintain your balance (if you did)? Balance Changes With Aging In adulthood, individuals standing on a force platform (see the “Assessing Balance” sidebar) show a minimal amount of sway. If the platform is moved repetitively back and forth, adults use visual information to stabilize the head and upper body, and the muscle response to movement occurs in the ankles (Buchanan & Horak, 1999). When adults stand on such a platform and it is moved slightly or slowly but unexpectedly, they use an ankle strategy to regain balance. That is, they use lower leg muscles that cross the ankle joint to bring themselves upright once again. When the movement is larger or faster, a hip strategy is used; muscles crossing the hip and knee joints bring the center of gravity back over the base of support (Horak, Nashner, & Diener, 1990; Kuo & Zajac, 1993). Older adults experience a decline in the ability to balance. Those over 60 sway more than younger adults when standing upright, especially if they are in a leaning position (Hasselkus & Shambes, 1975; Hellebrandt & Braun, 1939; Perrin, Jeandel, Perrin, & Bene, 1997; Sheldon, 1963). Age-related changes in balance are also seen with older adults on movable platform tests. In comparison with young adults, slightly more time passes before an older adult’s leg muscles respond after a perturbation in order to maintain balance, and sometimes the upper leg muscles respond first instead of the lower leg muscles, a pattern opposite that found in young adults. The strength of the muscles’ response is more variable among repetitions in older adults (Perrin et al., 1997; Woollacott, Shumway-Cook, & Nashner, 1982, 1986). Age-related changes in balance ability could be related to a variety of changes in the body’s systems, especially in the nervous system. As mentioned previously, some older adults experience changes in the kinesthetic receptors, and these changes might be more extreme in the lower limbs than in the upper ones. Older adults might also be placed at a disadvantage due to vision changes as well as changes that occur in the vestibular receptors and nerves in adults over 75 (Bergstrom, 1973; Johnsson & Hawkins, 1972; Rosenhall & Rubin, 1975). A decrease in fast-twitch muscle fibers or a loss of strength could hamper an older adult’s quick response to changes in stability, as might arthritic conditions in the 417 joints. Assessing Balance Balance can be assessed in many ways, both in field settings and in laboratories. Different assessments are used for static balance and dynamic balance. A device used in many laboratory settings is a force plate or force platform. A simple force plate consists of two square plates positioned one over the other with four pressure gauges in between the plates and at the four corners of the plates. The device is placed on the floor, or even set into the flooring to be level with the surface, so that individuals can stand on, walk across, or even jump on or off the plate. The most basic force plate simply measures vertical force applied in the geometric center of the top plate. More complicated force plates can measure force at, and the location of, a center of pressure. Think about an individual standing on a force plate on one leg. As he or she sways and the body varies from perfect vertical, the center of pressure exerted on the force plate moves. The force plate can detect the location of the center of pressure, how far it moves, and when. The most advanced force plates can break the vector of the force exerted on the force plate into three spatial components. So, force plates can measure changing pressures under the feet as someone stands on or moves across the platform. When researchers are interested in static balance, they can ask individuals to stand on a force plate and measure both how far the individual sways and the velocity of sway. Individuals can stand on one foot or two feet and in any kind of stance, such as side stride or split stride. Researchers also can ask individuals to lean as far as possible without losing balance to quantify the ability to control the body or the maximum limits of stability. Computerized dynamic posturography assessments incorporate a force plate. These devices are used to study the reaction of individuals to being slightly thrown off balance by virtue of the force plate tilting. By controlling the surrounding visual field with a three-sided enclosure around the participant, researchers can present conditions with normal vision or no vision and with a surround that is stable or that moves as the individual sways. Researchers can also control whether the force plate moves, and in what direction, by rotating or translating. This allows them to study the visual, vestibular, and somatosensory systems and their interactions in balance, including when balance is perturbed and the person must react to regain or maintain balance. Electromyographs can be used in conjunction with systems such as this to record how the muscles are activated to regain balance. 418 Perrin et al. (1997) recorded electromyograph activity in older adults during a backward tilt of a movable force platform. They observed some of the reflexes in the lower legs that were not involved in balance control, as well as the responses necessary for regaining balance. By comparing the time from the balance perturbation with the onset of each of these muscle responses in young and older adults, the investigators determined that nerve conduction speed in both the peripheral and central nervous systems was slower in the older adults. Thus, the declines in balance performance with aging most likely are associated with age- related changes in a variety of systems. Key Point The difficulties that older adults experience with balance most likely reflect changes in more than one system. Falls are a significant concern in older adults. In fact, falls are the leading cause of accidental death for people over 75 years old. A common result of falling, especially among older adults with osteoporosis, is fracture of the spine, hip (pelvis or femur), or wrist. Complications of such a fracture can result in death. Even when older adults recover, they experience heavy health care costs, a period of inactivity, and dependence on others. A fear of falling again can make them change their lifestyles or be overly cautious in subsequent activities. Woollacott (1986) studied the reaction of older adults when a movable platform tipped forward or backward to perturb their balance unexpectedly. Half the older adults she observed lost their balance the first time, but these adults learned to keep their balance after a few more tries. Thus, older adults are more liable to fall on a slippery surface than young adults but are capable of improving their stability with practice. Campbell et al. (1997) and Campbell, Robertson, Gardner, Norton, and Buchner (1999) compared the number of falls over a 1-year period in women over 80 years of age who participated in an individualized exercise program stressing strength and balance with the number of falls in women over 80 who did not participate in an exercise program. The number of falls in the exercise group (88) was significantly lower than the number of falls in the other group (152). Prevention and rehabilitation programs, then, are useful in reducing the risk of falls in older adults, but they must be ongoing. In chapter 15, we discuss the role of aerobic exercise in maintaining the speed of cognitive processes. Web Study Guide 419 Test several people on balance tasks and rate the balance tasks in Lab Activity 11.2, Development of Balance, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Key Point Exercise programs focused on improving strength and balance can reduce the risk of falls in older adults. Imagine you have a grandparent who comes to live with you. What types of surfaces and conditions around your house could be more likely to lead to falls for him or her than for a young adult? What steps could you take to reduce the chances of a fall? 420 Summary and Synthesis Perception and action are an ecosystem. Actions are coupled to perceptions, as shown by postural and balance responses. There is some disagreement about the exact role of action in the development of perception but little disagreement over its importance. Experience with movement has been shown to facilitate perception of space, including depth, surfaces, and slopes. Perception–action coupling for posture and balance is evident in young infants. However, it appears that a developmental trend determines which perceptual system gets priority. Young infants depend more on visual information when it conflicts with kinesthetic information. With advancing development, this finding is reversed. Older children, youths, and young adults rarely fall when placed in an environment in which vision and kinesthesis conflict. They have learned to rely more on kinesthetic information. Older adults show changes in their responses to balance perturbations. Although changes in the perceptual system could affect these responses, the changes seem to occur more in the timing and pattern of the muscle responses to perceptual information. Thelen (1995) summed up the relationship of perception and action: “People perceive in order to move and move in order to perceive. What, then, is movement but a form of perception, a way of knowing the world as well as acting on it?” Reinforcing What You Have Learned About Constraints Take a Second Look Think about the dilemma presented at the beginning of this chapter. It has always been hard for parents and teachers to know when to push children to take on certain learning tasks and when to allow children to put off those challenges. On one hand they want their children to be successful, but on the other they are afraid their children will be behind others in their development. Recent research appears to show that cognitive, perceptual, and motor systems develop together. If development in one system falls behind development in the others, we know from the model of constraints that it can become a rate limiting system that holds back the others. Hopes for artificially controlling the rate of development are generally met with failure. What parents and professionals can do is manipulate the environment and manipulate task goals to set the stage for advances in development when individuals are ready to make those advances. Test Your Knowledge 1. What are some reasons that contemporary researchers think cognitive and motor 421 development are more intertwined than previously thought? 2. What seems to be the role of experience with self-produced locomotion in the development of perception? Which aspects of perception are most affected? 3. Considering your answer to the preceding question, what would be the repercussions for perceptual and motor development of depriving an infant of locomotor experience? 4. How did views of perceptual-motor development change in the 20th century? 5. What is an affordance? What does the notion of affordance have to do with adapting equipment size to the size of a performer? 6. On what perceptual systems do children rely for balance, and how does this change with development? 7. What changes in various body systems might lead to a higher frequency of falls in older adults? What might reduce the risk of falling? Learning Exercise 11.1 Cognitive and Motor Deficits Choose one of the following disorders: autism, dyslexia, developmental coordination disorder, or attention-deficit/hyperactivity disorder. Research the characteristics of the disorder to determine whether both cognitive and motor deficits are commonly associated with it. Describe specific characteristics of deficits in each area (cognitive and motor), as appropriate. What accommodations would be necessary for a child with this disorder in a regular physical education class offered at the elementary school level? 422 Part V Functional Constraints to Motor Development Part V focuses on the effect of the sociocultural environment—for example, constraints that exist as a function of family influences or cultural belief systems—on individual functional constraints. Functional constraints can include motivation, attitude, self-concept, perception of a gender role, and knowledge about a topic. Especially when working with children, we must wait for individual structural constraints to undergo change through growth and maturation. In contrast, functional constraints can change more quickly. For example, a teacher could set a task goal that interacts with an individual’s high level of motivation to bring about a rather rapid change in motor behavior. Not all functional constraints, however, change quickly. Self-concept is shaped over many years and is influenced by social interactions, and knowledge about a sport is often acquired over several years of experience in playing that sport. Functional constraints and environmental constraints interact richly. For example, sociocultural norms strongly influence perceived gender roles; in turn, one’s perceived gender role influences the types of physical activity that one views as appropriate. Even the popularity of certain sports or dance forms in a culture can influence the knowledge that someone living in the culture possesses about that sport or dance. This part of the text examines functional constraints and the changes they undergo over the life span. Chapter 12 considers the role of society and culture in influencing individuals’ choice of physical activities and play environments. Chapter 13 discusses how environmental constraints influence the functional constraints of self-esteem and motivation and how that interaction influences choices about physical activities. Chapter 14 explores how the amount of knowledge people acquire about a particular activity influences their play and participation in that activity. 423 Suggested Reading Coakley, J. (2007). Sport in society: Issues and controversies (9th ed.). St. Louis: McGraw- Hill. Giuliano, T., Popp, K., & Knight, J. (2000). Footballs versus Barbies: Childhood play activities as predictors of sport participation by women. Sex Roles, 42, 159–181. Greendorfer, S.L. (1992). Sport socialization. In T.S. Horn (Ed.), Advances in sport psychology (pp. 201–218). Champaign, IL: Human Kinetics. Heywood, L. (1998). Pretty good for a girl. New York: Free Press. Lorber, J. (1994). Believing is seeing: Biology as ideology. Gender and Society, 7, 568–581. Parish, L.E., & Rudisill, M.E. (2006). HAPPE: Promoting physical play among toddlers. Young Children, 61(3), 32. Valentini, N.C., & Rudisill, M.E. (2004). Effectiveness of an inclusive mastery climate intervention on the motor skill development of children. Adapted Physical Activity Quarterly, 21, 285–294. 424 Chapter 12 Social and Cultural Constraints in Motor Development The Effects of Environmental Constraints 425 Chapter Objectives This chapter discusses the role of sociocultural constraints in motor development; defines the role of specific social agents, such as parents and schools, in individual development; and explains the socialization process and how it differs for various groups. 426 Motor Development in the Real World Looking Past the Skirts in U.S. Field Hockey Most citizens of the United States, if asked to conjure up a group of athletes playing field hockey, would envision women in plaid skirts running around a field whacking a ball with curved sticks. Fans with this picture in their heads might have been surprised to see Andrew Zayac and Jon Geerts playing at the 2006 National Field Hockey League championship or Cornelius Tietze playing at the 2010 Pennsylvania high school, PIAA Field Hockey Championship. The vast majority of field hockey players are women, but Title IX legislation allows men to participate as well. Zayac and Geerts played on the team from the University of Maryland at College Park, which took home the 2006 club crown, and Tietze played on Wyoming Seminary’s AA championship team. However, people living Pakistan, India, or the Netherlands might be more surprised by the scarcity of men on the field; in those and other countries, the majority of athletes playing field hockey are men. In fact, both Geerts and Tietze grew up in Europe playing field hockey. Clearly, both males and females can play field hockey. When growing up in a particular culture, people assign an “appropriate” gender (e.g., the notion that field hockey is for girls) to different sports. When selecting sports in which to participate, people are influenced by the culturally specific gender associations of specific sports. Field hockey is a perfect example. No one would think twice if a boy played field hockey in Europe; in the United States, however, only the occasional boy plays. When we think of this phenomenon in terms of constraints, we can see that no individual constraints discourage or prevent boys (as a group) from playing field hockey. The constraints have more to do with the social or cultural environment. Society as a whole influences the activity choices that individuals make, and girls and boys alike benefit from the addition of new athletic role models. When social or cultural factors influence the types of physical activity in which people get involved, those factors act as sociocultural constraints. You might not have considered sociocultural constraints as important to motor development, but in this chapter you will discover that these ever-present constraints can have a great influence on motor behavior throughout the life span. The idea that social and cultural aspects can influence motor development may come as a surprise to a maturationist. If you believe that genetics determines development, then you would be hard pressed to think of society as a developmental agent. However, those who follow an ecological perspective believe that social and cultural influences (in the form of environmental constraints) may greatly influence and interact with individual and task 427 constraints. This means that media coverage of events such as women’s wrestling and ice hockey may encourage participation in these sports by changing some of the social and cultural stereotypes associated with females in sport. 428 Social and Cultural Influences as Environmental Constraints In chapter 1, we introduce the idea of sociocultural influences as environmental constraints —the idea that sociocultural attitudes of groups of people either encourage or discourage certain motor behaviors. These factors are considered environmental constraints because they reflect a general attitude or belief system present either in society at large or in certain subcultures. If such attitudes are pervasive enough, they can modify someone’s behavior. They may not be obvious at all, yet they may still exert a powerful influence on how individuals move. Just as temperature and ambient light can fill a room, field, or community, so can attitudes, values, norms, and stereotypes envelop us. Even as late as the 1970s girls were not expected, or in some cases even allowed, to participate in some organized sports, such as baseball and ice hockey. This attitude about girls in sport meant that the opportunity to play organized and even pickup sports was limited. In essence, this attitude discouraged sport participation for many girls, especially after puberty. However, the passage of Title IX (requiring equal opportunity for girls and women in sport) in 1972 drastically changed the landscape of sport in the United States, making it more possible and, in time, socially acceptable for girls and women to participate in sport. Society and culture can have a profound effect on an individual’s movement behaviors, particularly in the area of sport and physical activity (Clark, 1995). Sociocultural elements such as gender, race, religion, and national origin can all direct one’s future movement behavior (Lindquist, Reynolds, & Goran, 1998). Even the media encourage and promote different types of physical activities (e.g., those that are gender specific) to mass audiences (Koivula, 2000; Messner, Duncan, & Jensen, 1993; Wigmore, 1996). A simple example illustrates how sociocultural constraints work: Think about who the most successful American athlete was during the past 10 years. Many athletes may come to mind, such as LeBron James, Tom Brady, or Mia Hamm. Most likely, you imagine a trim, muscular individual who plays a professional sport in the United States. However, if we asked this question of someone from Japan, he or she might picture a sumo wrestler (figure 12.1). Most Americans would not even know the names of sumo wrestlers. More important, in the context of motor development, most American children would not aspire to become sumo wrestlers and most American adults would not attempt to participate in sumo wrestling, regardless of individual constraints such as body type (which may actually encourage participation in some cases). In the United States, Sumo wrestling is not encouraged as a sport. Thus, society and culture influence the choice of sport or physical activity in which one participates; that is, they act as environmental constraints, encouraging certain movement activities while discouraging others. The chances that a young American boy or girl would pursue a career in sumo wrestling are slim. As a result, an entire group of movements (those associated with sumo wrestling) are discouraged and 429 may never be performed; over time, this constraint interacts with individual constraints to limit or even prevent the emergence of these movements. Key Point Societal and cultural beliefs, attitudes, and stereotypes can encourage or discourage motor behaviors. These are ever-present environmental constraints. 430 Figure 12.1 While quite popular in Asian countries, sumo wrestling is not followed intently in America. Participating in physical activities contributes to motor development. The benefits of experience in physical activity are well known and include improved physical and emotional health. In addition, sport may influence the behavioral patterns of participants (e.g., by teaching leadership and other skills). Thus, it makes sense to provide opportunities for all people to participate in sport and physical activity from an early age and throughout the life span. However, decisions to participate in sport or maintain a physically active lifestyle can have as much to do with the social milieu as with individual constraints. For example, the individual constraints of most typically developing American children allow them to participate in a wide variety of activities, yet many choose computer and video games and television over playing outdoors. Why? What are some of the most important social and cultural elements (people, places, and so on) that have influenced you during your life? How have these influences changed from the time you were an infant to the present? An individual’s early socialization in sport and physical activity is a key factor in motor development and the likelihood of later participation. People and situations continue to influence individuals in their choice of activities throughout their lives. For example, your peers influence your recreational activities and lifestyle choices. These activities can be physical (pickup basketball versus video games), academic (library versus study group), and social (movies versus barhopping), among others. The socialization process—as related to sport and physical activities, including the individuals who are influential in the process— deserves attention as a major environmental constraint in one’s motor development. The process by which one learns a social role in groups with certain values, morals, and 431 rules is one’s socialization process. Three major elements of the socialization process lead an individual to learn a societal role, as shown in figure 12.2 (Greendorfer, 1992; Kenyon & McPherson, 1973): 1. Significant others (influential or important people, called socializing agents) 2. Social situations (places where socialization takes place—schools, home, playgrounds) 3. Personal attributes (individual constraints) 432 Figure 12.2 The three major elements of the socialization process that lead to the learning of a societal role for participation in physical activity. Based on Kenyon and McPherson 1973. We examine the first two of these elements to see their influence and importance in the process of socialization into sport and physical activity. Of course, the third element, personal attributes, represents an interaction between environmental constraints (socializing agents and situations) and individual constraints. Among the socializing agents are family members, peers, teachers, and coaches. This section examines the influences these people provide—and how they might encourage or discourage certain motor behaviors. First, however, we examine a cultural phenomenon that cuts across many contexts: gender typing in sport and physical activities. Key Point Significant others (e.g., parents and friends) and social situations (e.g., school or sports teams) contribute to an individual’s socialization process. Sociocultural Constraints in Action: Gender-Specific Stereotyped Behaviors Significant others enter any context with socially and culturally prescribed notions of how they and others should act. Examination of one such notion, that of gender-specific stereotyping, shows just how potent sociocultural constraints can be. In general, people are born physiologically either male or female; their biological characteristics determine their sex. In contrast, gender is a culturally determined sociological construct that differentiates between men (“masculine”) and women (“feminine”) (Eitzen & Sage, 2003). Parents and other significant socializing agents often encourage children toward what they perceive as gender-appropriate behaviors, based on each child’s biological characteristics 433 (Fagot & Leinbach, 1996; Fagot, Leinbach, & O’Boyle, 1992; Lorber, 1994). In terms of developing motor skills, significant others steer boys toward “masculine” and girls toward “feminine” sports and physical activities (Royce, Gebelt, & Duff, 2003; Shakib & Dunbar, 2004). This practice is often termed gender typing, or gender-role stereotyping. Children begin to learn these gender roles early, perhaps as early as their first year (Fagot & Leinbach, 1996). Sex refers to biological characteristics used to determine whether individuals are classified as male or female. Gender refers to culturally defined sociological characteristics used to differentiate between males and females. Gender typing, or gender-role stereotyping, occurs when a parent or significant other encourages activities that are deemed “gender appropriate.” Traditionally, Western societies gender-type participation in sport and physical activities. Certain sports (e.g., football, baseball, and wrestling) are identified as masculine and others (e.g., figure skating, gymnastics, and field hockey) are identified as feminine. In addition, Westerners consider sport to be important and appropriate for boys; however, that attitude doesn’t always carry through for girls. Therefore, adults often permit and encourage vigorous, outgoing, rough-and-tumble play for toddler boys whereas they may be more likely to discourage girls from (or even punish them for) running, climbing, or venturing away from parents (Campbell & Eaton, 2000; DiPietro, 1981; Eaton & Enns, 1986; Eaton & Keats, 1982; Fagot & Leinbach, 1983; Lewis, 1972; Liss, 1983; Lloyd & Smith, 1985; McBride-Chang & Jacklin, 1993). Society reinforces constrained, sedentary types of play for girls, and thus many girls self-select away from vigorous play (Greendorfer, 1983), which leaves a comparatively small number of girls as active participants in vigorous, skilled play. Although the number of girls participating in high school sports has drastically increased since the inception of Title IX, girls still make up less than half the overall number of participants (Stevenson, 2007). Furthermore, girls are more likely to drop out of sport and activity participation after high school. Why is the issue of gender typing important for girls and boys? For girls, such limited involvement and practice may not allow them to develop their motor skills to their full potential. Even girls who do participate may feel that all-out effort and skilled performance are gender inappropriate. This, in turn, could affect a girl’s or woman’s motivation for participating, for training, or for striving for high achievement standards that rival those of boys and men. At the same time, boys may feel forced into participating in gender- appropriate sports they dislike. Worse yet, they may drop out of physical activity altogether rather than be subjected to participating in an undesired but gender-typed sport. Thus, this pervasive societal influence on boys’ and girls’ sport participation may factor into measurements of skill and fitness that compare the sexes. This is an important consideration in the context of an integrated model of interacting constraints. What appear to be fitness or skill differences in boys and girls based on physiological makeup (sex) may 434 in fact be related to a lifetime of stereotyped influences (gender) (Campbell & Eaton, 2000). Despite a growing awareness during the 1970s and 1980s that these societal roles might limit girls’ opportunities to enjoy the many benefits of sport participation, parents did not seem to change their contact patterns with their children. Research studies in the mid- 1980s confirmed that parents still tended to interact differently with sons and daughters in play environments (Power, 1985; Power & Parke, 1983, 1986; see Williams, Goodman, & Green, 1985, on “tomboys”). For example, parents tended to direct their girls’ play but allowed boys more opportunities for independent, exploratory play (Power, 1985). Although many changes seemed to take place during the 1990s, research still indicates that boys and girls received stereotypical messages about participation in physical activity (Coakley, 1998; Eitzen & Sage, 2003). Some research from the 21st century suggests that these roles have not yet changed. In a 2003 study by McCallister, Blinde, and Phillips, middle-school-age girls were surveyed on their beliefs about and attitudes toward girls and boys in physical activity and sport. Not only did the girls studied perceive boys as more athletic and physically capable and associate being an athlete with being a male, they also perceived athleticism as a negative trait in a girl. Overall, the researchers found that traditional, stereotypical beliefs held true in this sample. Some research indicates that times are changing: In a sample of 565 university-aged students, Royce, Gebelt, and Duff (2003) found that both men and women perceived female athletes as respected. Overall, the sample studied did not see being feminine and being an athlete as mutually exclusive, suggesting a change in negative stereotypes related to women in sport. What happens to girls who defy gender-role stereotypes? Giuliano, Popp, and Knight (2000) studied 84 Division III female college students, both athletes and nonathletes. They found that as children the athletes tended to play with “masculine” toys and games, were considered “tomboys,” and played primarily with boys or mixed-gender groups, whereas the nonathletes did not. This finding suggests that, rather than harming the females in any way, these early “masculine” experiences may have encouraged them to participate in athletics and physical activities throughout college—and perhaps beyond. Given the importance of a physically active lifestyle, then, parents may wish to avoid rigid adherence to gender-typed behaviors for their daughters. Web Study Guide Examine television commercials and the packaging of toys for evidence of gender-role stereotyping in Lab Activity 12.1, Examining Gender-Role Stereotyping, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 435 Can Laws Change Constraints? Effects of Title IX on Girls’ and Women’s Sport Participation Before the passage of Title IX, far more boys than girls participated in high school athletics. In 1971, fewer than 300,000 female athletes participated in high school sports compared with almost 4 million male athletes (Reith, 2004). Some argued that boys were biologically predisposed to sport participation; that is, more boys participate in sport because their sex is an individual constraint that encourages athletics. This argument provided a biological rationale for providing boys with more athletic opportunities than were provided for girls. But was biology the reason for the vast difference in participation numbers, or was this difference socially constructed? Title IX, passed in 1972, provided a unique chance to examine this question. Title IX states, “No person in the United States shall, on the basis of sex, be excluded from participation in, be denied the benefits of, or be subjected to discrimination under any education program or activity receiving federal financial assistance.” In terms of its effect on sport, the law mandated that educational programs strive for more gender equity. (For a full description of Title IX and its effect on sport, see Acosta & Carpenter, 2008, or Reith, 2004). Times have changed since 1971. More than 7 million high school students participated in athletics between 2005 and 2006. Of these, 3 million were girls and 4.2 million were boys (Howard & Gillis, 2007; Stevenson, 2007). These numbers show that participation in high school sports has expanded for both genders. What trends have appeared since 1972 in the types of sports high school students select? It appears that more high school athletes—both boys and girls—select gender-neutral sports, whereas participation in highly gendered sports has tended to increase more slowly (football is the major exception to this trend; Stevenson, 2007). In 1972, boys played football more than any other sport, followed by basketball and track. These remained the most popular sports for boys in 2004 and 2005, although baseball and soccer were increasing in popularity. Girls participate in basketball, track and field, fast-pitch softball, and volleyball in the greatest numbers. The overall increase in athletic participation and the specific increase in participation among girls suggest that the constraints discouraging participation were not individual but rather were related to the sociocultural environment. Significant Others: People’s Values Acting as Constraints Significant others, or socializing agents, are the people most likely to play a role in an 436 individual’s socialization process—family members, peers, teachers, and coaches (figure 12.3). People who act as socializing agents should be considered constraints because they will encourage or discourage certain motor behaviors. This section examines how each of these groups might influence participation in sport and physical activity. 437 Figure 12.3 Significant others such as coaches may influence the socialization process throughoutchildhood. Significant others, or socializing agents, are family members, peers, teachers, coaches, and others who are involved in the socialization process of an individual. The roles that parents, peers, teachers, and coaches play in the socialization of children can vary with the gender of a child. In addition, the gender of the person serving as a model for behavior may differentially influence the child’s internalization of the behavior. The result of such socialization can constrain a child to very specific types of physical activities and virtually eliminate others. Family Members A person’s family has a major influence in the process of socialization into physical activities, as well as other pursuits, in part because the family’s influence begins so early in a child’s life (Kelly, 1974; Pargman, 1997; Snyder & Spreitzer, 1973, 1978; Weiss & Barber, 1996). In fact, the family may be the only source of social interaction that an infant has— and therefore the primary source of social constraints. From their very first interactions, family members expose their infants to certain experiences and attitudes. They reinforce the behaviors deemed appropriate through their gestures, praise, and rewards; at the same time, they punish inappropriate behaviors. The process is systematic, but at times it is so subtle that family members may hardly realize what and how they communicate to the infant. Parents When someone participates in physical activities after early childhood, he probably reflects his parents’ interest and encouragement during the early years. Parents can encourage children to engage in either physical or sedentary activities. This may relate to the 438 participation habits of the parent (DiLorenzo, Stucky-Ropp, Vander Wal, & Gotham, 1998). As children become physically active, parents can encourage or discourage games and, eventually, specific sports or physical activities. Parents’ early involvement could lead to a lifetime of participation in physical activities for a child (Weiss & Barber, 1996). About 75% of eventual sport participants become involved in sport by age 8 (Greendorfer, 1979; Snyder & Spreitzer, 1976). In fact, the best predictor of adult sport involvement is participation during childhood and adolescence (Greendorfer, 1979; Loy, McPherson, & Kenyon, 1978; Snyder & Spreitzer, 1976). It follows, then, that a parent’s early bias toward or away from physical activities can have lasting consequences. Although Title IX may be opening new doors for girls in terms of parental expectations for gendered activity, things might not have changed much for boys. In a 2007 study, Kane determined that parents perceived gender nonconformity in their preschool children as primarily positive—but only within a limited range for sons. At the same time, the parents in this study reported that it was important for their sons to conform to normative standards of masculinity. Individual parents may play different roles in socializing children into physical activity. Snyder and Spreitzer (1973) proposed that a child’s same-sex parent is most influential in the extent of that child’s sport involvement. Further, McPherson (1978) suggested specifically that mothers serve as sport role models for their daughters. This notion was supported by DiLorenzo et al. (1998), who found that for eighth- and ninth-grade girls, their mothers’ physical activity and support were predictors for physical activity. Some researchers believe that fathers more strictly reinforce gender-appropriate behavior, which would include sport participation for boys (Lewko & Greendorfer, 1988). Greendorfer and Lewko (1978) identified fathers rather than mothers as a major influence in the sport involvement of both boys and girls. In contrast, Lewko and Ewing (1980) found that fathers influenced boys between the ages of 9 and 11 years who were highly involved in sport and that mothers influenced highly involved girls. In order to become involved, girls seemed to need a higher level of encouragement from their families than boys did, and they needed it from many members of the family. A similar pattern was found in Japanese children (Ebihara, Ikeda, & Miyashita, 1983). Although the research does not definitively indicate a differential role for mothers and fathers, parents clearly influence and affect the choices their children make in physical activities. During the 1980s and 1990s, girls’ and women’s participation in sport and physical activity became more recognized and widespread. Furthermore, it has become far more socially acceptable for women to participate in sports (e.g., soccer and ice hockey) that have been gender-typed as male in the past. For example, in 2006 and 2007, more than 5,000 high school girls participated in wrestling, more than 4,000 participated in flag football, and more than 7,000 participated in ice hockey (Howard & Gillis, 2007). Sports leagues for both genders have begun to include divisions for adults of different ages and skill levels, reflecting an increased interest in sport past high school and college. In addition, more opportunities exist for women and men to participate in nonsport physical activities 439 ranging from spinning to step aerobics to cardio kickboxing. This increase in both types and opportunities of activities for parents will likely have a positive effect on the socialization of children into physical activity. Siblings Siblings form an infant’s first playgroup and thus may act as important socialization agents into physical activity. For example, both brothers (Weiss & Knoppers, 1982) and sisters (Lewko & Ewing, 1980) can influence girls’ sport participation. Studies suggest that African American boys see athletes as their role models and that their male siblings shape their idea of a role model (Assibey-Mensah, 1998). On the other hand, some children and teens report that older siblings were not important in their sport involvement (Greendorfer & Lewko, 1978; Patriksson, 1981). Thus, it is possible that for most children siblings merely reinforce the socialization pattern into physical activity established by parents rather than act as a major socializing force in themselves (Lewko & Greendorfer, 1988). How Do Race, Ethnicity, and Other Factors Affect the Role of the Family in Physical Activity Socialization? Various investigators have reached different conclusions about the influence of family members on socialization into physical activity. Greendorfer and Lewko (1978) attempted to clarify these various conclusions by questioning children from a broad range of social backgrounds. They found some differences in the patterns of significant influences. The significant other who exerted the most influence on socialization into physical activity varied somewhat among children according to sex, social background, race, and geographic location. For example, fathers were influential in socializing Caucasian American but not African American boys into sport (Greendorfer & Ewing, 1981). In contrast, Lindquist et al. (1998) found few differences in children’s physical activity based on ethnicity when they controlled for social class and family background. This finding suggests that the roles of race, ethnicity, social background, and other factors in family socialization are more complex and difficult to characterize on a group basis. In fact, this complexity supports our notion of motor development: Socially and culturally specific agents constrain motor behaviors of individuals in different ways, leading to the emergence of different motor behaviors. We should not generalize about social agents but rather keep in mind that diverse patterns of influence may exist. Peers A child’s peers have the potential to reinforce or counteract the sport socialization process begun in the family (Bigelow, Tesson, & Lewko, 1996; Brown, Frankel, & Fennell 1990; 440 Greendorfer & Lewko, 1978; Weiss & Barber, 1996). If a peer group tends to participate in active play or sport, its individual members are drawn to such activities. If the group prefers passive activities, its individual members tend to follow that lead. Adult athletes typically report that peer groups or friends influenced the extent of their sport participation when they were in school, although the strength of this influence varies by sport. The first peer group that a child encounters is typically a playgroup. Children typically become involved in such groups from a very young age (some playgroups begin when children are still in infancy) and continue in them during their early school years. Boys and girls from several countries, including the United States, Japan, and Canada, have reported that peers influenced their childhood sport participation (Ebihara et al., 1983; Greendorfer & Ewing, 1981; Greendorfer & Lewko, 1978; Yamaguchi, 1984). During preadolescence, children enter more formalized peer groups, such as cliques. These peers continue to be influential during adolescence (Brown, 1985; Brown et al., 1990; Butcher, 1983, 1985; Higginson, 1985; Patrick et al., 1999; Patriksson, 1981; Schellenberger, 1981; Smith, 1979; Weiss & Barber, 1996; Yamaguchi, 1984). In fact, among the women she questioned, Greendorfer (1976) found that the peer group was the only socializing agent that influenced sport involvement throughout all phases of the life cycle studied: childhood, adolescence, and young adulthood. Other socializing agents were important at some ages but not at others. For example, the family, so important to young children, probably is less influential for adolescents. Peers often provide a stronger influence for participation in team sports than for participation in individual sports during childhood and adolescence (Kenyon & McPherson, 1973). Children’s and adolescents’ supportive peer groups are usually made up of others of the same sex. For adults, especially women, and particularly after marriage, spouses and friends of the opposite sex become more influential in either encouraging or discouraging involvement in certain activities (Loy et al., 1978). As individuals leave school and enter new social environments as members of the workforce, they often leave their peer groups. If a peer group was sport oriented, a reduction in sport involvement might follow. On the other hand, new peer groups at the workplace could stimulate sport involvement; the individual might, for example, join a team in a recreational sport league or perhaps participate in company-sponsored exercise and recreational programs (Loy et al., 1978). Key Point An individual’s peer group may either encourage or discourage physical activities. As socializing agents, peer groups can be as important as family. It is likely that the typical middle-aged adult, even one who was involved in sport as a young adult, reduces sport involvement. A study by Ebrahim and Rowland (1996) found 441 that of 704 women aged 44 to 93 years, only 25% took part in vigorous activity during the week before the study. This trend might be due in part to a lack of programs aimed specifically at middle-aged and older adults. However, this has been changing in recent years as the emphasis on fitness that began in the late 1970s has led to greater availability of exercise and recreation programs for members of these age groups. In addition, adult participation in sport and exercise programs has become acceptable and even desirable in Western societies. Peer groups appear to be essential for adherence to exercise, so they may be part of the initial reason for joining a recreational group. Once involved, adults keep participating to be part of a peer group. Despite the strong influence of peer groups on sport participation throughout life, it is still not clear that membership in a sport-oriented peer group always precedes participation— that is, that a person is drawn to an activity because of a desire to associate with peers. It is possible that individuals first select groups that fit their interests, including an interest in sport (Loy et al., 1978). Although it is unclear which comes first, the interest in sport and the desire to be a part of a peer group make it likely that an individual will continue to participate and to select membership in active groups. Peers apparently play just as important a role in sport socialization as the family plays (Lewko & Greendorfer, 1988). Coaches and Teachers Coaches and teachers can also influence an individual’s involvement in sport and physical activity (Greendorfer & Lewko, 1978). Male athletes consistently report that coaches and teachers influenced both their participation in and their selection of sports, particularly when they were adolescents and young adults (Ebihara et al., 1983; Kenyon & McPherson, 1973). Female athletes report that teachers and coaches influenced them during childhood (Greendorfer & Ewing, 1981; Weiss & Knoppers, 1982) and adolescence (Greendorfer, 1976, 1977). In contrast, Yamaguchi (1984) found that schoolteachers and coaches were not influential. Participants rarely name teachers and coaches as the most influential agents in their sport involvement. Perhaps the role of teachers and coaches is to strengthen the sport socialization process begun earlier by family and friends. Adults who dislike physical activity often report that they had poor movement experiences, particularly in physical education, when they were children. Keeping this in mind, how could you, as a physical educator, manipulate different types of constraints to make the gymnasium a more positive learning environment? Nevertheless, teachers and coaches should not overlook their potential to influence their students’ sport involvement. They can introduce children and adolescents to exciting new activities and stimulate them to learn the skills and attitudes associated with sport. 442 Conversely, teachers and coaches must also recognize the potential they have to turn their students away from sport and physical activity. Bad experiences in school can have lifelong consequences for a person’s overall lifestyle (Snyder & Spreitzer, 1973). Such negative experiences, known as aversive socialization, can occur when teachers or coaches embarrass children in front of their peers, overemphasize performance criteria at the expense of learning and enjoyment, or plan class activities that result in overwhelming failure rather than success. Children who experience aversive socialization naturally avoid physical activities and fail to learn skills well; consequently, any attempts they make to participate frustrate and discourage them. Assessing Youth Sports Coaching Behaviors: Coaches as Socializing Agents Participation in youth sports has grown steadily over the past several decades (Smoll & Smith, 2001), and this growth has led to a demand for coaches who understand the needs of young participants. Remember, coaches can act as socializing agents for young children. Many of the feelings, values, and behaviors that people have about physical activity come from their experiences in youth sports. A good coach can facilitate a lifetime of positive experiences, just as a poor coach can drive young people away from sport and physical activity. Frank Smoll and Ron Smith (2001) suggest that youth sports coaches adopt a four- part philosophy to enhance the enjoyment and benefits of children’s participation in sport. They posit that the primary objective of youth sports is to have fun. Here are the four points: 1. Winning isn’t everything, nor is it the only thing. 2. Failure is not the same thing as losing. 3. Success is not synonymous with winning. 4. Children should be taught that success is found in striving for victory (i.e., success is related to effort). How do coaches evaluate their ability to make physical activity a fun, positive experience for kids? Coaches can understand their own coaching behaviors better through self-monitoring. To aid in this process, Smoll and Smith developed the Coaching Self-Report Form, which the coach should complete soon after each practice or game. This form helps coaches assess the frequency of desired behaviors in sport situations. Along with feedback from knowledgeable sources such as other coaches or teachers, use of the form can enhance a coach’s ability to be a positive 443 socializing agent for participants in youth sports. Coaching Self-Report Form Complete this form as soon as possible after a practice or game. For items 1, 2, and 3, think not only about what you did but also about the kinds of situations in which the actions occurred and the kinds of athletes who were involved. 1. Approximately what percentage of the time did you respond to good players’ actions with reinforcement? 2. Approximately what percentage of the time did you respond to players’ mistakes or errors with each of the following communications? 1. Encouragement only 2. Corrective instructions given in an encouraging manner (Sum of a and b should not exceed 100%) 3. About how many times did you reinforce athletes for showing effort, complying with team rules, encouraging teammates, showing team spirit, and exhibiting other good behaviors? 4. How well did your team play tonight? (Circle one.) very poorly  not very well  average  quite well  very well 5. How positive an experience for the kids was this practice or game? very negative  somewhat negative  neutral  somewhat positive   very positive 6. How positive an experience for you was this practice or game? very negative  somewhat negative  neutral  somewhat positive   very positive 7. Is there anything you might do differently if you had a chance to coach this practice or game again? (If so, briefly explain.) Reprinted, by permission from J. Williams, 2001, Applied sport psychology: Personal growth to peak performance, 4th ed. (New York: McGraw-Hill Companies). Copyright McGraw-Hill Companies, Inc. Key Point 444 It is essential that coaches and teachers understand their potential influence in promoting or deterring physical activity participation among their athletes and students. Web Study Guide Reflect on how your life was shaped by socializing agents in Activity 12.2, Significant People, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Social Situations The situations in which children spend their formative years are a part of the socialization process. Play environments, games, and the toys children use can all influence their later activities. Play Environments and Games An adequate environment for play, such as a backyard or playground, can provide the social situation and environment that a child needs in order to begin involvement in sport and physical activity. Play spaces probably also influence activity selection. A child who lacks an adequate play space has a diminished opportunity to get involved in activities and practice skills. These environmental constraints thus discourage participation in sport and gross motor activities. Children who grow up in urban areas with limited play space are typically exposed to sports and activities that require little space and equipment, such as basketball. Colder climates provide children with an opportunity to learn ice skating; warmer climates encourage swimming. Play environment may also act as a sociocultural constraint, especially if the play space has gender-associated values, which can influence boys and girls to participate in gender-typed activities. For example, double-dutch rope jumping falls in the “feminine” domain; a boy might be labeled a “sissy” for rope jumping and thus be discouraged from participating in that activity. A girl might be told that a certain sport (e.g., football) is inappropriate for girls or labeled a “tomboy” if she does participate. Western society has traditionally considered certain types of games appropriate for boys but not for girls and vice versa. This labeling is particularly apparent as children enter adolescence. The pressure to participate in gender-appropriate games has implications for children’s opportunities to practice skills. Traditional boys’ games are typically complex and involve the use of strategy; participants are encouraged to work hard in pursuit of specific goals and 445 to use negotiation to settle disputes over rules. Traditional girls’ games, on the other hand, are typically noncompetitive, and rather than encouraging interdependence among group members, they involve waiting for turns to perform simple repetitive tasks, such as jumping rope or playing hopscotch. Such games rarely give girls opportunities to increase game complexity or to develop increasingly more difficult skills. In fact, the games often end because the participants lose interest, not because they achieve a goal (Greendorfer, 1983). These days, more and more children are participating in activities that are not “gender appropriate” (Giuliano et al., 2000). Further, some sports and activities, such as soccer and aerobics, are losing their gender-specific associations. Gender typing of sports and activities still exists, however, and it acts as a strong constraint on movement activities. Educators should keep in mind that a play environment that channels boys and girls into gender- typed games perpetuates a situation in which boys can better develop complex motor skills but girls cannot. Play With Toys Imagine walking into a toy store as a child. What do you experience? Bright colors and loud sounds beckon you toward toys that promise to enlighten, engage, and excite you. As a child (or even an adult), you may not realize that these toys act as part of the socialization process. That’s right; even toys are constraints! Toys can encourage children to be active or inactive. For example, a Frisbee or Koosh ball encourages a child to throw, catch, and develop an accurate shot. On the other hand, a board game or doll encourages sedentary play. Toys can also stimulate children to emulate sports figures; among others, one can find basketball, soccer, and cheerleader Barbies as well as World Wrestling Entertainment action figures. At the same time, video and computer games may simulate sport without promoting any physical activity at all. Each kind of toy has its advantages, but certain toys facilitate children’s socialization into sport and physical activity more than others do. Consider a toy that is popular today, such as Easy Bake Real Meal Oven, Webkinz (stuffed animals with virtual counterparts in a game on the company’s website), or Nerf N-Strike Disk Shot (a shooting game that uses soft projectiles and moving targets). How might these toys encourage certain behaviors and discourage other behaviors in a child? Toys are also a means by which gender typing can occur in the socialization process. For example, toys marketed to boys tend to be more complex and encourage more vigorous activity than those marketed to girls. The typical girls’ toy, such as a doll or kitchen set, promotes quiet indoor play (Greendorfer, 1983; Liss, 1983). Gender typing through toys is well entrenched in society, and even children under 2 years old may be aware of the gender associations of toys (Levy, 2000). In a series of studies published in 2005, Blakemore and 446 Centers interviewed undergraduates to determine their perceptions of the gender suitability of certain toys and to find out how they rated the characteristics of the toys. The participants associated girls’ toys with physical attractiveness, nurturance, and domestic skill and associated boys’ toys with violence, competition, excitement, and, to some degree, danger (Blakemore & Centers, 2005). Manufacturers often use gender-typed strategies to advertise their products. For example, commercials or packaging for sports equipment, racing-car sets, and action-oriented video games feature boys, and those for dolls picture girls (figure 12.4). Watch carefully for television advertisements during daytime television —most target either boys or girls but not both. These marketing ploys influence children as well as their parents. 447 Figure 12.4 Advertising for children’s toys is often gender typed. Parents also enjoy giving their children the same kinds of toys they played with as children, thus tending to perpetuate traditional gender typing. For example, a father might buy a Lincoln Logs set for his son, remembering the hours he spent with one as a child—despite the more modern, complex, and potentially less gender-stereotyped toys on the market. Moreover, parents can promote gender typing by negatively reinforcing play with toys they judge to be gender inappropriate (Fagot, 1978), such as telling boys not to play with dolls. In a study of gender typing, toys, and preschoolers, Raag and Rackliff (1998) found that many of the boys thought their fathers would perceive cross-gender-typed toys as “bad.” Raag (1999) also found that children who had a parent or significant other who viewed gender-neutral toys as “bad” were somewhat influenced by gender-typed toy labels. Such gender typing through toys is slow to change, and there is little evidence of change over the past several decades (Blakemore & Centers, 2005; Campenni, 1999; Eisenberg, Welchick, Hernandez, & Pasternack, 1985; Lloyd & Smith, 1985; Marcon & Freeman, 1999). In a unique study, Pennell (1999), disguised as Santa’s head elf, questioned 359 males and 417 females of various ages and ethnic backgrounds about their toy choices. Pennell found that both girls and (to a greater extent) boys had strong gender-typed toy preferences. In recent years, society has become more aware of the many ways in which children are gender typed and the implications of this process. Yet there is little evidence of any substantial change away from gender typing (Banerjee & Lintern, 2000; Blakemore & 448 Centers, 2005; Pennell, 1999; Turner & Gervai, 1995; Turner, Gervai, & Hinde, 1993; Weisner, Garnier, & Loucky, 1994). Teachers must realize that they influence this aspect of socialization (Fagot, 1984). Again, the evidence shows that teachers still behave differently toward the play of boys than toward that of girls (Fagot, 1984; Oettingen, 1985; Smith, 1985). They can reinforce early gender typing by continuing to label certain activities as more important or appropriate for one sex than for the other. They can choose different activities for boys’ and girls’ achievements. Or they can make every attempt possible to eliminate such distinctions and allow each individual to explore his or her full potential. It is likely that such day-to-day decisions and expectations accumulate over time to reduce differences in boys’ and girls’ motor development by channeling their practice opportunities (Brown et al., 1990; Brundage, 1983; Giuliano et al., 2000; Greendorfer & Brundage, 1984). 449 Other Sociocultural Constraints: Race, Ethnicity, and Socioeconomic Status Earlier in this chapter we describe some constraints related to gender as socially constructed rather than biologically defined. For example, the notion that girls are weaker than boys is firmly established in Western culture even though little biological evidence backs it up. (Recall that few physiological differences exist between boys and girls before puberty.) Socially constructed notions also go beyond gender differences into the realms of race, ethnicity, and socioeconomic status (SES). Oftentimes, it’s difficult to distinguish between sociocultural constraints (e.g., prevailing cultural attitudes) and individual constraints (e.g., physiological functioning) because research may not be completely clear-cut on how race, class, or ethnicity is defined. For example, race and ethnicity are often used together, but they are not equivalent. Racial characteristics are biologically based and relate to genetic similarities within groups, whereas ethnic characteristics are culturally based and relate to cultural similarities that connect groups. Race and ethnicity can coincide (biologically similar individuals who live in a particular geographic locale likely share culture), which makes their independent study very difficult. Consider ways in which low socioeconomic status might encourage certain behaviors and discourage others. Furthermore, social class and SES are associated with certain characteristics that may cut across race and ethnicity. Given that all of these factors interrelate, it is hard to identify constraints as strictly sociocultural or individual when considering SES. It may be best to consider the relationship of SES to other factors when looking for the influence of constraints. For example, children who come from a lower SES background may have less access to organized sports and physical activities, particularly those that require expensive equipment (e.g., ice hockey) or lessons (e.g., figure skating or tennis) and extensive time commitments from at least one parent. As a result, these children may not gain experiences and practice related to these particular activities. We should consider the research on race, ethnicity, and SES from a slightly different point of view—one that examines differences among groups without suggesting a priori that differences are biological in nature. This approach allows us to examine the potential influence of a variety of constraints without limiting our interpretation to “biological fact” (which might actually be a cultural assumption). Malina, Bouchard, and Bar-Or (2004) provide an extensive review of historical and contemporary research related to physical differences based on race, ethnicity, and SES. 450 Summary and Synthesis Humans are social beings. That is, individuals constantly interact with and depend on others as a part of everyday life. People form groups that can be small (family), medium (sports teams, town membership), or large (United States citizens). These groups often feature distinct values, morals, rules, and other factors that create a social atmosphere in which group members live. Thus, different groups and group members act as socializing agents who, along with social situations, encourage what is viewed as socially and culturally appropriate motor development. As you might expect, these sociocultural constraints interact with functional individual constraints to influence motivation, self-esteem, and feelings of competence for a task. Such constraint interactions are explored in chapter 13. Reinforcing What You Have Learned About Constraints Take a Second Look We should not overlook the effects of our social and cultural environment, or the influence of gender typing, on motor development. Back in the early 1970s, boys participated in sports such as field hockey, gymnastics, volleyball, and softball in far greater numbers than they do today. After Title IX passed, girls began to participate in these sports in large numbers. Perhaps the increase in girls’ participation led to gender stereotyping of these sports as “feminine” and, in turn, to a drop in boys’ participation. However, with the participation of Andrew Zayac, Jon Geerts, and others in sports such as field hockey, sociocultural constraints will likely change again, eventually encouraging a broader range of individuals to play more sports. Test Your Knowledge 1. Who are the socializing agents most likely to influence children’s socialization into sport and physical activity? 2. How might gender-role stereotyping result in fewer women participating in sport and physical activity? 3. Describe how toys are part of the socialization process. 4. What is the difference between “sex” and “gender,” and why does this distinction matter in the context of motor development? 5. Describe the changing roles of significant others across childhood and adolescence. 6. How do sociocultural constraints work in regard to our model of motor development? Provide specific examples. Learning Exercise 12.1 451 Observing Sociocultural Constraints on the Internet One of the benefits of the Internet is that we have immediate access to information about many societies and cultures. After a little web browsing, it becomes clear that various societies and cultures promote varying activities for their members—sports, for example, or the age at which a certain activity is deemed appropriate, or the roles viewed as proper for males and females, and so on. In this learning activity you will use the Internet to explore several countries and identify sociocultural constraints specific to those places. 1. Have you ever wondered what your motor development might be like if you had grown up in a different society and culture? In this exercise you will imagine in turn that you are a college-age individual from each of six continents: Africa, Asia, Australia, Europe, North America, and South America. To get started, select a country from each continent. Do not choose the country in which you reside. 2. Next, visit at least two websites from each country as well as two websites about each country (e.g., from an encyclopedia or travel guide). Remember to record the URLs of these websites for future reference. The more websites you visit, the more information you will have to work with. 3. For each country, identify sociocultural constraints specific to that society or culture. 4. For each country you choose, develop a biographical portrait of yourself as you might be if you had been born and raised there. Focus on sociocultural constraints. What would you be like? How would your life and motor development differ from country to country? How might your motor development there compare with your actual motor development in your real home country? Would similarities exist between your lives in the various countries? 452 Chapter 13 Psychosocial Constraints in Motor Development Individual–Environment Constraints 453 Chapter Objectives This chapter explores the relationship between social influences and an individual’s feeling of self-esteem, discusses the effect of self-esteem on motivation to participate in sport and physical activity, investigates why individuals continue participation or drop out of sport, and examines children’s attributions of success or failure in physical activity. 454 Motor Development in the Real World Project ACES and the World’s Largest Exercise Class May 1, 2013, marked the 25th anniversary of Project ACES (All Children Exercise Simultaneously). On that day, millions of schoolchildren all over the globe exercised at the same time (10 a.m. in each locality) in a symbolic gesture of fitness and unity. This noncompetitive program has proven to be educational, motivational, and fun. When Len Saunders created Project ACES in 1989, he had no idea that it would reach the magnitude and success it enjoys today. The program has been praised by U.S. presidents Barack Obama, Bill Clinton, George H.W. Bush, and Ronald Reagan. The program has also received praise from state governors, senators, and sports and entertainment celebrities and has been endorsed by groups such as the American College of Sports Medicine and the President’s Council on Fitness, Sports and Nutrition. Project ACES has reached millions of children, parents, and teachers all over the world, including participants from more than 50 countries. Visit the project’s website at www.lensaunders.com/aces/index.html. The goal of Project ACES is to motivate children to participate in physical activity on a daily basis. The belief is that good experiences with physical activity in childhood lead to continued participation throughout the life span, which in turn allows a lifetime of improved health. In the United States, a government focus on health and physical activity (e.g., Healthy People 2010) may have helped increase the number of citizens who choose to exercise. From 2001 to 2005, more women (up 8.3% to 46.7%) and men (up 3.5% to 49.7%) reported that they exercised on a regular basis; these figures are closing in on the Healthy People 2010 target of an activity rate of 50% (Centers for Disease Control and Prevention, 2007). Still, despite the gains in participation, more than half of the population in the United States does not exercise on a regular basis. Why do some individuals participate in physical activities on a regular basis whereas others avoid them? We spend much of this text describing motor behaviors that most typically developing individuals exhibit. However, we have yet to discuss one type of constraint—the individual functional constraint—that can drastically alter type and amount of personal physical activity and affect the emergence of movement over time. An individual functional constraint is not a specific anatomical structure but rather a psychological construct, such as motivation, self-efficacy, or emotion. Often, socializing agents such as parents or peers play a strong role in developing an individual’s functional constraints. Therefore, we consider the interaction between sociocultural constraints and individual functional constraints. 455 As noted in the previous chapter, the social or cultural environment can encourage or discourage specific behaviors. Of course, these environmental constraints have different effects on different individuals. One person may not participate in an activity because of parental influences that discourage sport; another person may participate because of these same influences, as an act of rebellion. This chapter explores the interaction between social factors and functional constraints such as emotions, perceived ability, motivation, and other personal attributes. One key functional constraint related to physical activity is self-esteem. 456 Self-Esteem All individuals evaluate themselves in various areas, such as physical ability, physical appearance, academic ability, and social skills. These self-judgments are called by many names, including self-esteem, self-concept, self-image, self-worth, and self-confidence. This chapter uses the term self-esteem to mean one’s personal judgment of his or her own capability, significance, success, and worthiness; it is conveyed to others in words and actions (Coopersmith, 1967). Whether your self-evaluations are accurate is not as important to your self-esteem as is your belief that they are accurate (Weiss, 1993). Others can identify your level of self-esteem through what you say to them as well as through your nonverbal behaviors in joining or avoiding certain activities. For example, someone with high self-esteem for physical activity is not likely to avoid physical activity. Self-esteem is important because it influences one’s motivation to join and sustain particular activities. Researchers have found a high correlation between physical activity and self-esteem (McAuley, 1994; Sonstroem, 1997). Self-esteem is one’s personal judgment of his or her own capability, significance, success, and worthiness; it is conveyed to others through words and actions (Coopersmith, 1967). It involves one’s self-evaluation both in general and in specific areas. Self-esteem is not just a general sense. It is specific to domains—that is, areas or situations. For example, a certain teenage boy may evaluate himself as high in the physical and social domains but low in the academic domain. In each domain, individuals may differentiate their abilities at even more specific levels (Fox & Corbin, 1989). Academic ability may be perceived in terms of ability in mathematics, writing, foreign languages, and so on. This chapter focuses on self-evaluations in the physical domain related to physical skills. A domain is an independent area or sphere of influence, such as the social, physical, or academic. Key Point Self-esteem influences participation in sport and physical activity; it also influences skill mastery. It becomes more accurate as a person ages. Over time, an individual’s self-esteem in a given domain more closely matches his or her actual abilities. Professionals interested in motivating people to be active must understand self-esteem and the factors that influence people’s judgments of their capabilities. Those working with children should know how self-esteem develops, and those working with people of any age should be aware of the criteria that people use as a basis for their evaluations and whether 457 these criteria change as individuals grow older. Measuring Self-Esteem in Children It would seem much easier for developmentalists to measure running and jumping, or strength and flexibility, than self-esteem in children. Susan Harter (1985) uses a question format (“Some kids . . . BUT other kids . . .”) to measure children’s self- perceptions. For example, one pair of statements is, “Some kids feel that they are better than other kids their age at sports BUT other kids don’t feel they can play as well.” Next to each statement are two boxes, one labeled really true for me and one labeled sort of true for me. Children check one of the boxes, indicating which kids they perceive as being like themselves and to what extent. Harter’s Self-Perception Profile for Children contains 36 statements for 5 specific domains (scholastic competence, athletic competence, social acceptance, physical appearance, and behavioral conduct) and a global score for self-worth. This profile is appropriate for children aged 8 through early adolescence. For children under age 8, Harter and Pike (1984) designed a pictorial scale. Instead of two statements, two pictures are presented: One shows a competent or accepted child and the other shows an unaccepted child or a child who is unable to do the accepted task. Children say whether they are a lot or a little like the child in the picture. Hence, the scale is made more concrete for young children and can be given to children who cannot read or understand written statements. The pictorial scale assesses four domains: cognitive competence, physical competence, peer acceptance, and maternal acceptance. Scales available for adolescents and adults typically cover many more domains. Development of Self-Esteem Children’s self-esteem is greatly influenced by verbal and nonverbal communications from those who are significant to them, including parents, siblings, friends, teachers, and coaches (figure 13.1). Verbal comments such as “Good” or “Why can’t you do better?” are sources of information, as are facial expressions and gestures (Weiss, 1993). Children are likely to compare themselves with other children as well, and the results of these evaluations influence self-esteem. These appraisals and comparisons do not, however, exert equal influence throughout life. This section examines how the pattern of influence can change. Imagine you are a physical therapist. Should improving patients’ self-esteem be a primary concern in your work? Why or why not? 458 459 Figure 13.1 Emotions and social interactions influence the development of self-esteem for physical performance. Interactions are verbal and nonverbal. Based on Horn 1987. Social Interactions Children as young as age 5 can compare themselves with others (Scanlan, 1988), but under the age of 10 they depend more on parental appraisals and outcomes of contests than on direct comparisons (Horn & Hasbrook, 1986, 1987; Horn & Weiss, 1991). Young children are not as accurate as teenagers in their evaluations of their physical competence. The level of intrinsic motivation and the extent to which children believe they control their lives influence the accuracy of children’s perceptions of their physical ability. Children older than 10 rely on comparisons with and appraisals given by their peers. Perceived competence is important: Those with high perceptions of competence tend to have more positive reactions in sport and physical activity than do those who feel less competent (Weiss & Ebbick, 1996). Feedback and appraisal from teachers and coaches also contribute to the development of self-esteem in the physical domain (Smoll & Smith, 1989). For example, male athletes aged 10 to 15 years show high self-esteem when they play for coaches who give frequent encouragement and corrective feedback (figure 13.2), especially if the athletes begin with somewhat low self-esteem (Smith, Smoll, & Curtis, 1979). Coaches’ appraisals and self- perceptions of improvement also influence teenage girls, but the pattern of coaches’ influence is interesting. In a study by Horn (1985), self-esteem did not increase when girls received reinforcement from coaches after successful performances. Instead, an increase in perceived competence was associated with criticism. Apparently, the coaches’ positive comments were general and did not relate specifically to the girls’ performance whereas the criticisms were associated with a skill error and often included a suggestion for improvement. Therefore, teachers and coaches cannot expect global praise to automatically raise a child’s self-esteem. Feedback should relate to performance (Horn, 1986, 1987). 460 Figure 13.2 Feedback from a teacher or coach can contribute to the development of an important individual constraint: self-esteem. Emotions The development of self-esteem is also related to emotions associated with participation. The pride and excitement associated with success, as well as the disappointment and stress associated with failure, influence a person’s self-esteem and motivation to sustain participation (Weiss, 1993). This, of course, relates not just to sport but to physical activity in general. Enjoyment leads to higher levels of self-esteem and motivation to participate. In turn, perceptions of high ability and mastery, low parental pressure, and greater parent or coach satisfaction lead to enjoyment in preadolescents and young adolescents (Brustad, 1988; Scanlan & Lewthwaite, 1986; Scanlan, Stein, & Ravizza, 1988). Relationship Between Causal Attributions and Self-Esteem Self-esteem can influence behavior because people tend to act in ways that confirm their beliefs of themselves; that is, people tend to be self-consistent. If you have low perceived competency and low self-esteem surrounding your ability to perform a skill, then you tend to perform the skill with low competency. These beliefs often are evident in the reasons 461 people give for their successes and failures. These reasons are called causal attributions. People of any age with high self-esteem tend to make attributions that are 1. internal (believing that they influence outcomes through their own behavior), 2. stable (believing that the factors influencing outcome are consistent from situation to situation), and 3. controllable (believing that they personally control the factors influencing outcome). Causal attributions are the reasons to which people credit their successes and failures. These differ for people with high and low self-esteem. For example, competitors with high self-esteem attribute their success to their talent (internal), think they can win again (stable), and believe they are responsible for their successes rather than merely lucky (controllable). They view their failures as temporary and meet them with renewed effort and continued practice to improve skills. In contrast, people with low self-esteem attribute success to factors that are 1. external (believing that they could not change outcomes), 2. unstable (believing that the outcome is a product of fluctuating influences such as good and bad luck), and 3. uncontrollable (believing that nothing they do could result in a different outcome). Competitors with low self-esteem often attribute losing to a lack of ability and attribute winning to luck or to a task so easy that anybody could win. Examining causal attributions can help us understand adult behavior, but few researchers have studied the causal attributions of children in sport and physical activity. This information is particularly important because children are in the process of developing self- esteem. We have seen that children use various factors in judging themselves as they develop. Therefore, we must be concerned with the accuracy of their self-estimates and the roles that adults play in helping children make appropriate attributions. Children’s Attributions The sparse information available on age-related changes in children’s attributions indicates that children aged 7 to 9 years attribute outcomes to both effort and luck more than older children and teens do (Bird & Williams, 1980). These factors are unstable. The children in this study, however, reacted to stories provided by the researchers rather than to actual outcomes they experienced, and a more recent study failed to find age differences in attributions after actual participation (Weiss, McAuley, Ebbeck, & Wiese, 1990). Because young children might not be able to distinguish between ability and effort very well, more information is needed on age differences. 462 Differences do exist in the attributions made by children who differ in their perceived physical competence (Weiss et al., 1990). As expected, children with high physical self- esteem give internal, stable, and controllable reasons for their successes. Their attributions for success are more stable and their future expectations for success are higher than those of children with low physical self-esteem. Again, children vary in the accuracy of their physical estimates (Weiss & Horn, 1990), and girls who underestimate (rather than accurately estimate or overestimate) their physical abilities typically choose less challenging skills and attribute outcomes to external factors. Boys who underestimate their physical abilities report little understanding of what is responsible for their successes or failures. Children who both perceive their physical abilities as low and tend to underestimate their abilities probably make inaccurate attributions about the outcomes of their efforts. Their behavior is characterized by an unwillingness to try challenging tasks, a lack of effort to do well, and avoidance of participation. Key Point Children who perceive their physical abilities as low are not likely to persist in physical activities, therefore missing out on the associated health and psychosocial benefits (Weiss, 1993). Parents, teachers, and coaches can help children, especially those with low self-esteem, give proper credit to the reasons for success or failure. Adults can help children with low self- esteem retrain their attributions (Horn, 1987; figure 13.3). Rather than let children attribute failure to lack of ability and attribute success to luck, adults can emphasize improvement through effort and continued practice. They can also encourage children to set goals and can provide accurate feedback about the children’s progress. Children who come to think their situations are hopeless (i.e., those with learned helplessness) need challenges that are accurately matched to their abilities and in which difficulty is increased in steps much smaller than those presented to other children. Children with high self- esteem for physical competence probably possess high levels of intrinsic motivation to participate in physical activity. If children with low self-esteem are ever to enjoy physical activity and ultimately realize the benefits of participation, adults must make special efforts to improve their self-esteem (Weiss, 1993). 463 Figure 13.3 Adults must help children with low self-esteem for physical performance improve their self-esteem. Retraining can change causal attributions. Adults’ Attributions Self-esteem also influences the motivation of adults. Like children, adults tend to behave according to their beliefs about themselves. Recall that children obtain the information on which they base their self-judgments largely from their significant others and their own comparisons. Adults obtain information from four sources (Bandura, 1986): 1. Actual experiences (previous accomplishments or failures) 2. Vicarious experiences (observing a model) 3. Verbal persuasion from others 4. Their physiological state Key Point An adult’s level of self-esteem affects motivation for physical activity. Like children, adults behave according to their beliefs about themselves, which are acquired from actual and vicarious experiences, others’ opinions, and their physiological state. An individual’s actual experiences are particularly influential, and changing physiological status is a reality for most older adults. For example, failing eyesight lowers an older adult’s confidence for participating in racket sports. In contrast, verbal persuasion is a much weaker influence. The models available to older adults vary considerably. Some have opportunities to see others like themselves participating in a wide range of activities; others do not, especially on a personal basis rather than in a magazine or on television. Given these influences, it is clear that a person’s self-esteem can increase or decrease throughout life. 464 A few investigators have related adults’ physical self-esteem and their motivation to maintain or improve their fitness. Ewart, Stewart, Gillilan, and Kelemen (1986) involved men with coronary artery disease, aged 35 to 70, in either a walk or jog plus a circuit weight-training program or a walk or jog plus volleyball program for 10 weeks. They measured self-esteem, arm and leg strength, and treadmill-running performance before and after the program. The researchers found that those with higher pretraining self-esteem improved more in arm strength than those with lower self-esteem, even when accounting for beginning strength level, type of training, and frequency of participation. Self-esteem did improve with training but only when participants received information indicating that their performance was improving. For example, the weight-training group improved their self-esteem for lifting weights but not for jogging, even though they improved on both tests in a postprogram assessment. They could monitor their improvements in weight training during the program but their jogging distance remained constant, so they had no indication they were improving. Hogan and Santomeir (1984) also observed an increase in self-esteem for swimming in older adults after a 5-week swimming class. Thus, older adults’ self-esteem can influence how much improvement they realize in a program, and participation can raise self-esteem when participants have information about their actual improvements. Web Study Guide Interview sport participants to obtain information about their attributions for success and failure in Lab Activity 13.1, Identifying Causal Attributions in Sport Participants, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 465 Motivation The motivation to participate in activities of a certain type involves many factors, including those that lead people to initiate or join an activity. Other factors encourage people to persist in an activity and to exert effort in order to improve. Still other factors lead people to end their involvement. In the previous chapter, we discuss factors that encourage children’s initial sport involvement or the sport socialization process. Let’s turn now to factors that keep children in physical activity and sport or lead them to drop out. We also consider how the factors that motivate people to participate in physical activities change over the life span. Persistence Researchers have focused quite a bit of attention on the reasons that children and teens continue to participate in sport (Weiss, 1993). In general, the reasons include the following: A desire to be competent by improving skills or attaining goals A desire to affiliate with or make new friends A desire to be part of a team A desire to undertake competition and be successful A desire to have fun A desire to increase fitness McAuley (1994) and Sonstroem (1997) found that girls most often cited fun (followed by health benefits) as the reason they participate in physical activity. Most individuals cite not just one or two but several reasons for participating. Harter (1978, 1981) proposed a competence motivation theory to explain this. According to the theory, children are motivated to demonstrate their competency and therefore seek out mastery attempts, or opportunities to learn and demonstrate skills. Those who perceive that they are competent and believe that they control situations have more intrinsic motivation to participate than others. Membership in subgroups can also influence a person’s motivation to persist in sport. Examples of subgroups include age groups, starters versus benchwarmers, elite athletes versus recreational participants, and so on. Consider age groups. Brodkin and Weiss (1990) studied varying age groups of competitive swimmers: ages 6 to 9, 10 to 14, 15 to 22, 23 to 39, 40 to 59, and 60 to 74. They found that children cited wanting to compete, liking the coaches, and pleasing family and friends as reasons to participate. The 15- to 22-year-olds listed social status, as did young children to an extent, and fitness motives were important 466 to the young and middle adults. Children and older adults did not consider fitness as important. Young children and older adults named fun as the most important reason to participate. Another investigation of children involved in swimming found that those younger than 11 years were motivated to participate by external factors: encouragement from family and friends, liking the coaches, social status, and activities that they enjoyed (Gould, Feltz, & Weiss, 1985). Teenagers in this study cited more internal factors: competence, fitness, and the excitement of swimming. Thus, different age groups may have different reasons for participating, but more research is needed on other activities and with participants of varying skill levels (Weiss, 1993). Dropping Out Withdrawal from sport programs is a very real aspect of youth involvement in physical activity. Changing from one type of activity to another might be part of one’s development or might reflect a person’s changing interests or desire to try something new, but withdrawing from activity altogether has serious repercussions for health at any point in life. It is often difficult for surveys and research studies to distinguish between participants who switch activities and those who withdraw from activity altogether. In addition, dropouts do not always quit by choice; injuries or high monetary costs, for instance, might force some to withdraw. Thus, the reasons that participants give for quitting deserve further attention. Some young dropouts cite very negative experiences, such as these, as reasons for withdrawing from sport (McPherson, Marteniuk, Tihanyi, & Clark, 1980; Orlick, 1973, 1974): Dislike for the coach Lack of playing time Too much pressure Too much time required Overemphasis on winning Lack of fun Lack of progress Lack of success Such negative reactions come from a small number of dropouts (Feltz & Petlichkoff, 1983; Gould, Feltz, Horn, & Weiss, 1982; Klint & Weiss, 1986; Sapp & Haubenstricker, 1978). The majority of dropouts withdraw to pursue other interests, to try different sport activities, or to participate at lower intensity levels. Teens often report dropping out to take jobs. Many plan to reenter their sport later. Thus, much of the attrition in youth sports 467 reflects shifting interests and involvement levels rather than negative experiences. Nonetheless, professionals should be concerned about negative experiences because they can be detrimental to a person’s psychological development and can lead to a lifelong avoidance of healthful activities. Key Point Not all children and adolescents cite negative reasons for leaving sport and physical activity. Often, individuals simply want to pursue different activities that may or may not include physical activity. Web Study Guide Reflect on activities in your life and what influenced you to persist or end involvement in those activities in Lab Activity 13.2, Motivation to Participate, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Teacher-Centered Versus Student-Centered Approaches What motivates a child to learn motor skills? When teaching fundamental motor skills to young children, instructors often attempt to promote change in movement through teacher-centered methods. That is, the instructor designs and presents developmentally appropriate activities in class and then chooses when to progress students to the next task. As a motor skills intervention, the teacher-centered approach has proven successful in various research studies (Goodway & Branta, 2003; Sweeting & Rink, 1999). However, to date, it is unknown whether teacher-centered approaches are the most effective way to improve children’s motor skills. As an alternative to the teacher-centered approach, a group of researchers have examined a different approach toward teaching motor skills to young children. It is called mastery motivational climate (Goodway, Crowe, & Ward, 2003; Parish, Rudisill, & St. Onge, 2007; Valentini, Rudisill, & Goodway, 1999). Fundamental to this approach is the notion that effort and outcome are related; that is, when the learner’s environment is both mastery oriented and highly autonomous, children achieve and learn (in this case, improve in fundamental motor skills; Ames, 1992). Several research studies have shown that this approach to teaching and learning leads to improvement in motor skills (Valentini & Rudisill, 2004a,b,c) and in physical activity levels (Parish, Rudisill, & St. Onge, 2007). 468 How would an instructor develop a mastery motivational climate for young children? Researchers at Auburn University provided a model that follows the principles presented by Ames (1992); they call it HAPPE (high autonomy physical play environment) and describe it as follows (Parish & Rudisill, 2006): Toddlers (and young children) engage in a variety of authentic and meaningful tasks that match their skills and abilities. The tasks allow children to make choices according to individual interests and abilities. The primary authority in the classroom rests with the teacher, who serves as a facilitator of learning. Toddlers and young children are actively involved in decision making, self-management, and self-monitoring and they have opportunities to develop leadership roles. Teachers offer recognition to individual children in response to their effort and engagement in learning. Teachers give feedback and encourage toddlers’ attempts to learn a skill. Groupings emerge naturally as toddlers and young children choose to play alone or with another child and as they decide which skill they want to practice. More experienced toddlers may choose to play in small groups. Toddlers are encouraged to evaluate their own performance, and teachers guide toddlers as they solve problems typically encountered during physical play. Toddlers have plenty of time to fully explore and practice physical play. Researchers have found that motivational climates help young children improve in fundamental motor skills and physical activity (e.g., Goodway & Branta, 2003; Goodway et al., 2003; Parish, Rudisill, & St. Onge, 2007). Still, more research needs to be done in order to determine whether teacher-centered and student-centered approaches differ substantially in the amount and rate at which they enhance motor skill development. Adult Activity Levels Both the amount and the intensity level of physical activity decrease as adults grow older, especially among women (Boothby, Tungatt, & Townsend, 1981; Curtis & White, 1984; Ebrahim & Rowland, 1996; McPherson, 1983; Rudman, 1986). In 1996, the U.S. Department of Health and Human Services reported that even when adding together all the different types of exercise in which individuals participate, two-thirds of adults aged 65 and older who did exercise did not achieve recommended levels (U.S. Department of Health and Human Services, 1996). This withdrawal from and reduction in physical activity does not result from changes in physiological health alone (Spreitzer & Snyder, 1983). Psychosocial factors also influence adults’ activity levels (McPherson, 1986). These factors include the following: Stereotypes of appropriate activity levels 469 Limited access to facilities and programs Childhood experiences Concerns over personal limitations on exercise Lack of role models Lack of knowledge about appropriate exercise programs Belief that exercise is harmful or is ineffective in preventing disease (Duda & Tappe, 1989a) However, indications exist that adults, especially older ones, are becoming more actively interested in health and in the influence of physical activity on health status (Howze, DiGilio, Bennett, & Smith, 1986; Maloney, Fallon, & Wittenberg, 1984; Prohaska, Leventhal, Leventhal, & Keller, 1985). In fact, the number of women participating in the National Senior Games increased by 110% from 1991 to 1998 (Women’s Sports Foundation, 2000). Duda and Tappe (1988, 1989a,b) proposed that adult exercise participation reflects three interrelated factors (figure 13.4): 1. Personal incentives, such as a desire to demonstrate mastery, compete, be with others, receive recognition, maintain health, cope with stress, or improve physical fitness 2. A sense of self, particularly in regard to one’s self-esteem for physical activity 3. Perceived options, or the opportunities a person has in a given situation, such as transportation to various sites where adult programs are offered 470 Figure 13.4 Three interrelated factors can influence the level of adults’ exercise participation. If you were to design a physical activity program for older adults, what could you do to encourage your participants first to join and then to continue with the program? Personal incentive values and self-esteem can change throughout life. For example, as an adult ages, the desire to compete might decrease while the desire to be with others might increase. Older adults can also come to perceive that their physical abilities have declined over time. Duda and Tappe surveyed 144 adults in three age groups (25–39, 40–60, and 61-plus years) who were participating in an exercise program. Personal incentives differed among the age groups and between men and women. Middle-aged and older adults placed more value on the health benefits of exercise than did young adults. For example, figure 13.5 shows the extent to which men and women in each age group valued the stress- reducing benefits of exercise. Young adult men put little emphasis on this exercise benefit, as shown by their low average on the Personal Incentives for Exercise Questionnaire. Men also valued competitive activities more than women did. Exercise leaders, then, might help older adults stick to their exercise programs by emphasizing social interaction, health benefits, and stress reduction (Duda & Tappe, 1989b). 471 Figure 13.5 Personal incentives for adults to exercise differ between age groups and between the sexes. Group averages on the “coping with stress” category of the Personal Incentives for Exercise Questionnaire (a 5-point Likert-type scale) are plotted here. Reprinted by permission from Duda and Tappe 1989. No age group differences in self-esteem were apparent among these older adults, but differences did exist between men and women. The women had lower physical self-esteem and less feeling of control over their health status than the men did. These beliefs are typically associated with less involvement, but the women felt they had more social support for their involvement than did the men, and they continued to participate. Exercise leaders who adopt strategies targeted at adults who might have low physical self- esteem can improve participants’ self-esteem and exercise involvement (Duda & Tappe, 1989b). Another group of adults might not have the same characteristic incentives and perceptions as those Duda and Tappe surveyed. Yet exercise leaders can encourage older adults to persist in their exercise programs by being aware of the incentives and perceptions of their particular groups (figure 13.6). They can then emphasize the aspects and benefits of exercise that are most important to those participants. 472 Figure 13.6 In order to improve self-esteem and other individual functional constraints, exercise leaders should keep in mind the incentives for and perceptions of physical activity that older adults have. 473 Summary and Synthesis Individuals’ perceptions of their physical abilities can change dramatically across the life span. In turn, these perceptions can affect self-esteem. Maintaining high levels of self- esteem seems to enhance performance as well as perceived competence, which motivates an individual to continue participating in physical activity. This set of dynamics offers an example of how constraints can interact to encourage movement. For example, older adults who desire social interactions with friends, as well as improved fitness levels, may join an exercise program for older adults. This program may involve activities that facilitate both socialization and fitness, which help individuals improve their self-esteem. The higher levels of self-esteem and perceived competence then help motivate the older adults to return to the program, which continues to foster self-esteem, and so on. At the same time, these constraint interactions can discourage behavior. Consider children who begin participating in a sport such as soccer. They may have joined to be with friends and have fun. Perhaps, however, the coach wants to win and provides criticism and negative feedback to the players if they make a mistake. The players may begin to attribute losses to their lack of ability and believe that they lack competence in that activity. In turn, their self-esteem begins to decrease. Eventually, they may drop out of the sport altogether. These two examples demonstrate the powerful interplay between sociocultural and individual functional constraints. We all live in a social context and are subject to social opinions. These opinions help fashion and reinforce our beliefs about ourselves. Eventually, we act as we believe. Keeping this in mind, it is essential for those who work with others in physical activity and sport to look beyond the activity to the individuals themselves. Reinforcing What You Have Learned About Constraints Take a Second Look Think back to project ACES, discussed at the beginning of the chapter. Imagine that you are exercising and that, at the very same moment, millions of other people are doing the exact same thing. Does the thought of all those people joined in simultaneous physical activity motivate you to exercise? Len Saunders designed Project ACES to motivate children to exercise by bringing them together at one point in time. Motivation (an individual functional constraint) can be greatly enhanced by creating a learning environment designed to motivate, such as Project ACES or HAPPE at Auburn University. This interaction of constraints (motivated individuals in a motivational climate) should encourage positive and progressive change in motor skill over time. Test Your Knowledge 474 1. What is self-esteem? Is it general or specific? How is it developed? 2. People tend to attribute their successes and failures to various causes. What are the differences in the causal attributions made by those with high self-esteem and those with low self-esteem? To what do children tend to attribute their performance? 3. What factors are associated with persisting in sport and physical activity? With dropping out? 4. How does perceived competence change with children’s causal attributions? Learning Exercise 13.1 Exploring Motivations for Physical Activity and Exercise What motivates you and other students to be involved in sport and physical activities? If you evaluate a group of students, you will likely discover a diverse array of reasons to participate—or not participate. 1. First, determine your own motivations. Do you participate in sport and physical activities now? Look at the list of reasons for persisting in sport participation in the “Persistence” section of chapter 13. Write down those reasons that apply to you and add any others not included in the list. Have you dropped out of a physical activity? Look at the list of reasons for dropping out of a sport in the “Dropping Out” section of chapter 13. Do any of the reasons listed apply to you? Have you persisted in some activities and dropped out of others? What factors are related to these choices? 2. Compare your notes from #1 above with a group of other students. What are the factors most commonly associated with persistence? What are the factors most commonly associated with dropping out? Calculate percentages for easy interpretation (e.g., “Seventy percent of my group dropped out because of lack of time”). Reflect on these factors and offer some generalizations about motivational factors and the students in your class. 475 Chapter 14 Knowledge as a Functional Constraint in Motor Development 476 Chapter Objectives This chapter discusses the benefits to motor performance of knowing about an activity, differentiates between the knowledge of novices and that of experts and recognizes that children tend to be novices, and identifies trends in the speed of cognitive processing over the life span. 477 Motor Development in the Real World A Little Knowledge Goes a Long Way Technology has changed sport and dance performance in many ways. Technique can now be analyzed and compared with that of skilled models or that of an athlete’s previous performances. Instant replays can confirm or refute referees’ decisions. But nothing has become more commonplace than using technology to provide information about tactics. Coaches and athletes alike review video clips and statistics to identify patterns in their opponents’ play, and they spend long hours developing strategies to counter those patterns. The cost of using technology is widely accepted as a necessity in order to be competitive. Technology certainly adds to the knowledge that athletes gain from their own experience; at some levels of competition, where athletes are closely matched in skill level, the decisions that athletes make based on their knowledge of a sport can make the difference in the outcome of a competition. Great performers bring not only their physical talents and conditioning but also their knowledge about a task to the physical performance of that task. Knowledge, then, is an individual constraint that interacts with other constraints to give rise to movement; specifically, it is a functional individual constraint. Individuals and groups have varying amounts of knowledge about a movement task. For example, children have had less time to acquire knowledge than adults, whereas older adults might have the advantage of far more experience than younger adults. This chapter examines how knowledge constrains movement over the life span. 478 Knowledge Bases At any age, knowledge about an activity facilitates performance, and increased knowledge facilitates remembering information about that topic (Chi, 1981). Children undoubtedly have a smaller base of knowledge than adults because they have had fewer experiences. Yet children who become experts on a particular topic can outperform adults in that area. Chi (1978) observed that child experts in chess recalled significantly more chess positions than adult novices in chess. Why would performance be related to size of the knowledge base? There are at least three reasons: 1. Increased knowledge reduces the need to remember a great deal of information in the short term (Chase & Simon, 1973). 2. Increased knowledge allows more effective use of the cognitive processes (Ornstein & Naus, 1984, cited in Thomas, French, Thomas, & Gallagher, 1988). 3. Increased knowledge reduces the amount of conscious attention needed to perform some tasks (Leavitt, 1979). A knowledge base is the amount of information a person has on a specific topic, consisting of declarative knowledge and possibly procedural and strategic knowledge. Thus, knowledge is a functional constraint that interacts with other constraints, especially task constraints, to give rise to movement. Performance is facilitated not only by practice of physical skills but also by increased knowledge of the sport or activity. Types of Knowledge Key Point Experts have more declarative and procedural knowledge, and they structure that knowledge differently than do novices. Before considering the development of knowledge about sport, we must identify the types of knowledge and the differences between experts and novices. Chi (1981) has defined three types of knowledge: 1. Declarative knowledge—knowing factual information 2. Procedural knowledge—knowing how to do something in accordance with specific rules 3. Strategic knowledge—knowing general rules or strategies that apply to many topics 479 Declarative and procedural knowledge are specific to a certain topic; strategic knowledge can be generalized. The give-and-go is an example of strategic knowledge that can be generalized. An athlete who understands how to execute a give-and-go (in which a player passes to a teammate, then advances toward the goal or basket for a return pass) in basketball can execute a give-and-go in hockey or soccer, provided she has the physical skills to do so. Experts have more declarative and procedural knowledge about an activity than do novices (Chi, 1978; Chi & Koeske, 1983; Chiesi, Spilich, & Voss, 1979; Spilich, Vesonder, Chiesi, & Voss, 1979). Experts independently organize the information they know in a similar way (Chiesi et al., 1979; Murphy & Wright, 1984)—that is, experts structure knowledge, whereas novices do not. By organizing their information in a methodical knowledge structure, such as a hierarchy, experts facilitate their memory recall and, therefore, their use of information. A knowledge structure is the manner in which a person organizes information about a topic, typically expressed in hierarchical fashion. Experts structure information in ways similar to other experts. Thomas and colleagues (1988, adapted from Berliner, 1986) identified other sport-specific ways in which experts and novices differ. Those pertinent to our discussion follow: Experts make more inferences about objects and events. In sport, this helps experts predict upcoming events and anticipate the most likely occurrences. Experts analyze problems at a more advanced level. For example, expert athletes probably think of offensive plays as concepts rather than as lists of individual players’ movements. Experts quickly recognize patterns. For example, expert athletes quickly recognize defensive configurations. Experts preplan their responses for specific situations. Softball infielders, for example, do so before the batter hits by choosing the base to which they will throw given the runners on base and the number of outs. Experts tend to organize knowledge in relation to the goal of the game. For example, an expert basketball player thinks of offensive strategies not in terms of a long list of individual offenses but in terms of those that successfully attack, say, a zone defense versus those that attack a player-to-player defense. Experts spend much time learning about their topics. Sport-specific expertise in particular requires hours of practice and experience, especially if a player wants to develop procedural how-to knowledge. Keep in mind that expertise is specific. For sport and dance, this means that individuals become experts in specific sports (tennis, basketball) or dance forms (modern, ballroom). In addition, expert performers in sport and dance have a high level of physical skill. Both skill and the knowledge of how to use skills in specific situations are necessary for success (Thomas et al., 1988). 480 From the perspective of a coach, identify three or four team sports, as well as a particular position for each, that would require an athlete to have skill and knowledge in order to perform well. Development of a Knowledge Base Let’s consider how individuals, especially children, develop a knowledge base in a particular sport. First, they must acquire declarative knowledge, which provides a foundation for procedural knowledge (Chi, 1981). Young children often lack declarative knowledge of a sport (figure 14.1); typically, they are novices who must learn game rules, goals, and patterns of play before they can exhibit procedural knowledge and make appropriate decisions regarding which action to perform. Strategic knowledge is the last to develop. It requires experience with many types of tasks, which then enables children to generalize across topics. 481 Figure 14.1 These novice soccer players are just beginning to develop a knowledge base about soccer. French and Thomas (1987) conducted one of the first studies of knowledge development in sport with children. They proposed that children need declarative knowledge of both basketball and basketball skills in order to make appropriate decisions while playing basketball. Coaches classified the 8- to 12-year-old boys in a youth basketball program based on their skills, knowledge of the game, and ability to make good judgments in a game. The best third of the group was designated the expert group and the bottom third the novice group. The boys in these two groups were then tested on their basketball knowledge and skills. In individual interviews, they were asked to give the appropriate action for each of five basketball game situations described to them. The researchers also observed the players during games and graded their decisions as appropriate or inappropriate. The experts scored much better than the novices on both the knowledge and skill tests. More importantly, the experts chose the appropriate action in game situations more often than novices. During their situation interviews, experts were more likely to give answers dependent on the action of the opponents and to identify more alternatives, indicating more basketball knowledge. French and Thomas observed some of the 8- to 10-year-old boys from their first study over the course of a season along with a group of boys who did not play basketball. By the end of the season, both expert and novice players were making better decisions about actions and scored better on the knowledge test (figure 14.2). The control group made no significant progress. Interestingly, none of the children improved in physical skill over the season, either on skill tests or in game play. This initial study, then, indicates that basketball knowledge is related to children’s skill performance and that children might acquire knowledge faster than they improve their physical skills. Professional tennis players were shown in turn to have more tactical concepts than varsity players (McPherson & Kemodle, 2007) or other tennis players (Nielsen & McPherson, 2001). 482 Figure 14.2 Both expert and novice boys scored better on a postseason basketball knowledge test than on a preseason test. A control group did not improve. Data from French and Thomas 1987. Key Point With advancing knowledge, players tend to report that their actions depend on opponents’ actions. French, Nevett, and colleagues (French et al., 1996; Nevett & French, 1997) also examined expertise in youth baseball players. In their first study, they hypothesized that players with more declarative and procedural knowledge solve game situation problems better whereas those with less knowledge exhibit more errors in solving those problems. The researchers gave youth baseball players 7 to 8 years old and 9 to 10 years old five baseball situation problems to solve. The players’ coaches categorized the players for skill level. In three of the problem situations, the players who coaches categorized as more skilled solved the problem better, regardless of age. Overall, though, these 7- to 10-year-olds were still developing their knowledge base regarding baseball. They had difficulty quickly predicting base runners’ actions and failed to monitor some critical game conditions. Nevett and French (1997) followed up this initial study with 8-, 10-, and 12-year-old and high school baseball shortstops. They trained players to use a talk-aloud procedure between pitches. Although players were told to verbalize any of their thoughts, the researchers were particularly interested in information about possible plays that could be made if the batter hit the ball. Players who were 12 years of age and under did not develop advanced defensive plans, rehearse the plans, or update the plans as well or as frequently as high school players did. It is likely that frequent and repetitious responses to game situations through experience help in the development of procedural knowledge structures. Even though declarative knowledge can be acquired at young ages, experience in game situations is 483 necessary in order to develop a knowledge structure that is beneficial to skilled performance. McPherson (1999) expanded work on knowledge bases in sport to adults. She posed six tennis situation problems to six novice tennis players and six college varsity experts, all of who were women aged 18 to 22 years. Compared with youth experts in previous research, adult experts generated a greater number, variety, and level of sophistication of condition- and-action concepts. Adult novices were similar to youth novices in that they had weak representations of the game situation problems and offered few solutions. The adult novices generated fewer tactical concepts than did youth tennis experts in previous studies. Thus, years of experience—from practicing, being coached, and playing—are influential in developing a knowledge base, and youths with more experience can have a more advanced knowledge base than adult novices. Key Point Experience in playing is important in developing a knowledge structure. Assessing Cognitive Decision Making The more knowledge an athlete has, the more quickly and accurately that athlete can decide on an action. So, in assessing decision making, the speed and accuracy of decisions in sport provide information about the size of an athlete’s knowledge base. In their study of knowledge development in sport, French and Thomas (1987) needed to assess the decisions that young basketball players made during games. The written knowledge test and interview yielded useful information, but it was not certain that the players who gave good answers to questions away from the court would make good decisions in a fast-moving, demanding game situation. To assess decision making in games, French and Thomas designed an observational instrument based on a typical offensive sequence in basketball: When a player catches the ball, he or she must decide whether to hold the ball, pass, dribble, or shoot. The researchers identified all the decisions a player could make, then categorized them as appropriate or inappropriate for a given situation. French and Thomas videotaped youth basketball games. A trained basketball expert watched each player in each game for one quarter of playing time and coded each decision the player made when he or she received a pass. A second expert watched 484 independently and coded some of the players to ensure that an expert observer would code each decision the same way at least 90% of the time. The observer gave a player a score of 1 for an appropriate decision and a score of zero for an inappropriate decision. For example, a player received 1 point for passing to an open teammate but no points for passing to a closely guarded teammate. In this way, the researchers could measure the players’ decision making in game play. Observational instruments are an excellent means of measuring behavior. Performers can be observed in real situations rather than in artificial laboratory conditions. Note, however, that French and Thomas had to develop a coding system that included all of the decisions a player could make, then locate experts and train them to use the assessment instrument. Finally, the observers coded from videotape so that they could stop the tape to record their judgments, thereby ensuring that they missed no action. This is a tedious procedure, but it provides an interesting and accurate measure of decision-making behaviors in sport. Web Study Guide Explore how the speed of recognizing defensive and offensive configurations in basketball is related to one’s knowledge base in Lab Activity 14.1, Examining Knowledge and Decision Making in Physical Activities, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Teachers and coaches would benefit from continued study of knowledge development in sport. Educators may be able to improve children’s skill acquisition in sport and dance by using appropriately timed instruction of and emphasis on rules, formations, strategies, and goals. Increased knowledge enhances memory. If you were a physical education teacher at a middle school, what is one activity you would use in your classes? What knowledge about the activity could you teach as you introduce the physical skills of the activity? Sex differences in sport performance might be attributable in part to differences between boys’ and girls’ knowledge of sport. Society makes it easier for boys to acquire sport knowledge by targeting sport-related merchandise to them—board and electronic games, books, collector cards, and so on. Girls’ use of these items could be viewed by some as less appropriate to their gender role, and companies tend to market nonsport items such as 485 dolls or crafts to girls. This difference could contribute to a persisting performance gap between boys and girls. Also, children who practice more probably acquire more knowledge, so unequal opportunities to participate might also widen the performance gap between most boys and at least some girls (Thomas et al., 1988). Knowledge bases in older adults have not been widely studied. Langley and Knight (1996) conducted a case study of a single senior adult competitive tennis player, a 58-year-old man. The investigators conducted interviews and observations and analyzed narrative information with coding techniques. They found that the senior player had a rich knowledge of tennis situations, which centered on performance capabilities and opponent limitations. The player knew how his opponent’s actions in a particular setting affected his own capabilities in that setting. He realized that his physical conditioning had declined somewhat as he aged but felt that his better skill in executing a number of shots more than compensated. Langley and Knight used Gibson’s (1979) notion of affordances to suggest that experienced players perceive the game play environment in terms of the actions that environment affords. Experience allows the player to perceive affordances that are opportunities for success against an opponent. Working with adults only, Gygax, Wagner-Egger, Parris, Seiler, and Hauert (2008) applied a method for studying mental representations from the field of psycholinguistics. Focusing on soccer players’ mental representations as they encountered playing situations, the researchers observed differences in focus of attention—tending to include or not include other players—that were associated with varying expertise. The pattern of association, though, was not simply related to increasing expertise. Gygax et al. also explored the emotional elements of mental representations. These cognitive aspects of motor performance need more research attention. Key Point Older adults can have a rich knowledge base in a sport or dance form through extensive experience. This knowledge may allow older adults to compensate for slight declines in physical performance. We can speculate that older adults with expertise in a sport have an advantage in performance because superior knowledge might offset a loss of physical skill or speed (figure 14.3). Also, older adults learning new sports can expect to improve as they acquire knowledge of the sport. 486 Figure 14.3 Knowledge can help older adults perform successfully even if physical abilities begin to decline. 487 Memory Knowledge and memory are inseparable topics. We remember what we understand; at the same time, “coming to understand” is related to our existing base of mental representations, which define our knowledge at that point in time. Sutherland, Pipe, Schick, Murray, and Gobbo (2003) found that providing children with prior information about an event improved their recall of the event and their knowledge acquisition in the event, both soon after the event and 4 months later. So, new experiences are integrated with our existing base of meaning. The question asked most often about memory development is whether memory capacity changes with development. Yet, how much we remember, no matter what our age, depends on what we already know about a topic. Knowledge is organized or structured in specific content domains (Kuhn, 2000), such as chess, dinosaurs, or baseball. Memorizing, then, is a process of revising our knowledge of a topic. Although much research has been carried out on the topic of memory capacity, the answer to whether it increases with development has been elusive. This probably reflects researchers’ approaching the study of memory without regard to one’s knowledge context. That is, memory has been studied in isolation. The answer to the question of capacity awaits new research approaches that study memory in the broader context of the developing cognitive system. Key Point Memories are knowledge structures resulting from our efforts to understand and know. People of all ages remember more when they have a reason to do so. Very young children first remember when adults engage them in recounting experiences. With advancing age, children internalize this remembering activity and carry it out on their own. Eventually they remember purposively, for their individual benefit or that of their social group. Again, laboratory research on memory often has involved memorizing items for no particular reason other than completing the research task. Future researchers must study memory in the context of information that is important to people. Young children typically do not employ strategies for remembering, but they can be taught to use strategies such as rehearsal, labeling, and grouping. Thomas, Thomas, and Gallagher (1981) demonstrated that teaching children a rehearsal strategy along with a skill enhanced their skill acquisition. Gallagher and Thomas (1980) also found that grouping arm movements in an organized order helped children recall and duplicate movements. The 488 difficulty is that children won’t necessarily apply the strategies learned in one context to another context. Perhaps the use of memory strategies is related to a broader issue—the development of cognitive strategies such as inference and problem solving—and needs to be studied in that context. Van Duijvenvoorde, Jansen, Bredman, and Huizenga (2012) found that when the demands of long-term and working memory were reduced, children, adolescents, and young adults were all capable of making advantageous decisions. Explicitly providing the pros and cons of options helped children and adolescents make advantageous decisions even in complex situations. Because this research used a cognitive task, additional research in movement settings is needed to see whether the same results are obtained. Research on memory in adults and older adults tends to find a decline in performance on memory tasks. Yet, as with the research involving children, researchers have tended to study memory in isolation and without regard to adults’ knowledge of a topic or motivation to remember. Moreover, a host of environmental factors might affect performance of older adults on memory tasks. Diseases (e.g., hypertension) might impair memory performance. Highly fit older adults have been observed to perform better than unfit adults on memory tasks (Stones & Kozma, 1989), and memory performance has been linked to self-reported health status (Perlmutter & Nyquist, 1990). Improvements on memory tasks have also been noted after exercise interventions, so exercise probably has a small but positive effect on memory performance (Clarkson-Smith & Hartley, 1990; Colcombe et al., 2005). Alzheimer’s disease is one of the most devastating diseases of aging for individuals and their families. Late-onset Alzheimer’s disease, the most common form, is characterized by confusion, memory loss, and behavioral problems. As the number of individuals living into older adulthood increases, so too will the number of older adults who develop Alzheimer’s. The cause of late-onset Alzheimer’s disease is probably both genetic and environmental, but researchers are investigating both genes that might trigger the disease, through production of a specific protein, and genes that might control the timing of onset. The hope is that drugs might be designed that could either block the protein or delay onset of the disease, perhaps past the natural life span. Although performance on memory tests might show improvement through childhood and decline in older adulthood, it is most important to acknowledge that memory is related to current knowledge and understanding of information in a specific context as well as to the motivation to remember information. 489 Speed of Cognitive Functions In addition to differences in individuals’ knowledge about specific sports and dance over the life span, general differences exist in the speed with which individuals can access and use that knowledge. That is, a person can have considerable knowledge yet be unable to recall and apply that knowledge as quickly as another person. This would be a more pertinent issue when participating in activities requiring quick decisions and responses. For example, slower cognitive function would not be an issue in deciding how to pick up a spare in bowling because there is sufficient time between deliveries. It would be an issue, however, in the middle of a tennis point when deciding whether a lob is an appropriate shot for the situation. Let’s now consider age-related differences in the speed of cognitive functions. We would do well to begin by acknowledging that most of the research on cognitive speed has been undertaken from an information processing perspective. From this viewpoint, short-term and long-term memory and information retrieval play a major role in movement responses. In contrast, ecological perspectives downplay the role of knowledge and cognitive processes in movement responses. From this perspective, affordances in the environment are perceived. Experience might influence whether an affordance is perceived, but the affordance is always available in the environment. With this in mind, let’s first consider cognitive speed in children. Speed of Cognitive Processing in Children Children take longer than adults to process cognitive information to be remembered. As children get older, they can eventually process either the same amount of information faster or more information in the same amount of time. This trend is apparent in even the simplest of motor responses, simple reaction time. The maximum speed of this response increases from age 3 through adolescence (Wickens, 1974). An improvement with age also occurs in the time required to respond in continuous tracking (Pew & Rupp, 1971). In this type of task, children must continuously match their movement to a target—for example, a video game in which the player controls the image of a car with a joystick to keep the car on a curved road. The slower processing speed exhibited in children appears related to factors considered to be central processes (processes of the central nervous system, or CNS) rather than peripheral ones (Elliott, 1972). One such central process is attention; another is speed of the memory processes. Simple reaction time is the time between the onset of a stimulus (e.g., a light or buzzer) and the beginning of a movement response (e.g., lifting a finger from a button). 490 Key Point Speed of initiating a response increases throughout childhood and youth and is faster when the response is compatible with the stimulus signal. The speed with which an individual can select motor responses is a function of age (Wickens, 1974). Clark (1982) demonstrated this by manipulating the spatial stimulus– response compatibility of a reaction-time task when testing 6-year-olds, 10-year-olds, and adults. In the compatible condition, participants pressed a key on the right if the right stimulus light came on and a key on the left if the left stimulus light came on. In the incompatible condition, participants pressed a key opposite the direction of the light. Spatial compatibility, then, affects the participant’s response selection. Clark found that processing time decreased (performance improved) in the older groups tested in the incompatible condition. Although these central factors of attention, memory, and response selection influence children’s slower processing speeds, peripheral factors do not. For example, nerve impulse conduction speed in the peripheral nerves does not contribute substantially to the speed differences between children and adults. Young children are able to process information faster as they mature because of improvements in central factors such as response selection and speed of the memory process. How could slower cognitive processing affect children in sports such as soccer, basketball, and tennis? If you were a coach, how might you structure their tasks for increased success given this limitation? Speed of Cognitive Processing in Middle-Aged and Older Adults As is the case with young children, middle-aged and older adults exhibit limitations in the processing of information. These limitations are also apparently related to central rather than peripheral processes. However, researchers have found important differences between performers at opposite ends of the life span. For example, older adults do not exhibit declining performance in all types of skills. They undergo little change in their performance of single, discrete actions that can be planned in advance (Welford, 1977b) or of simple, continuous, and repetitive actions, such as alternately tapping two targets (Welford, Norris, & Shock, 1969). However, in actions requiring a series of different movements, especially when speed is important (Welford, 1977c), older adults show a large decrement in 491 performance. The major limitations on older adults, then, seem to involve the decisions they base on perceptual information and the programming of movement sequences (Welford, 1980). These are central rather than peripheral factors. Let’s consider in more detail some of the central components of information processing that are affected by aging. Key Point Older adults respond more slowly than young adults and are more susceptible to being distracted. Older adults apparently learn new tasks, whether cognitive or motor, more slowly than younger adults do. For example, rote learning of cognitive material is slower in older adults because they need more repetitions to reach criterion—that is, to learn the material at a predesignated level. This may reflect the need for more time for the information to register in long-term memory. Similarly, older adults improve more slowly than younger adults in new motor skills, although they maintain well the skills they learned early in life (Szafran, 1951; Welford, 1980). Attentional factors also play a role in the performance limitations of older adults, who perform their fastest on a reaction-time task when a warning signal is given at a consistent interval before the stimulus and perform their slowest when the signal interval varies from trial to trial. This finding suggests that a fixed interval minimizes distraction by irrelevant associations (Birren, 1964). Rabbitt (1965) also demonstrated that older adults are hampered more than younger adults by the presence of irrelevant stimuli in a card-sorting task. In this task, participants are challenged to sort a stack of cards based on information given on the card face, such as the shape of a symbol or its color. If some information on the card face is not relevant to the sorting task, older adults’ performance suffers compared with that of younger adults. Key Point At any point in the life span, CNS factors play a much larger role in speed of cognitive processing than do peripheral factors. Many older adults are more easily distracted and attend less well to critical stimuli than when they were younger. The cause of this decline in performance might be a lowered signal-to-noise ratio in the CNS. The neural impulses of the CNS take place against a background of random neural noise such that the effectiveness of a neural signal depends on the ratio between the signal strength and the background noise—the signal-to-noise ratio. As a person ages, signal levels in the CNS decrease because of changes in the sense 492 organs, loss of brain cells, and factors affecting brain cell functioning; at the same time, noise level increases (Crossman & Szafran, 1956; Welford, 1977a). Older adults can compensate for this lower signal-to-noise ratio if they are given extra time to complete a task, but they are at a disadvantage if they must perform a series of movements or make a series of decisions rapidly. CNS factors also influence the slower speed of processing in older adults. Researchers have consistently documented a slowing of reaction time with aging. Although a slight slowing of neural impulse conduction velocity is associated with aging, it is not great enough to account for the magnitude of lengthened reaction times. Choice reaction time slows in older adults even more than does simple reaction time. Making a task more complex by increasing the number of signals or by designating responses that are less logical (e.g., pressing the left button in response to the right signal light) disproportionately increases older adults’ reaction time when compared with that of younger adults (Cerella, Poon, & Williams, 1980; Welford, 1977a,b). Choice reaction time is a measure requiring the earliest possible response to more than one stimulus, usually with a different response matched to each of the possible stimuli. Older adults’ movement time also shows a very slight slowing (Singleton, 1955), but older adults maintain the speed of planned, repetitive movements such as tapping (Earles & Salthouse, 1995; Fieandt, Huhtala, Kullberg, & Saarl, 1956; Jagacinski, Liao, & Fayyad, 1995). Because almost all behaviors mediated by the CNS slow down as an adult ages, central factors are assumed to be largely responsible for slower information processing speed (Birren, 1964). However, the schemata of older adults can be particularly complete and refined for skills the adult has had a lifetime of experience with. This experience is particularly helpful when accuracy is more important than speed. When older adults are not pressed to perform as quickly as possible, they demonstrate very accurate performance on well-practiced tasks. In our discussion to this point, you may have noticed that older adults have been discussed as if they form a homogeneous group. No distinction has been made between subgroups such as healthy adults and clinical populations, or active and inactive adults. Since the 1970s, however, research studies have shown that active older adults are closer to young adults in simple and choice reaction-time performance than are inactive older adults (Rikli & Busch, 1986; Spirduso, 1975, 1980). In 2003, Colcombe and Kramer published a meta- analysis of 18 aerobic fitness intervention studies with older adults. They found that fitness training improved performance on a variety of cognitive tasks. The greatest effect was for executive tasks (those involving planning, inhibition, and scheduling of mental processes), but speed, visuospatial, and controlled-process (e.g., choice reaction time) tasks also improved (figure 14.4). The benefits of training were greater if the intervention was a combined aerobic and strength program as compared with aerobic training alone, if the program lasted more than 6 months, if sessions lasted more than 30 min, and if exercisers 493 were “young-old” (55–65 years) or “mid-old” (66–70) rather than “old-old” (71–80). 494 Figure 14.4 Stanley Colcombe and Arthur Kramer conducted a meta-analysis showing that cognitive task performance was better at a second testing for both control and exercise groups. Yet, improvement in the exercise groups was much greater than that in the control groups. Among exercisers, groups combining strength and aerobic training improved somewhat more than those using aerobic training only. Four types of cognitive tasks were included: speed, visuopatial, controlled processing, and executive control. The greatest effect was on executive control tasks, but improvement occurred on all cognitive tasks. Data are from Colcombe and Kramer 2003. Great interest exists in learning how exercise exerts its favorable effect on cognitive functions, and researchers in this area can now take advantage of more widely available advanced imaging techniques, such as function magnetic resonance imaging (MRI). Colcombe et al. (2003) used MRI scans to show that aerobic fitness levels moderated loss of nervous system tissue with aging, and Colcombe et al. (2006) used MRI scans to find that brain volume increased in 60- to 79-year-olds who started an aerobic exercise program. So, aerobic fitness has a favorable effect on the brain structure of older adults. Is the same true of brain function? Colcombe et al. (2004) used functional MRI scans to see that fit adults showed greater activity in involved areas of the brain during a cognitive task compared with unfit adults (see also McAuley, Kramer, & Colcombe, 2004). This finding, along with those on training older adults to perform specific tasks (Erickson et al., 2007), suggests that the older adult brain retains more plasticity in functioning than was previously thought. A number of other factors could be involved in the beneficial effect of exercise, including improved vascular health in the brain and beneficial effects on insulin resistance and glucose intolerance (Weuve et al., 2004). Web Study Guide Observe reaction-time performance in individuals representing many parts of 495 the life span in Lab Activity 14.2, Age-Related Reaction Times, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Key Point Sufficient aerobic training can improve older adults’ performance on a variety of cognitive tasks. If you were a therapist at a residential facility for older adults, what activities might Colcombe and Kramer’s meta-analysis lead you to recommend for the residents? Why? 496 Summary and Synthesis Knowledge acts as a constraint in performing skills. Those with more knowledge make more appropriate responses in settings requiring decisions. They anticipate situations and actions and think of global strategies more than of specific, single occurrences. Acquiring knowledge takes time and experience. Children need experience to develop an extensive knowledge base, yet expert children can bring more knowledge to a situation than a novice adult can. Older adults with extensive experience can make their knowledge base a great asset in the performance of skills. Speed of cognitive processing does, however, differ over the life span. Although youth and older adult experts might surpass young adults with their knowledge, they might experience slower processing of that information. On the other hand, older adults who train aerobically at a sufficient level might moderate declines in cognitive function. Reinforcing What You Have Learned About Constraints Take a Second Look We have seen that knowledge is important to the performance of motor skills. It seems that the time and money athletes and teams spend on the technology that increases their knowledge about their own and competitors’ performance is well worth it! It is helpful to appreciate that knowledge about an activity is an individual constraint that can be changed quickly. Teachers and coaches who help performers increase their knowledge help their performance by doing so, sometimes at a more rapid rate than the improvement of physical skills. Test Your Knowledge 1. Name and define the types of knowledge, and provide an example of each, for one of your favorite sports or dance forms. 2. Describe four sport-specific ways that novices and experts differ in their knowledge of a sport. 3. What can youth athletes do better as they increase their expertise in their sports? 4. What are some factors that can influence performance on memory tasks? 5. Describe the trend in cognitive processing speed over the life span, assuming that middle-aged and older adults become more sedentary with age. 6. How does aerobic training affect cognitive functioning in older adults? What are the implications for activities of daily living and sport participation? 497 Learning Exercise 14.1 Teaching Strategies for Older Adult Learners Older adults may learn new things more slowly than young adults do. 1. Identify two strategies you would adopt to teach a new skill to an older adult. 2. Write a lesson plan for teaching this skill that uses each of your two strategies. Learning Exercise 14.2 Compensatory Strategies of Older Adults Older adults who participate in sport sometimes compensate for limitations in their movement (due, for example, to changes in the skeletal system, muscle strength, or flexibility) by anticipating their next movements based on experience in playing the game. Interview an older adult who participates fluently in a sport such as tennis, racquetball, or volleyball and ask him or her to describe making such adaptations. For example, Dodo Cheney, who won tennis championships into her 80s, recounts learning to position herself closer to the net (when in her younger days she would have positioned herself at the baseline) in order to reach drop shots or short balls. 498 Part VI Interaction of Exercise Task and Structural Constraints The theme of our study of motor development is that movement arises from the interaction of individual, environmental, and task constraints. We also note repeatedly that within each type of constraint, multiple systems interact. For example, chapter 5 examines individual structural systems—such as the skeletal, muscular, and neurological systems—that interact with growth and aging. Later chapters review perceptual systems, their interaction, and how they constrain movement. In part VI, we explore the interaction of task and structural constraints, focusing specifically on exercise. Over time, exercise leads to improved levels of physical fitness, which can be measured by changes in different structural constraints. Physical fitness has many systems or components, such as flexibility and strength. A person who is fit in one component is not necessarily fit in another. For example, an individual may be very strong but not very flexible. This part of the book discusses in detail the following structural constraints as components of fitness: cardiorespiratory endurance (chapter 15), strength (chapter 16), flexibility (chapter 16), and body composition (chapter 17). Some writers view fitness as including additional components, such as agility and power, but the four mentioned here are the essential systems that can be measured to show change in structural constraints. Potentially, a person can improve physical fitness through a systematic program of exercise aimed at these four components. An individual’s fitness level in each of the four systems—along with the interaction of these systems—can either permit or restrain movements over the life span. Fitness is important in every part of the life span, but older adulthood is the period when fitness may have the greatest influence on quality of life, through the movements permitted and the positive effect on all of the body’s systems. We begin by discussing endurance for vigorous activity. 499 Suggested Reading Bar-Or, O., & Rowland, T.W. (2004). Pediatric exercise medicine: From physiologic principles to health care application. Champaign, IL: Human Kinetics. Cahill, B.R., & Pearl, A.J. (Eds.). (1993). Intensive participation in children’s sports. Champaign, IL: Human Kinetics. Feltz, D.L. (Ed.). (2004). The Academy papers: Obesity and physical activity. Quest, 56, iv–170. Maud, P.J., & Foster, C. (Eds.) (2006). Physiological assessment of human fitness (2nd ed.). Champaign, IL: Human Kinetics. Rowland, T.W. (Ed.). (1993). Pediatric laboratory exercise testing. Champaign, IL: Human Kinetics. Rowland, T.W. (2005). Children’s exercise physiology (2nd ed.). Champaign, IL: Human Kinetics. Spirduso, W.W., Francis, K.L., & MacRae, P.G. (2005). Physical dimensions of aging (2nd ed.). Champaign, IL: Human Kinetics. Taylor, A., & Johnson, M. (2008). Physiology of exercise and healthy aging. Champaign, IL: Human Kinetics. 500 Chapter 15 Development of Cardiorespiratory Endurance 501 Chapter Objectives This chapter examines the body’s response to short-term vigorous exercise and how this response changes over the life span, reviews the effects of short-term exercise over the life span, studies the body’s response to prolonged exercise and how this response changes over the life span, and reviews the effects of endurance training over the life span. 502 Motor Development in the Real World Never Leave Your Living Room A movie trailer showed one person piloting a plane while another person, a passenger, asks, “Where did you learn to fly?” The answer? “PlayStation!” You might recognize the answer as one of the electronic game consoles that can be connected to a television. It seems as though electronic games for television or computer get better and more lifelike every year. You can play games based on all the major sports, both individual and team, and with many of the nuances (e.g., putting spin on your shots) of the real thing. It makes one wonder whether some people will ever leave their living room to actually play the sports! When time spent playing video games is added to the hours spent watching television and using computers, it is easy to see why concern about the fitness level of people in Westernized countries, even children, is so widespread. Components of physical fitness can act as individual constraints to most activities; some components are more important to some kinds of physical activities than others. A lack of fitness can easily serve as a rate limiter to the performance of motor skills and the physical activities of everyday living. Indeed, fitness is related to one’s very quality of life. Of course, a relationship exists between the growth and aging of the body and of its systems (structural constraints) and the fitness components, and a relationship exists between functional constraints and training for the maintenance or improvement of fitness. It is important to understand how these various structural and functional individual constraints interact in the performance of skills. Of all the fitness components, cardiorespiratory endurance has the greatest implications for lifelong health, but its development in children is surrounded by many myths. For many years, experts thought that children’s cardiovascular and respiratory systems limited their capacity for extended work. They thought so because measurements of children’s blood vessel size were misinterpreted. Even though the mistake was soon discovered, the myth has persisted for decades (Karpovich, 1937). In addition, many parents and teachers think that children automatically get enough exercise to become and remain fit. This belief serves as a social constraint to children’s regular and systematic participation in exercise. Recent studies documented a worldwide trend toward reduced fitness (Tomkinson & Olds, 2007; Tomkinson, Olds, Kang, & Kim, 2007), showing that the sedentary lifestyle that many of today’s adults have adopted has spilled over to the lives of their children. A high percentage of children and teenagers already exhibit one or more of the risk factors for coronary heart disease, and far too many are obese. Children in poor physical condition are likely to 503 maintain that status throughout their adult lives. Educators and exercise leaders must thoroughly understand development of and potential for cardiorespiratory endurance so that they can challenge children to attain an appropriate level of fitness for vigorous activity. Cardiorespiratory endurance reflects one’s ability to sustain vigorous activity. It is important for two broad reasons. First, participation in many physical activities demands sustained vigorous exertion. Second, the health of the cardiac, vascular, and respiratory systems is related to endurance level, largely because training that improves endurance makes these systems more efficient. In this chapter we review the body’s basic physiological responses to both short-term and long-term vigorous activities. We also discuss the changes that occur in these responses with growth and aging. 504 Physiological Responses to Short-Term Exercise Vigorous physical activity can be a short burst of intense exercise, a long period of submaximal or maximal work, or a combination of these types. Our bodies meet the differing demands of brief, intense activity and of longer, more moderate activity with different physiological responses. During a brief period (10 s) of intense activity, the body responds by depleting local reserves of oxygen and sources of energy stored in the muscles, thus creating a deficit of oxygen that must eventually be replenished. These are anaerobic (without oxygen) systems. Anaerobic system performance can be reflected in measurements of anaerobic power and anaerobic capacity. Anaerobic power is the rate at which a person’s body can meet the demand for short-term, intense activity. Anaerobic capacity is the maximum oxygen deficit that a person can tolerate. As the period of exercise demand grows longer, the anaerobic systems contribute less to the body’s response. Respiration and circulation increase to bring oxygen to the muscles. Ninety seconds into an exercise bout, anaerobic and aerobic (with oxygen) energy systems contribute about equally. After 3 min, aerobic processes meet the demands of exercise. The types of exercise that promote anaerobic performance, then, are vigorous but of short duration, whereas those that promote aerobic performance are sustained and consequently less vigorous or intense. Developmental Changes in Anaerobic Performance At any age, anaerobic performance is related to body size, particularly fat-free muscle mass and muscle size; the ability to metabolize fuel sources in the muscles; and quick mobilization of oxygen delivery systems. Some of these factors change as a person grows (Malina, Bouchard, & Bar-Or, 2004). Childhood Young children have smaller absolute quantities of energy reserves than adults do because they have less muscle mass (Eriksson, 1978; Shephard, 1982). Therefore, children attain less output of absolute anaerobic power than adults do. As children grow their muscle mass increases, as do their energy reserves. They also can better tolerate the by-products of the metabolic process. Thus, mean and peak anaerobic power improve steadily as a person ages (Duche et al., 1992; Falgairette, Bedu, Fellmann, Van Praagh, & Coudert, 1991; Inbar & 505 Bar-Or, 1986). Total work output scores improve over the entire adolescent period in boys but only until puberty in girls, perhaps reflecting the patterns of muscle growth in the sexes (figure 15.l, a and b) or sociocultural views of appropriate activities for girls. Accounting for differences in muscle mass, however, does not entirely eliminate the differences in anaerobic performance that favor boys (Van Praagh, Fellmann, Bedu, Falgairette, & Coudert, 1990). Not all of the differences between children and adults are attributable to difference in body size, either. When we divide anaerobic performance scores by body weight, scores still improve with age. 506 Figure 15.1 Anaerobic performance. (a) Change in total work output (measured in Joules) on a 10 s all-out bicycle ergometer ride with advancing age. C. Bouchard and J.A. Simoneau (unpublished data) measured a cross-sectional group of French Canadian youths on this task. When anaerobic performance scores are divided by body weight, as in (b), scores still improve with age. Reprinted by permission from Malina, Bouchard, and Bar-Or 2004. Undoubtedly, better neuromuscular coordination and skill contribute to improved anaerobic performance as children grow older, and the capacity for energy production improves with age (Rowland, 1996). Armstrong, Welsman, and Kirby (1997) found that advancing maturation independent of body mass was related to higher mean and peak anaerobic power. More mature children can be expected to show better anaerobic performance than other children even if they are similar in body size to less mature children (Tomkinson, Hamlin, & Olds, 2006). Key Point Anaerobic fitness improves with growth but at a faster rate than can be explained by growth alone. 507 Adulthood Once individuals attain adult body size, their anaerobic performance remains stable throughout young adulthood (Inbar & Bar-Or, 1986). Any improvement in anaerobic power and capacity is achieved through training alone. The anaerobic systems of older adults do not produce energy as quickly as those of younger adults; this decline is likely associated with loss of muscle mass. At the same level of exercise, older adults accumulate by-products of energy metabolism sooner than young adults do (Spirduso, Francis, & MacRae, 2005). The loss of anaerobic power was recorded as 50% by age 75 (Grassi, Cerretelli, Narici, & Marconi, 1991). It is not clear, however, whether anaerobic power and capacity necessarily decline as adults grow older. Those engaged in lifelong intense training showed no deterioration in anaerobic performance (Reaburn & Mackinnon, 1990). These master athletes likely maintained much of their muscle mass. Because any loss of muscle mass in older adults is likely to affect anaerobic performance, a lack of training in anaerobic tasks to maintain conditioning logically would affect performance. Key Point Declines in anaerobic performance in older adults reflect a loss of muscle mass. Anaerobic Training Although the results of training studies are somewhat inconsistent, preadolescent and adolescent boys have demonstrated improved anaerobic power with anaerobic training (Grodjinovsky, Inbar, Dotan, & Bar-Or, 1980; Rotstein, Dotan, Bar-Or, & Tenenbaum, 1986). Improvements are not large, and some cross-sectional comparisons show no anaerobic differences between active and nonactive boys (Falgairette, Duche, Bedu, Fellmann, & Coudert, 1993; Mero, Kauhanen, Peltola, Vuorimaa, & Komi, 1990). Improvements with training might be associated with metabolizing energy reserves more efficiently, therefore improving anaerobic capacity (Eriksson, 1972). Less is known about girls, but McManus, Armstrong, and Williams (1997) found small improvements in prepubescent girls with both cycle and sprint running training. Assessing Anaerobic Performance 508 No direct, noninvasive methods of measuring anaerobic fitness exist, so it is typically studied through short-duration tasks. The Quebec 10 s and the Wingate 30 s all-out rides on a bicycle ergometer and the Margaria step-running test are common laboratory tests that provide scores in total work output, mean power, or peak power. Total work output indicates how much absolute work an individual can do in a 10 or 30 s time period. In contrast, power indicates the rate at which individuals can produce energy—that is, the work they can do per a specific unit of time. Mean power is the average power individuals achieve during the 10 or 30 s period, whereas peak power is the highest rate they achieve. The 50 yd dash and sprinting a flight of stairs are common field tests. Participants must be willing and able to give an all-out effort in order to provide an accurate measure of anaerobic performance. Taking anaerobic measurements can be difficult or even dangerous with older adults, especially those who have been inactive. Imagine you are a physical education teacher. Which games that students play provide anaerobic training at the elementary school level? At the middle school level? Little is known about how untrained older adults respond to anaerobic training, although training programs that improve muscle mass are likely to bring about an improvement of anaerobic performance. Reaburn and Mackinnon (1990) studied master athletes over 46 years of age who were training for world swimming competition. After sprint swimming, these athletes produced and removed lactic acid just as well as younger adults. Therefore, long-term training of sufficient intensity might maintain anaerobic performance. Key Point Anaerobic training improves the anaerobic performance of preadolescent children and maintains that of master athletes. 509 Physiological Responses to Prolonged Exercise How do our bodies sustain submaximal physical activity for prolonged periods? Unlike in short-term exercise, the energy for prolonged exercise is derived from aerobic systems—the oxidative breakdown of food stores—in addition to the local reserves depleted in the first few minutes of exercise. The success with which we meet the needs of prolonged activity can be indicated by measurements of aerobic power and aerobic capacity. Aerobic power is the rate at which long-term oxygen demand is met during prolonged activity. Aerobic capacity is the total energy available to meet the demands of prolonged activity. Sustained, prolonged activity depends on the transportation of sufficient oxygen to the working muscles for longer periods. The needed oxygen is delivered through increases in heart and respiratory rates, cardiac output, and oxygen uptake. An increased respiratory rate brings more oxygen to the lungs, making it available for diffusion into the bloodstream. Increased cardiac output (the amount of blood pumped into the circulatory system) allows more oxygen to reach the muscles. The body achieves this increased cardiac output through increased heart rate or increased stroke volume. Changes in stroke volume during exercise are relatively small, but one of the long-term benefits of training is greater stroke volume. The limiting factor to continued vigorous activity is the heart’s ability to pump enough blood to meet the oxygen needs of the working muscles. When individuals engage in very heavy activity, their heart rates increase throughout the session until exhaustion ends the activity. When they stop vigorous activity, their heart rates decrease quickly for 2 to 3 min and then more gradually for a time related to the duration and intensity of the activity. Fit individuals regain their resting heart rates more quickly than unfit individuals. This description is only a brief summary of the physiological responses to exercise. A more detailed treatment is available in exercise physiology textbooks. Changes in Aerobic Performance During Childhood How do children respond physiologically to prolonged activity? Children tend to have hypokinetic circulation (Bar-Or, Shephard, & Allen, 1971); that is, their cardiac output is less than an adult’s (cardiac output is the product of stroke volume and heart rate). Children have a smaller stroke volume than adults, reflecting their smaller hearts. They compensate in part with higher heart rates than adults at a given level of exercise, but their cardiac output is still somewhat lower than an adult’s. Children also have lower blood hemoglobin concentrations than do adults; hemoglobin concentration is related to the blood’s ability to carry oxygen. 510 Hemoglobin is the protein in the blood that carries oxygen. You might assume that these two factors in children, hypokinetic circulation and low hemoglobin concentration, result in an oxygen transport system that is less efficient than that in adults. However, children can extract relatively more of the oxygen circulating to the active muscles than adults can (Malina & Bouchard, 1991; Shephard, 1982), which seems to compensate for these factors. The result is a comparably effective oxygen transport system. Children also mobilize their aerobic systems faster than adults do (Bar-Or, 1983). Key Point Children’s physiological response to endurance activity is very efficient, but children cannot exercise as long as adults can. Children do have a lower tolerance than adults for extended periods of exercise, perhaps due to smaller energy reserves in the muscles. As children grow, their hypokinetic circulation is gradually reduced and their response becomes increasingly similar to the adult response. Longitudinal and cross-sectional studies demonstrate that absolute maximal oxygen uptake increases linearly in children from age 4 until late adolescence in boys and until age 12 or 13 in girls (Krahenbuhl, Skinner, & Kohrt, 1985; Mirwald & Bailey, 1986; Shuleva, Hunter, Hester, & Dunaway, 1990). Maximal oxygen uptake is the most common measure of fitness for endurance activities. Figure 15.2a shows this trend between ages 6 and about 18 years of age. Boys and girls are similar in maximal oxygen uptake until about age 12, though boys have a slightly higher average. After this age, maximal oxygen uptake plateaus in girls but continues to increase in boys. The increase with age is related to growth of the musculature, lungs, and heart. Maximal oxygen uptake is the highest amount of oxygen the body can consume during aerobic work. 511 Figure 15.2 The relationship between maximal oxygen uptake and age. (a) Absolute scores. (b) Maximal oxygen uptake values relative to kilograms of body weight. The boys’ scores are centered in the shaded area and the girls’ scores are centered in the unshaded area. Reprinted by permission from Bar-Or 1983. Key Point Absolute maximal oxygen uptake increases in boys throughout childhood and adolescence and in girls until age 12, after which it plateaus. Key Point Maximal oxygen uptake per kilogram of body weight is stable in boys and declines slightly in girls throughout childhood and adolescence. A strong relationship exists between absolute maximal oxygen uptake and lean body mass. In fact, maximal oxygen uptake can be expressed in relative rather than absolute terms, dividing it by body weight, lean body weight, or another body dimension. As figure 15.2b 512 shows, maximal oxygen uptake relative to body weight stays about the same throughout childhood and adolescence in boys. It declines in girls, probably because adipose (fat) tissue increases. When maximal oxygen uptake is related to fat-free mass, scores show a slight decline during and after puberty and small sex differences remain. Key Point Maximal oxygen uptake is related to body size, especially lean body mass, as well as to maturity status. Thus, body weight appears to increase slightly faster than maximal oxygen uptake around puberty (Malina & Bouchard, 1991). Maximal oxygen uptake might depend somewhat on maturity in addition to body size; comparisons of maximal oxygen uptake with age show a relationship in adolescents who vary in age but are identical in size (Sprynarova & Reisenauer, 1978). Two adolescents identical in size could differ in maximal oxygen uptake if one were more physiologically mature than the other. Even though maximal oxygen uptake is the best single measurement of endurance, it might not predict running performance in children as well as it does in adults. Running test performance relates more to anaerobic measures in children than in adults. Caution should be used when inferring maximal oxygen uptake levels from children’s running performance. It is important to recognize the relationship between children’s increasing body size and their improving ability to sustain exercise during growth. With body growth come increases in lung volume, heart and stroke volume, total hemoglobin, and lean body mass. These factors foster improved cardiac output and, subsequently, improved exercise capacity and absolute maximal oxygen uptake. Because children vary in size despite their chronological age, evaluations of exercise capacity among children should relate to body size rather than chronological age alone. In the past, educators frequently based evaluation only on age. Average exercise capacity and average body size of groups of children and adolescents generally increase with age, but exercise capacity is also related to maturation rate. As noted in chapter 4, the relationship between chronological age and maturation status is imperfect. Therefore, each child’s unique size and maturity level should be considered when establishing expectations for endurance performance. In children, body size is a far better predictor of endurance than the child’s sex. After puberty, however, boys on average attain a considerable edge over girls in absolute maximal oxygen uptake and have the potential to retain this edge throughout life. Several factors contribute to this sex difference. One is body composition. The average man gains more lean body mass and less adipose tissue during adolescence than does the average woman. Women are similar to men in maximal oxygen uptake per kilogram of fat-free body mass, 513 but when adipose tissue is included, women have a lower maximal oxygen uptake. Another factor in sex differences in oxygen consumption is that women tend to have lower hemoglobin concentrations than men do (Åstrand, 1976). Key Point Imagine you are an administrator for the youth sports program at a community center. If maximal oxygen uptake is related to body composition and physical maturity, what would this mean for the ways in which you group children in teams? What should be the criteria when grouping children into teams for contact or combative sports? For noncontact sports? By the time the average male reaches late adolescence he has an edge over the average female in both oxygen consumption and working capacity (figure 15.3). It must be remembered that environmental factors, especially training, influence the endurance capacities of individual men and women throughout their lives. Thus, it would not be surprising to find that an active woman has a higher maximal oxygen uptake than a sedentary man. 514 Figure 15.3 Physical working capacity with advancing age in childhood and adolescence. Measurements were made in school classrooms (without habituation of the Canadian subjects) at a heart rate of 170 beats per minute. Readings would probably be up to 10% higher given climatic control (20–22 °C) and some familiarization with experimental procedures. Measurements are in (a) watts (1 watt = 6 kg·m−1·min−1) and (b)watts per kilogram of body weight. Data from Shephard 1982. Laboratory studies tend to conclude that children have greater metabolic requirements for moving their body mass in such activities as walking or running than adults do (Bar-Or, 1983; Rowland, 1996). This finding, which implies that children are at a disadvantage in endurance exercise compared with adults, might actually be an artifact of laboratory studies. When exercise economy is scaled to body mass, differences between boys and men are eliminated (Eston, Robson, & Winter, 1993). That is, laboratory studies tend to compare children and adults at identical workloads when it is likely that they exercise at a metabolic rate scaled to body size (Rowland, 2012). Children, then, are as economical as adults. This finding is interesting in light of our emphasis on the importance of body scaling. Web Study Guide Analyze children’s scores on a test of prolonged moderate activity and note 515 sex differences in Lab Activity 15.1, Changes in Youth Endurance Performance, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Assessing Aerobic Performance Several methods can be used to assess a person’s physiological responses to sustained activity requiring repetitive contraction of the large muscles. The most common activities used are cycling on an ergometer or walking or running on a treadmill. The effort asked of the individual can be submaximal or maximal. Measurements of aerobic power and capacity tend to be specific to the task performed (e.g., cycling, running), so caution must be used in comparing scores on different tasks. Young children have difficulty keeping a cadence during bicycle ergometer tests. They are also more likely than adults to make unnecessary movements during testing. In addition, they may be at risk of falling on a treadmill. Aerobic exercise tests are usually graded; that is, they increase workload in stages. There is no standard protocol for any age group, but intensity should always be appropriate for the fitness level and size of those tested. A common measure of fitness for endurance activities is maximal oxygen uptake, or maximal aerobic power, which is the maximum volume of oxygen the body can consume per minute (Heyward, 1991; Zwiren, 1989). The more efficiently a person’s body uses oxygen (i.e., the less oxygen consumed for the same amount of work performed), the more fit the individual. Endurance exercise performance is too complex to be represented by any single measure, yet maximal oxygen uptake and endurance activity performance are highly correlated, making maximum oxygen uptake one of the preferred measures of endurance. In a test assessing maximal oxygen uptake, you can measure or estimate the amount of oxygen consumed during activity. This score is also expressed as oxygen consumed per minute per kilogram of body weight. Maximal oxygen uptake is a common measure of endurance in studies of children and older adults because it can be estimated from a submaximal test, thus avoiding the need for exercise to exhaustion. Direct measures of oxygen use require more sophisticated and expensive equipment than is needed for estimates from submaximal tests. Another measure of physiological response to prolonged exercise is maximal working capacity, which means the highest work, or exercise, load a person can tolerate before 516 reaching exhaustion (Adams, 1973). Because this test requires maximal effort, it requires motivating individuals to work to exhaustion. Although it is unlikely, an individual may have a heart attack during such a test. For this reason this measure is not often used with children or older adults. Other measures of endurance fitness are less common. For example, maximal cardiac output can be directly measured, but this test is difficult to administer because it requires intubation (inserting a tube into the body). Measuring an individual’s electrocardiograph changes during exercise is of interest when studying adults (Heyward, 1991), but it does not apply very well to most children because its main purpose is to identify impaired heart function. Therefore, the preferred research measure of endurance fitness in children and older adults is maximal oxygen uptake for changes in aerobic power and adaptations to submaximal exercise efforts for changes in aerobic capacity. Several research investigators attempted to identify field tests for children that estimate endurance nearly as reliably as when it is measured in a laboratory. Such field tests allow educators to measure aerobic performance without laboratory equipment. The investigators compared maximal oxygen uptake scores from laboratory tests with performance in 800 m, 1200 m, and 1600 m runs for 83 children in the first, second, and third grades. Performance on the 1600 m run was a better predictor of maximal oxygen uptake for boys and girls than performance on the 800 m or 1200 m runs. An average velocity score on the 1600 m run had a slightly higher correlation with maximal oxygen uptake than a total time score. We can conclude that a 1600 m run is a better field test of endurance in children than shorter runs. This test proved to have high test–retest reliability (Krahenbuhl, Pangrazi, Petersen, Burkett, & Schneider, 1978). In young trained runners, the correlation between maximal oxygen uptake and race time is high (Cunningham, 1990; Unnithan, 1993). Aerobic Performance in Adulthood Average maximal oxygen uptake per kilogram of body weight peaks in the 20s, then decreases throughout the adult years. The loss is approximately 1% per year of life. The decline is found in both cross-sectional and longitudinal research and among athletic, active, and sedentary adults (Spirduso, 1995). Athletic and active adults, however, maintain a higher maximal oxygen uptake than sedentary adults. This section discusses the structural and functional changes in the cardiovascular and respiratory systems that contribute to this decline. Cardiovascular Structure and Function 517 Cardiovascular function is related to the structure of the heart and blood vessels. The major structural changes in a nondiseased heart with aging include a progressive loss of cardiac muscle, a loss of elasticity in cardiac muscle fibers (Harrison, Dixon, Russell, Bidwai, & Coleman, 1964), a thickening of the left ventricular wall, and fibrotic changes in the valves (Pomerance, 1965). The major blood vessels also lose elasticity (Fleg, 1986). It remains unclear whether these changes are unavoidable in aging or reflect a chronic lack of oxygen. The effects of these structural changes on cardiovascular function are numerous. Maximum heart rate. Whereas resting heart rate values of older adults are comparable with those of young adults, the maximum achievable heart rate with physical exertion gradually declines with aging (Lipsitz, 1989). The difference is about 188 beats per minute for persons in their 20s and 168 for persons in their 50s and 60s (Spirduso, 1995). Decreased maximum heart rate may be the major factor in reduced maximal oxygen uptake with aging (Hagburg et al., 1985). Stroke volume. The stroke volume of older adults may or may not decline with aging; research studies have yielded both results (see Stamford, 1988, for a review). Asymptomatic ischemic heart disease (affecting blood supply to the heart) may account for the equivocal results. Investigators using rigorous screening for heart disease may find no decrease in stroke volume, whereas researchers whose studies include participants with undetected disease may find a decrease (Safar, 1990). Cardiac output. Cardiac output is the product of heart rate and stroke volume. Healthy older adults experience a decline in cardiac output during heavy activity with a decline in maximal oxygen uptake, and those with ischemic heart disease experience even greater decline as maximum heart rate and stroke volume decrease. Cardiac output at rest or with submaximal work is unchanged with aging, and cardiac output is much higher in adults who train aerobically than in sedentary adults. Blood pressure. Older adults reach their peak cardiac output at a lower intensity of work than do younger adults (Brandfonbrener, Landowne, & Shock, 1955; Shephard, 1978a). Older adults’ more rigid arteries resist the volume of blood the heart pumps into them. This resistance is even greater if the person has atherosclerosis (the buildup of plaque on the artery walls). In turn, this resistance raises resting pulse pressure (the difference between systolic and diastolic blood pressure) and systolic blood pressure. Whether blood pressure increases or decreases during exercise also depends on the health of the cardiac muscle fibers and their ability to tolerate an increased workload. A lifestyle that includes regular physical activity is associated with lower systolic blood pressure (Reaven, Barrett-Connor, & Edelstein, 1991). Blood flow and hemoglobin content. For activity to be sustained, oxygen must be delivered to the working muscles by the blood. Peripheral blood flow is apparently well maintained in older adulthood. Hemoglobin is also maintained in older adulthood (Timiras & Brownstein, 1987), but the incidence of anemia increases in older adulthood. This condition is associated with reduced hemoglobin values. 518 Respiratory Structure and Function Elasticity of the lung tissue and chest walls declines with aging (Turner, Mead, & Wohl, 1968). Therefore, older adults expend more effort in breathing than young adults. Of interest is the lung volume, especially the volume termed forced vital capacity . A large vital capacity reflects a large inspiratory capacity of the lungs and results in better alveolar ventilation. Because the greatest part of oxygen diffusion to the capillaries takes place at the alveoli (figure 15.4), better alveolar ventilation contributes to increased amounts of oxygen circulating in the blood and reaching the working muscles. Forced vital capacity is the maximum volume of air the lungs can expel after maximal inspiration. 519 Figure 15.4 The respiratory system. Oxygen diffusion to the capillaries takes place at the alveoli, enlarged in the figure. A decreased vital capacity with aging is well established; the average decrease is 4% to 5% per decade (Norris, Shock, Landowne, & Falzone, 1956; Shephard, 1987). The loss is more dramatic in smokers than in nonsmokers, and well-trained persons in their 40s are known to maintain the vital capacity of their 20s (Shephard, 1987). Key Point Cardiovascular factors are a greater limitation to older adults’ aerobic performance than are pulmonary factors. The oxygen and carbon dioxide exchange in the lungs loses some efficiency with aging, and this decline is not offset by training (Dempsey, Johnson, & Saupe, 1990). Generally, though, the pulmonary systems of older adults do well at rest and during moderate activity. Moreover, the pulmonary system is not the major limiting factor in exercise capacity. Key Point Maximal oxygen uptake declines throughout adulthood, a trend that is related to a decrease in maximum heart rate and in muscle mass. Active older adults maintain an edge in maximal oxygen uptake over those who are sedentary. 520 Changes in Muscle Mass The decline in maximal oxygen uptake is probably related both to loss of muscle mass and the ability of muscles to use oxygen and to cardiovascular and respiratory changes. Maximal oxygen uptake measures the amount of oxygen delivered to and used by the muscles. So, the more muscle mass, the more likely maximal oxygen uptake is greater. In fact, when maximal oxygen uptake is related to the kilograms of muscle (rather than kilograms of body weight) in older adults, declines shrink from 60% to 14% in men and 50% to 8% in women (Spirduso, 1995). Thus, maintenance of muscle mass is a factor in minimizing the loss of endurance performance. The addition of adipose tissue with aging works against maintenance of maximal oxygen uptake. The end result of cardiac and pulmonary changes and of the loss of muscle mass is that maximum exercise capacity and maximal oxygen uptake (whether absolute or relative to body weight) decline as an adult ages and the recovery period after vigorous activity lengthens. The results of both longitudinal and cross-sectional studies are plotted in figure 15.5, and a decline with advancing age is evident. A lifetime of negative environmental factors, such as smoking or poor nutrition, can contribute to or accelerate the changes. Conversely, a lifetime of exposure to positive environmental factors, such as healthful exercise, can better maintain endurance levels. If you were a personal trainer with older adult clients, what types of training activities would you plan in order to help them improve their aerobic performance? 521 Figure 15.5 Maximal oxygen uptake declines as a person ages. (a) Cross-sectional studies and longitudinal studies show declines in adulthood. (b) The decline is not as rapid in active adults. The dotted lines represent the change in inactive adults and the solid lines represent the change in active adults. Data plotted are from (a) Dehn and Bruce (1972), (b) Dill et al. (1967), (c) Hollman (1965), and (d) Dehn and Bruce (1972) (all cited in Dehn & Bruce, 1972). Reprinted, by permission, from B.A. Stamford, 1986, Exercise and the elderly. In Exercise and sport sciences reviews, Vol. 16, edited by K.B. Pandolf (New York: Macmillan), 344. © The McGraw-Hill Companies. Changes with growth and aging dramatically affect endurance performance throughout the life span. Various systems can constrain the potential for vigorous, sustained activity. It is important for everyone to have some knowledge of how the various systems influence aerobic activity because of the health implications of regular participation in aerobic activity. Moreover, educators and therapists must understand these influences thoroughly in order to promote training that yields significant health benefits. Endurance Training The result of aerobic training is predictable in adults. An adult improves maximal oxygen uptake by training at least 3 times a week for at least 20 min at an intensity of 60% to 90% of maximum heart rate. Stroke volume increases, and maximal cardiac output subsequently 522 increases. Oxygen is better extracted from the blood at muscle sites. Maximal ventilation per minute rises. Inactive adults who begin training typically increase maximal oxygen uptake by 25% to 50% (Hartley, 1992). Therefore, appropriate training yields benefits. Let’s consider whether the same is true for children. Training Effect in Children Children who begin an aerobic training program are continuing to grow, and, as noted earlier, maximal oxygen uptake increases with growth. Therefore, to know the effect of training in children, we must distinguish any increase in maximal oxygen uptake due to growth from that due to training. In research studies, this need for differentiation makes it absolutely necessary to include a control group that grows but does not train. Moreover, as noted in chapter 4, children mature at different rates. Comparing a group that contains many early maturers with a group that contains many late maturers can certainly bias an investigation of training effects. In fact, one research group noted that when they sought to compare inactive and active groups of children, late maturers more often fell into the inactive category (Mirwald, Bailey, Cameron, & Rasmussen, 1981). Thus, maturation level must be assessed in research studies. Early studies of aerobic training in prepubescent children were equivocal. Consider the seven sample studies depicted in figure 15.6. Three found a significant increase in maximal oxygen uptake by the training group over the control group, and four found no significant difference after training. In a few longitudinal studies, training did not result in differences between active and inactive groups until the children reached peak height velocity (Kobayashi et al., 1978; Mirwald et al., 1981; Rutenfranz, 1986). In other words, training was not associated with a higher maximal oxygen uptake in preadolescents (beyond the increase due to growth), but it was in adolescents. This led Katch (1983) to propose the trigger hypothesis, which states that until the results of the hormones that initiate puberty are realized, the effects of aerobic training on maximal oxygen uptake are minimal at best. Key Point The effect of growth and maturation on maximal oxygen uptake must be distinguished from the effect of training. 523 Figure 15.6 Changes in maximal oxygen uptake with training of prepubescent children. The three bars marked with an asterisk (*) indicate studies finding a statistically significant improvement over the control group. The other four studies found no significant differences after training. Reprinted by permission from Zwiren 1989. Several factors could account for the lack of a training effect: Training effects may be reliant on hormonal responses. High activity rates in children before training may minimize the training effect. Research studies may have included too few children or may have used flawed research methods. Training intensity may have been insufficient for children (Rowland, 1989b). Maximal oxygen uptake may not be as useful a measurement of aerobic fitness in children as other measures (e.g., anaerobic threshold or ventilatory anaerobic threshold) (Armstrong & Welsman, 2007; Rowland, 1989a; Washington, 1989). Aware of these possibilities, investigators more recently based their reviews on studies meeting certain criteria for research method and training intensity. Pate and Ward (1990) screened and then analyzed 12 studies; 8 of these found an increase in maximal oxygen uptake with training, though the mean increase was only 10.4%, compared with the control group increase of 2.7%. Similar analyses led some to conclude that appropriate aerobic training can lead to increases in maximal oxygen uptake of approximately 15% in children (Sady, 1986; Shephard, 1992; Vaccaro & Mahon, 1987). Payne and Morrow (1993) reported from their meta-analysis of 23 studies that an average 524 increase in fitness of less than 5% was found. Tolfrey, Campbell, and Batterham (1998) trained 26 children (boys and girls) matched to 19 control children for physiological maturation. The children trained 30 min per session 3 times per week for 12 weeks at nearly 80% of maximum heart rate. Habitual physical activity level and percentage body fat were considered in the analysis of the results, but the exercise-training group still did not improve in maximal oxygen uptake beyond the improvement also seen in the control group. It is possible that research studies on the whole have not used training intensity levels that are sufficient to yield a training effect in children. However, exercise programs must not be so demanding as to be harmful, nor should they involve a level of activity that results in children disliking exercise. The overload principle is useful to follow with children. This principle calls for training with incrementally—not drastically—increased intensity or duration slightly beyond the individual’s norm. The intensity or duration of each exercise bout is increased gradually over several weeks or months. Key Point Aerobic training yields small improvements, at best, in preadolescents but significant improvements after puberty. In contrast, adolescents after puberty respond to aerobic training much as adults do. Heart size and volume, total blood volume, total hemoglobin, stroke volume, and maximal cardiac output all increase in adolescents who receive training (Ekblom, 1969; Eriksson & Koch, 1973; Koch & Rocker, 1977; Lengyel & Gyarfas, 1979), whereas submaximal heart rate for a given level of exercise decreases (Brown, Harrower, & Deeter, 1972). Kobayashi et al. (1978) found a 15.8% increase in aerobic power with training between ages 14 and 17. A longitudinal study of males and females over the 15 years from ages 13 through 27 showed that those who reported being physically active had a 2% to 5% increase in aerobic fitness over those who did not (Kemper, Twisk, Koppes, van Mechelen, & Post, 2001). Training is specific even in adolescents. Santos, Marinho, Costa, Izquierdo, and Marques (2012) noted that strength training in adolescent boys between 12 and 14 years did not improve aerobic capacity, but programs concurrently providing resistance and endurance training led to an improvement in both fitness components. Imagine you are a physical education teacher serving on a districtwide committee charged with revising the physical education curriculum. How much activity that is characterized as aerobic training would you favor at each grade level from kindergarten through 12th grade? What about anaerobic- training activities? Would you advocate having high school students complete 525 their physical education requirement as sophomores? Training Programs in Adulthood Earlier we reviewed the structural and functional changes that occur with aging in the cardiovascular and respiratory systems. We observed that maximal oxygen uptake declines with aging, even in those who train. However, we also noted that training and active adults had higher—even if declining with time—maximal oxygen uptakes than did sedentary adults (Hollenberg, Yang, Haight, & Tager, 2006) (figure 15.7). This finding provides a hint that training programs for adults yield benefits. Let’s consider two groups: adults who maintain an active lifestyle and sedentary adults who take up training. 526 Figure 15.7 These average maximal oxygen uptake values for the adult years show that activity is associated with higher values, although all groups (active and inactive) experience decline. The dotted lines represent improvement in sedentary adults with training. Reprinted by permission from Spirduso, Francis, and MacRae 2005. First, evidence exists (Dehn & Bruce, 1972; Drinkwater, Horvath, & Wells, 1975; Kasch, Boyer, Van Camp, Verity, & Wallace, 1990; Shephard, 1978b; Smith & Serfass, 1981) that declines are not as dramatic in older adults who remain active as in those who become sedentary. Figure 15.5b shows that the decline in maximal oxygen uptake is steeper in inactive (dotted line) than in active (solid line) adults with advancing age. Vigorous training can even keep maximal oxygen uptake steady for a time in older adults (figure 15.7; Kasch & Wallace, 1976). Over a long period of time, prolonged training can sharply reduce the decline in maximal oxygen uptake. Kasch et al. (1990) observed only a 13% decline in men 45 to 68 years of age who maintained exercise training compared with an average loss of about 40% in nonexercisers. Second, older adults can significantly increase their maximal oxygen uptake with a good training program (Posner, Gorman, Klein, & Woldow, 1986; Shephard, 1978b), even if they have undertaken little training earlier in their lives and even into their 70s (Hagburg et al., 1989; Stamford, 1973). Improvements ranged from 10% to 25% (Blumenthal et al., 1991; Shephard, 1987). These gains are not as high in absolute terms as those in younger people who begin training but are similar to those gains in the young in relative terms. Even low training intensity can be very effective for older adults early in their exercise program. Inactive older adults taking up aerobic training also improve in other strength and mobility tasks (Kalapotharakos, Michalopoulos, Strimpakos, Diamantopoulos, & Tokmakidis, 2006) and improve their blood lipids (Ring-Dimitriou et al., 2007). Key Point 527 Adults can benefit from aerobic training in order to minimize the decline in performance that would otherwise accompany aging. What mechanisms are involved in the improvements in older adults with training? Undoubtedly, training maintains or improves muscle mass. As mentioned previously, more muscle mass is associated with higher maximal oxygen uptake. Fit older adults also have larger vital capacity than sedentary older adults (Shephard, 1993). Information about the cardiovascular system is available from a case study of Clarence DeMar, who ran 12 miles a day throughout his life and competed in marathons at age 65. The autopsy performed after his death from cancer at age 70 showed well-developed cardiac muscle, normal valves, and coronary arteries two to three times the size normally seen (Brandfonbrener et al., 1955). Although the benefits of endurance training even at low intensity are well established for the older adult, vigorous work can still overwhelm the diseased heart. Older adults with cardiovascular disease should participate in programs designed specifically for this population. The guiding principle in designing training programs for older adults as well as for children is a gradual increase in exercise intensity and duration. Web Study Guide Compare age group records for aerobic and anaerobic events and identify trends in performance among older adults in Lab Activity 15.2, Aerobic and Anaerobic Performance in Older Adulthood, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Long-Term Training Effects Despite the body’s favorable response to training at any age, the question arises of whether active youths have an advantage over their sedentary counterparts in maintaining endurance into older adulthood. Ideally, researchers would assess this aspect of fitness through long- term longitudinal studies; however, the difficulties involved in obtaining longitudinal data (expense and subject attrition) make such research scarce. In the absence of such research, consider a cross-sectional study conducted by Saltin and Grimby (1968) that measured the maximal oxygen uptake of three groups of men between ages 50 and 59. The men in the first group were nonathletes in their youth; those in the second group were former athletes but were now sedentary; and the men in the third group had been athletes in their youth and still maintained active lifestyles as older adults. The investigators had to rely on self-reports (rather than laboratory data) to determine the men’s activity levels in youth. Even so, measures of maximal oxygen uptake yielded average values 528 of 30, 38, and 53 ml·kg of body weight−1·min−1 for the nonathletes, sedentary former athletes, and active adults, respectively. More recently, Trudeau, Laurencelle, Tremblay, Rajic, and Shephard (1998) followed up with participants 20 years after their participation in a semi-longitudinal study. The treatment group in the original study took a 1 h specialized physical education class 5 times per week during their 6 years of elementary school. The control group received just 40 min of exercise per week during that time. As adults, women from the treatment group exercised 3 or more times per week more often than women from the control group. The men did not differ. Men and women from the treatment group more often self-reported their health to be very good to excellent. Telama, Yang, Laakso, and Viikari (1997) also followed up on participants 9 and 12 years after they initially answered a questionnaire about their leisure activities (at the ages of 9, 12, 15, and 18 years). The correlations between youth and adult activity were low but significant. A line of longitudinal studies also shows that persistent participation in organized youth sports programs and competitions predicted physical activity levels in young adulthood (Telama, Yang, Hirvensalo, & Raitakari, 2006). Despite the limitations of these studies, the evidence suggests that regular activity in childhood has positive lifelong benefits. Promoting an active lifestyle with children and youths could predispose them to be more active as adults (figure 15.8). Nevertheless, the most important factor for endurance is the individual’s current activity level. At all ages, the capacity for prolonged, vigorous work tends to be transitory. People maintain (or improve) endurance if they are currently training for endurance; conversely, endurance capacity decreases when people discontinue their training programs. 529 Figure 15.8 Endurance capacity is positively related to training, especially after puberty. Moreover, regular activity in childhood might predispose individuals to be more active in adulthood. Perhaps functional constraints such as a positive attitude toward exercise persist over the life span. Think ahead to your own potential lifestyle 20 years from now. What is your plan for staying active? What physical activities do you plan to include in your life? Effects of Disease on Endurance Performance Diseases and disabilities can constrain endurance performance at any point in the life span. A detailed discussion of diseases and working capacity is beyond the scope of this text, but it is important to realize that cardiovascular, pulmonary, infectious, and neuromuscular diseases affect performance. An individual with such a disease possesses a unique set of structural constraints that affect physical performance. Therapists and educators must prescribe initial levels of activity based on these constraints and then constantly adapt activities as a disease progresses or rehabilitation brings about improvements. Short-term infectious diseases, such as influenza, mononucleosis, and chicken pox, generally reduce an individual’s working capacity (Adams, 1973) to varying degrees. It is important for a teacher, coach, or exercise leader to keep this in mind when monitoring performance. Individuals who are trying to maintain peak efficiency want to adhere to training schedules and performance levels even when they are ill, but this is an impractical goal. 530 Imagine you are a physical education teacher with a student who has just returned to school after a 2-week illness. You are scheduled to begin fitness testing. What approach would you take with your student? Why? Teachers, coaches, exercise leaders, doctors, nurses, therapists, and (when children are involved) parents should work as a team; every member should want to do his or her part to help the participant. Clearly, cooperation and communication are imperative. Activity may be beneficial in many cases, but it must never place the participant at increased risk. Those involved in the participant’s exercise program, then, must plan the limits of activity carefully, set expectations accordingly, and monitor the participant closely. 531 Summary and Synthesis Endurance for vigorous activities improves as the body grows. In addition, an individual can increase endurance after puberty by training, although the effects are transitory. A person must maintain training in order to preserve higher levels of endurance and reap the associated benefits. After the adolescent growth spurt, sex differences in working capacity are apparent. The causes of these differences are still open to discussion, but body size, body composition, and hemoglobin levels are at least partially responsible. Recall our discussion of individual functional constraints in part V. Earlier in this chapter we saw how a myth influenced attitudes about children’s training. Functional constraints might also be responsible for some of the sex and age group differences. Societal norms and expectations can constrain activity and training because some individuals are led to believe that such activity is inappropriate for their group. These attitudes are changing, but many people still do not undertake regular exercise. It remains to be seen whether recent exercise and fitness movements will have any effect on life span fitness in the 21st century. Reinforcing What You Have Learned About Constraints Take a Second Look Our discussion of cardiorespiratory endurance helps us appreciate the interaction between this component of physical fitness and the performance of motor skills. Endurance is necessary for the performance of many skills and physical activities of daily living. In addition, appropriate training for endurance supports growth and maintenance of the body’s systems such that participants are in better overall health and enjoy a better quality of life. However, the availability of technology in westernized countries, including computers and gaming systems, tempts people to be more and more sedentary. Sedentary lifestyles at any age lead to a decline in the cardiac, respiratory, and vascular systems. This decline eventually results in a performance decrement and, with advancing age, greater risk of threats to good health. Other body systems, such as the neurologic system, also can suffer from a sedentary lifestyle, which in turn can lead to a reduced quality of life. Other components of physical fitness include muscular strength and flexibility, and performance of many sport and everyday living activities depends on a minimal level of strength and flexibility. Chapter 16 examines these two fitness components, which interact with task and environmental constraints in giving rise to movements. Test Your Knowledge 532 1. How do anaerobic endurance and aerobic endurance change with growth in childhood? How do they change with aging? 2. Can prepubescent children improve anaerobic endurance with training? Aerobic endurance? Explain your answers. 3. What are the sex differences in anaerobic and aerobic endurance over the life span? To what factors might these differences be attributed? 4. At what points in the life span can individuals improve aerobic endurance with training? Do those who build higher endurance in youth realize a lifelong benefit? What types of studies best answer this question? Why? 5. How is anaerobic endurance measured? Aerobic endurance? What is the difference between power and capacity in the measurement of anaerobic and aerobic endurance? 6. How do infectious diseases affect endurance? What strategies are best followed when one begins training after an illness? 7. Do we know whether being active as a child or teen makes a difference in fitness levels in adulthood? On what do you base your answer? 8. How does maturity affect anaerobic and aerobic endurance in youth? 9. If you wanted to predict a youth’s endurance level, would you base your prediction on age, size, maturity, or some combination of these? Why? 10. What factors tend to limit the aerobic endurance of older adults? Anaerobic endurance? Learning Exercise 15.1 Testing Children and Teens for Endurance Locate five research articles on endurance or aerobic performance in children and teens. You can use articles referenced in this text, perform an electronic search, or check a journal such as Pediatric Exercise Science. Once you have located the articles, read about the methods the authors used to conduct their research (typically described in a section titled “Methods”). Note the age and sex of the participants and summarize how endurance performance was tested. What was similar and what was different about how each set of researchers tested endurance? What accommodations, if any, were made for the ages of the participants? 533 Chapter 16 Development of Strength and Flexibility 534 Chapter Objectives This chapter explores the relationship between muscle mass and strength and how these change in relation to each other over the life span, reviews the effects of strength training over the life span, describes changes in flexibility over the life span, and reviews the effects of flexibility training by individuals of any age. 535 Motor Development in the Real World Super Athletes At the turn of the 21st century and shortly thereafter, the world was treated to exceptional performances by athletes in track and field, skiing, skating, baseball, and many other sports. As we moved to the middle of the first decade, however, reports surfaced that some of those athletes had used banned or illegal supplements. Many of these supplements, such as anabolic steroids, help athletes increase muscle mass quickly by allowing them to recover sooner from strenuous training. Because muscle mass is related to strength, these athletes gained a strength advantage over other competitors. The importance of strength, as well as flexibility, to performance of motor skills and tasks is widely recognized today, as we see by the risks some performers are willing to take in order to reach higher levels of strength and flexibility. Strength and flexibility are obvious constraints to skilled performance; sometimes, in fact, skills can be performed only if one has sufficient strength. You might recall that we suspect leg strength is a rate limiter for infant standing. You also might know older adults who have difficulty climbing stairs after they have lost leg strength. Flexibility is also a necessity for many activities. Gymnasts and high jumpers work on their flexibility to perform skills. Senior golfers sometimes exhibit swings that reflect a loss of flexibility. Today’s coaches have come to realize that strength and flexibility are related. Athletes are at their best when they are strong and supple. Training that promotes increased muscle mass at the expense of flexibility puts athletes at risk of injury. Muscle balance is a goal of training today. Muscle strength should be built for all directions of joint movement (e.g., flexion and extension), and flexibility through the appropriate and full range of motion should be fostered. This chapter considers changes over the life span as well as the effects of training throughout the life span, first in strength and then in flexibility. 536 Development of Strength As noted in chapter 4, muscle mass follows a sigmoid growth pattern, and this growth is largely the result of an increase in muscle fiber diameter. Sex differences are minimal until puberty, when boys add significantly more muscle mass than girls do, especially in the upper body. Loss of muscle is small from young adulthood until the age of 50, but thereafter the average loss can be pronounced. The loss is greater for sedentary individuals with poor nutrition. What about muscle strength? Does it simply parallel the changes in muscle mass? Strength- training programs are promoted for individuals of all ages. What effect does resistance training have on strength and muscle mass, especially before the adolescent growth spurt? What about in older adulthood, when muscle mass is typically lost? Strength is the ability to exert force. These are important questions to answer. Many skills require a certain level of strength, such as the gymnastics skills performed on parallel bars. Some skills can be performed better with more strength, such as baseball batting. Even activities of daily living can become difficult without sufficient strength. Older adults who have lost much of their strength have difficulty with everyday tasks, such as getting out of the bathtub or climbing the stairs, and they are often at greater risk of falling. The first step in answering these important questions about strength is to understand the relationship between muscle mass and strength. This section first describes the patterns of change for the average individual and then discusses the effects of training. If you were a therapist, what activities of daily living would you include in the clinic that might be difficult or even risky for an individual who has lost strength due to disease, disability, or aging? Muscle Mass and Strength The amount of force a muscle group exerts depends on the fibers (muscle cells) that are neurologically activated and on leverage (the mechanical advantage the muscle fibers gain based on where force is applied in relation to an axis of rotation). In turn, the fibers activated depend on the cross-sectional area of the muscle and on the degree of coordination in activating the fibers—that is, the nervous system’s pattern and timing in innervating the various motor units to bring about the desired movement. The cross- sectional area of muscle increases with growth, which means that strength increases as 537 muscles grow, but muscle mass is not the only factor in strength. Neurological factors are also involved, and neurological changes over the life span influence muscle strength. Therefore, we cannot assume that strength changes simply as muscle mass changes. Keeping this in mind, let’s see how strength changes over the life span. Key Point Muscle strength is related to muscle size, but changes in strength do not always parallel changes in muscle size. Assessing Strength In strength assessments, individuals typically exert maximum force against resistance. They might move their limbs, as in an isotonic test (constant resistance, as in lifting a barbell) or isokinetic test (constant speed of movement, as with a Cybex machine), or they might exert force against an immovable resistance, as in an isometric test. For us to compare results between individuals, those conducting the assessment must report several kinds of information: The muscle group, such as knee flexors or elbow extensors The movement, such as knee flexion or elbow extension The speed of movement, usually in degrees per second For isometric tests, the angle of the joint (in degrees) as force is exerted must also be recorded because a muscle group can exert different levels of force at different joint angles. A common isotonic strength test is a 1-repetition maximum (1RM) lift of a free weight such as a barbell. As a limb moves through a range of motion, force production is maximal at one point and thus submaximal at other points. The 1RM test therefore indicates forces that can be sustained at the weaker ranges of joint motion. If an isokinetic exercise machine is used, movement speed is kept constant and the device automatically provides an adjusted counter force. A force–velocity curve is generated and the peak on that curve indicates the maximal strength achieved at the strongest joint angle. Because the 1RM test is difficult (and potentially dangerous) for novices, scales have been developed that estimate 1RM weight based on the maximum number of lifts at a given lower weight. Several devices assess isometric strength. The spring-loaded dynamometer requires 538 individuals to compress a handle, and their exertion is registered. Alternatively, the individual can pull on an anchored cable with a handle. A tensiometer is placed on the cable and registers the force of exertion. Dynamometers and tensiometers usually measure in Newtons, a measurement unit for force. In school settings, functional tests of strength are often used with children. Among these are chin-ups, the flexed-arm hang, and rope climbing. Note that body weight is used as the resistance in these tasks, so body weight is a factor in performance levels. Some of these tasks also require skills, such as rope climbing, and the skill factor must be considered in interpreting test results. Isotonic strength is the exertion of force against constant resistance through the range of motion at a joint. Isokinetic strength is the exertion of force at a constant limb velocity through the range of motion at a joint. Isometric strength is the exertion of force without a change in muscle length (i.e., without movement of a limb). Developmental Changes in Strength Strength is certainly one of the individual structural constraints that change with growth and aging. Changes in strength may be brought about by multiple influences on strength and resistance training, both in the long term and in the short term. Because an individual’s strength level is a constraint that interacts with task and environmental constraints to permit or limit movements, strength levels change movements over the life span. Preadolescence Strength increases steadily as children grow older (figure 16.1) (Blimkie, 1989; Pate & Shephard, 1989). Boys and girls have similar strength levels until they are about 13 years old, although boys are very slightly stronger than girls of the same height during childhood (Asmussen, 1973; Blimkie, 1989; Davies, 1990; Parker, Round, Sacco, & Jones, 1990). 539 Figure 16.1 Development of isometric strength. Boys (open symbols) continue to steadily improve in isometric strength throughout adolescence whereas girls (filled symbols) tend to plateau. Graph is based on data accumulated by Shephard (1978b) for handgrip and on unpublished results of Howell, Loiselle, and Lucas (1966) for other measures. Based on Shepard 1982. We know that muscle mass also increases steadily as children grow older, so how is strength related to muscle mass in childhood? Wood, Dixon, Grant, and Armstrong (2006) measured the elbow flexor strength, muscle size, and moment arm length (the perpendicular distance between the joint center or axis of rotation and the muscle tendon’s point of attachment to the bone) of 38 boys and girls close to 9.6 years of age. The latter two values were assessed using magnetic resonance imaging. The largest contribution to strength was the cross-sectional area of the elbow flexor muscles. In children, then, strength is greatly related to muscle mass. In fact, Barrett and Harrison (2002) found that children matched adults in the functional ability of muscle per unit of muscle volume, implying that muscle size plays a large factor in child–adult strength differences. Other factors might be involved in strength levels. Consider the age at which individuals reach peak gains in muscle mass and strength. As noted in chapter 4, peak gain (the peak in the velocity curve) indicates the point of the fastest increase. If strength development directly follows muscle mass development, the peak gain in strength would coincide with the peak gain in muscle mass. In turn, teachers and coaches could predict children’s strength levels by simply measuring their muscle mass, which can be estimated from weight measurements or by subtracting a child’s estimated fat weight from body weight. However, several studies indicate that these peak gains do not coincide with each other in most adolescents (Carron & Bailey, 1974; Jones, 1947; Stolz & Stolz, 1951). For example, Rasmussen, Faulkner, Mirwald, and Bailey (1990) conducted a longitudinal study with boys and found that peak muscle mass velocity occurred at an average age of 14.3 years but 540 peak strength velocity occurred at 14.7 years. Tanner (1962) suggests that the typical sequence of peak muscle mass velocity followed by peak strength velocity probably reflects increasing hormone levels and their effect on the protein structure and enzyme systems of the muscle fibers. Thus, the endocrine system plays a role in strength increase with growth. Another way to examine muscle growth and strength development is to relate measures of muscle strength to various body sizes in children and determine whether strength increases at the same rate as body size. Asmussen and Heeboll-Nielsen (1955, 1956) took this approach in studying Danish children between ages 7 and 16 years. They assumed that body height could represent changes in body size, including body weight; in this age group, body weight is proportional to body height raised to the third power. Asmussen and Heeboll-Nielsen showed this was approximately true for their sample of Danish boys and girls. Because height measures could represent body size, they grouped the children into height categories by 10 cm intervals and measured them for isometric strength. Successive height groups demonstrated increasing muscle strength but at a rate greater than that of their increase in height. Asmussen and Heeboll-Nielsen also divided boys of the same height into two age groups, one younger and one older by approximately 1.5 years. The older group showed greater arm and leg strength by about 5% to 10% per year of age. This experiment also demonstrates that strength is not related to muscle size alone; rather, neural influences are likely. These influences might include myelination of nerve fibers, improved muscle coordination (movement requires a contraction of some muscles and a coordinated relaxation of the muscle on the opposite aspect of the body), and improved extent of motor unit activation (Blimkie, 1989; Kraemer, Fry, Frykman, Conroy, & Hoffman, 1989; Sale, 1989). Only one of these neural influences—improved motor unit activation—has been examined experimentally. Blimkie (1989) found some support for the suggestion that older children can activate a greater proportion of motor units to exert force. The studies mentioned thus far typically measured isometric strength directly with a cable tensiometer or a dynamometer. The benefit of measuring strength with this equipment is that the effects of skill, practice, and experience are minimized. However, these factors do influence the performance of sports skills, making studies of the development of functional muscle strength very useful. Key Point Functional strength tasks have a strength component and a skill component. Two skills that involve functional muscle strength are vertical jumping and sprinting. Practice and experience, as well as leg strength, influence children’s performance on both 541 tasks. Asmussen and Heeboll-Nielsen (1955, 1956) measured performance on these two skills in successive height groups of Danish children. They found that functional muscle strength, like isometric strength, increased at a faster rate than one would anticipate from muscle growth alone. Further, the rate of gain in functional muscle strength was even greater than the rate of gain in isometric strength, emphasizing again the role of neurological factors in improved muscle strength as children mature. Web Study Guide Plot data and analyze changes in functional strength in preadolescence in Lab Activity 16.1, Examining Trends in Strength Development, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Adolescence and Young Adulthood As noted in chapter 4, boys gain more muscle mass in adolescence than girls do, largely as a result of higher levels of androgen secretion. It is no surprise, then, that boys undergo a spurt of increased strength at about age 13. Girls continue a steady increase in strength during adolescence before reaching a plateau. As a result of differential growth of muscle mass during adolescence, then, the average adult man is stronger than the average adult woman. Women can produce only 60% to 80% of the force that men can exert, although most of these differences can be attributed to differences in arm and shoulder strength rather than in trunk or leg strength (Asmussen, 1973). As noted in chapter 4, sex differences in muscle mass are more pronounced in the arms and shoulders than in the trunk and legs. However, the average difference in body or muscle size accounts for only half of the difference in strength between men and women. Cultural norms probably play a role in the sex differences in strength. These norms, of course, begin to exert their influence very early in life. For example, Shephard (1982) noted the effect of repeating strength measures on naive boys and girls (i.e., boys and girls who have never been tested for strength). Whereas the boys showed no tendency to improve over three visits, the girls improved on each subsequent visit in almost every case and improved significantly on two of the eight strength measures (figure 16.2). It is possible that the task gained acceptability to the girls as they became more familiar with it. The boys may have been more used to all-out demonstrations of strength. Girls at that time were probably not encouraged to go all out and might even have been discouraged from doing so, which would have limited their experience in exerting strength. Neither should motivation be discounted as a major factor in strength measurement. Certainly, if Shephard had recorded only the first set of scores, he would have concluded that the sex differences in strength were much greater than what he 542 found after comparing the third set of scores. If you were a coach or personal trainer of young girls, how might you work to overcome any stigmas associated with weight training in order to help them improve their strength? 543 Figure 16.2 The effect of test repetition on muscle force measurement. When repeated visits were measured, girls improved on these strength measures whereas boys showed little or no change. Based on Shepard 1982. Cultural norms can also influence strength differences between the sexes through habitual physical activity. That is, the traditional physical activities promoted to growing boys tend to provide long-term resistance exercise. Those promoted to girls do not. Daily physical activities that promote strength have a cumulative effect over the growing years, resulting in significant sex differences that cannot be attributed to muscle size alone. Some research has hinted that sex differences exist in muscle fiber composition—that is, that men and women do not have the same proportions of type I (slow-twitch) and type II (fast-twitch) muscle fibers. If so, part of the sex differences in strength might be attributed to muscle fiber composition. Animal studies indicate that muscle composition is related to isometric strength (see Komi, 1984, for a review). On the other hand, Davies, White, and Young (1983) could find no relationship between strength and muscle fiber composition in boys and girls 11 to 14 years of age. Much more research is needed on this topic. After the growth period, increases in muscle mass are associated with resistance training. Some drugs used in conjunction with training can increase muscle mass at a rate greater than training alone, but most have unhealthy side effects. Electromyographic measurements of muscle activation in strength tasks show that improved strength in adults who are engaged in resistance training is related to improved neurological activation and increased muscle size. In fact, in the early weeks of training most strength improvements are related to neurological factors because muscle has not yet increased in size (Moritani & DeVries, 1980). 544 Key Point Strength increases gradually throughout childhood; boys experience a spurt of increased strength in adolescence, whereas strength increases steadily in girls. Middle and Older Adulthood Strength levels generally are maintained throughout the 20s and 30s. For the average adult, strength declines thereafter. The decline is somewhat gradual at first. Shephard (1978b) placed the loss in the 50s at 18% to 20%. Shock and Norris (1970) measured a significant loss in arm and shoulder strength after age 65. Murray, Gardner, Mollinger, and Sepic (1980) reported a 45% loss of strength after age 65. Isometric strength (the ability to exert force against immovable resistance) and isotonic strength (the ability to exert force against movable resistance) both decline. The loss is particularly noticeable in the muscles of the upper leg. Imagine you are a physical education teacher. What activities not typically labeled “resistance training” might still improve leg strength? Upper body strength? Are any of these typically gender stereotyped? We see several trends in the overall decline of strength with aging; table 16.1, from Spirduso (1995), summarizes these trends. On the left are the better-maintained aspects of strength and on the right are the aspects that decline more in the general population. 545 These losses are what we would expect from the loss of muscle mass in older adulthood, yet the loss of strength might be larger than the loss of muscle mass. Young, Stokes, and Crowe (1985) found a 39% loss of strength but only a 25% loss in cross-sectional area in the quadriceps muscles of older men compared with younger men. Aniansson, Hedberg, Henning, and Grimby (1986) documented a 10% to 22% loss of strength (figure 16.3) but a 6% loss of muscle mass in the same muscle group over a 7-year span. 546 Figure 16.3 Changes in strength with aging. The average force (torque measured in Newton meters, Nm) exerted in a stationary knee position and at several speeds of knee extension decreased for 23 men over a 7-year interval. Changes are significant at the confidence levels of p < .01 (**) or p < .001 (***). Reprinted by permission from Ariansson et al. 1986. Thus, loss of muscle mass does not parallel loss of strength in older adulthood. Spirduso, Francis, and MacRae (2005) identified a number of factors, in addition to muscle atrophy, that can contribute to loss of strength with aging (figure 16.4). As illustrated, a decrease in activity, poor nutrition, and an increase in the likelihood of disease contribute to loss of strength either directly or through changes in the body systems. We have noted a change in the muscle system—muscle atrophy—but changes also occur in muscle fibers, possibly such that fibers are not as distinctly type I or type II with aging (Andersen, Terzis, & Kryger, 1999) and that type II fibers shrink in size. 547 Figure 16.4 Factors contributing to the loss of muscle strength with aging. Reprinted by permission from Spirduso, Francis, and MacRae 2005. The nervous system might be involved because of a loss of motor neurons in the spinal cord with aging, resulting in a loss of motor units (Green, 1986; Grimby, 1988). Other units reinnervate some of the fibers of the lost motor neurons, such that the number of fibers per motor neuron increases (Campbell, McComas, & Petito, 1973; Fitts, 1981). The result would be a loss of muscular coordination, especially fine motor coordination. The vascular system also might be involved in the loss of strength. The number of capillaries per muscle fiber seems to decline with aging, but this is almost assuredly related to a trend toward inactivity (Cartee, 1994). In older adults who undertake aerobic exercise, the number of capillaries actually increases and muscle blood flow improves. As with so many other aspects of aging, it is difficult to distinguish whether loss of muscle mass and strength in older adults is related to aging of tissues or disuse. We know that strength is better maintained in frequently used muscles than in infrequently used muscles (Kauffman, 1985; Wilmore, 1991). Kallman, Plato, and Tobin (1990) demonstrated how variable the loss of strength is among older adults. They observed young, middle-aged, and older adults over a 10-year period. Many of the older adults lost less strength than middle- aged and young adults lost during the 10 years, and some lost no strength at all. This variability most likely reflects extrinsic factors among these adults, especially exercise and activity levels, reminding us that significant loss of strength with aging is not a foregone conclusion. Strength and muscle mass share the same five general phases of change over the life span (early increase, steady advancement, adolescent spurt, maintenance in adulthood, possible decline in older adulthood), yet the timing of those changes can be distinct, as can the degree of change. 548 Key Point Strength is maintained in adulthood. Gradual declines occur after the 30s and more notable declines begin in the 50s; losses are extremely variable among older adults. Strength Training An adult can increase muscle strength with strength training, which also results in a noticeable increase in muscle size. The effect is most noticeable in postpubescent men; hence, circulating testosterone was initially considered the stimulus for such increases in muscle size. In the past, this view probably led many to think that resistance, or weight, training was of limited use to other groups. The thinking has changed dramatically. Numerous newspaper articles and television segments feature older adults who are taking up weight training. Resistance exercise has even become a part of curricula in elementary school physical education (figure 16.5). Rehabilitation programs focus on regaining strength after injury, even when the patient is not a professional athlete. Key Point In the absence of training, adults lose strength at a greater rate than would be expected based strictly on loss of muscle mass. 549 Figure 16.5 Increasing muscle mass with growth—a structural constraint—leads to increased strength, but resistance training also increases strength over the life span. Strength is often an individual constraint in the performance of motor tasks. The strength level of an individual interacts with the task and environment either to permit a task or not or to influence how a movement is performed. If strength training can change an individual’s level of strength in a relatively short time, then clearly it becomes a means to help individuals perform tasks. It can change the point at which strength becomes a rate limiter for a given task or skill. Educators and therapists can intervene to change motor performance over a matter of weeks. Hence, we should take great interest in how strength training can change strength at any point in the life span. This section considers how strength training affects various age groups. If you were a therapist, why would restoring the strength of someone not participating in sport be as important as restoring the strength of an athlete after disease or injury? Prepubescence Research has documented that boys and girls as young as 6 or 7 years can increase their strength with a variety of resistance-training methods, including weights, pneumatic machines, hydraulic machines, and isometrics (figure 16.6) (de Oliveira & Gallagher, 1994; 550 Duda, 1986; Falk & Tenenbaum, 1996; Sadres, Eliakim, Constantini, Lidor, & Falk, 2001; Sale, 1989; Weltman, 1989). 551 Figure 16.6 Muscle strength increases with training. Compared with their nontraining counterparts, prepubescent boys achieved larger relative increases in strength of four muscle groups at two speeds of movement. Reprinted by permission from Malina, Bouchard, and Bar-Or 2004. For example, Pfeiffer and Francis (1986) compared the strength of 14 prepubescent boys with that of a control group before and after a 9-week, 3-day-a-week training program. The boys trained on a Universal machine and with free weights, completing 3 sets of 10 repetitions in each session. Strength improved significantly in the young boys. In fact, the percentage increase was greater in the young boys than in the pubescent and postpubescent boys Pfeiffer and Francis also tested (figure 16.7). Others have confirmed that although postpubertal individuals gain more absolute strength with training, prepubertal individuals gain more strength expressed as a percentage change from their starting strength (Sale, 1989). Faigenbaum, Milliken, Moulton, and Westcott (2005) considered whether low- or high-repetition-maximum resistance training is better. Although boys and girls increased their strength with either approach far more than a control group did, the high-repetition group demonstrated gains in muscle endurance and flexibility as well. 552 Figure 16.7 Percentage increases in strength with training. Prepubescent boys generally achieved larger relative increases in the strength of four muscle groups at two speeds of movement with 9 weeks of training than did pubescent or postpubescent boys. Reprinted by permission from Malina, Bouchard, and Bar-Or 2004. Several investigators found that increased muscle size did not accompany increased strength in prepubescents (Ramsay et al., 1990; Sale, 1989; Weltman et al., 1986). What, then, accounts for the strength increase? As noted previously, strength is related to muscle size and to the central nervous system’s ability to fully activate muscles. Improvement in prepubescents likely results from their improved ability to exert force in the intended direction as they are better able to activate the agonist (contracting) muscles and coordinate the antagonist (lengthening) muscles (Sale, 1989). These neural factors probably account for much of the initial strength gain when males or females of any age group begin training. Key Point Prepubescent children can increase strength with training, even without an accompanying increase in muscle size. Even if prepubescent children can improve their strength with training, does such training involve negative effects? Children’s bones are still growing and could be susceptible to injury at both traction and pressure epiphyses. Weight training could potentially cause a single traumatic injury or chronic injury from repeated lifts. Also, some professionals who work with children are concerned that a loss of flexibility or even stature may accompany 553 strength training. Several studies found no damage to bones or muscles in training prepubescents and recorded no injuries (Rians et al., 1987; Servedio et al., 1985; Sewall & Micheli, 1986). In one study in which 27 prepubescent boys were observed over 2 years of a twice-weekly resistance-training program, only one minor injury occurred and there were no differences in height compared with a nontraining group (Sadres et al., 2001). Neither did researchers find loss of flexibility (Rians et al., 1987; Servedio et al., 1985; Sewall & Micheli, 1986; Siegel, Camaione, & Manfredi, 1989). However, all of the prepubescents in these studies were closely monitored. Educators should closely supervise weight-training programs for young children and insist that participants adhere strictly to guidelines (Sale, 1989). Imagine you are a middle school physical education teacher who is planning resistance-training activities. What are some important constraints to consider when implementing a training program for children? Think of individual structural and functional constraints as well as task and environmental constraints. Adolescence Developmentalists generally accept that strength training has beneficial effects for adolescents. Pfeiffer and Francis (1986) demonstrated that pubescent and postpubescent boys improved their strength with training. Other training methods yield the same result, including isometric training (Nielsen, Nielsen, Hansen, & Asmussen, 1980) and plyometric training (Steben & Steben, 1981). Tibana et al. (2012) noted that adolescent boys showed a higher recovery capacity between sets in a resistance-training session than did adult men. After puberty, muscle hypertrophy can accompany regular strength training. As noted previously, adolescent boys add far more muscle mass than do adolescent girls during the growth spurt. Do the sexes also differ in their response to training? Cureton, Collins, Hill, and McElhannon (1988) placed young adult men and women on a weight-training program in which the resistance level was 70% to 90% of the individual’s maximum. Men and women gained strength at identical levels in terms of percentage increase, but men gained more strength in terms of absolute increase for two of four tests. For example, men and women might increase 5%, but if the men were stronger at the start, their increase was greater in absolute terms. Both men and women experienced muscle hypertrophy in their upper arms, again by an identical percentage increase, although one measure yielded a greater absolute increase in men. After puberty, then, both improved coordination in recruiting the muscle units needed to exert force and muscle hypertrophy response to strength training appear to be similar in men and women in relative terms. Muscle 554 hypertrophy is more noticeable in men in that a percentage increase of a larger muscle mass yields greater absolute dimensions. Adolescents, like children, should be closely supervised when using weight training to improve strength. Their bones are still growing, and they are susceptible to a variety of musculoskeletal injuries (Risser & Preston, 1989). Performing Olympic-style lifts, in particular, can bring about back injuries (Jesse, 1977). Any activity that could possibly limit the ability to be active throughout life is of doubtful benefit to youths. Educators may want to take a cautious approach by starting adolescents with light resistance and scheduling progression in small increments. Close supervision is warranted because adolescents are susceptible to peer pressure and can easily be drawn into games of trying to outperform each other. Key Point Adolescents can increase strength and muscle mass with training. Year-to-year comparisons of strength measurements taken between ages 7 and 17 years show that the strongest children are not necessarily the strongest adolescents (Rarick & Smoll, 1967). The weakest 7-year-olds might be late maturers who eventually catch up to or pass their peers. Researchers have also found that children and adolescents who participate regularly in sport are stronger than those who do not (Bailey, Malina, & Rasmussen, 1978). This could be seen as proof that the training provided by sport participation develops strength. It should be noted, though, that young athletes are often more physiologically mature than nonathletes. Hansen, Klausen, Bangsbo, and Muller (1999) noted that the elite 10- to 12-year-old soccer players selected for the best teams were taller, leaner, and more mature than those not selected. Middle and Older Adulthood Young and middle-aged adults can maintain or even increase their strength through resistance training. Male soldiers improved push-up performance and leg power with just a 12-week resistance-training program (Kraemer et al., 2004). Even obese women not dieting increased their muscle strength with a 12-week training program (Sarsan, Ardic, Ozgen, Topuz, & Sermez, 2006). Women who had just gone through menopause improved muscle strength with a resistance-training program (Asikainen et al., 2006). Key Point Middle-aged and older adults can increase strength and muscle mass with 555 training. But what about older adulthood, when strength levels decline? Can older adults prevent or reverse losses in muscle mass and strength through training? Mayer et al. (2011) examined more than 1,500 articles published between 2005 and 2010 that addressed the effectiveness of resistance training in individuals over age 60 and concluded that older adults could increase their strength with training. This increased strength reflected an increase in muscle mass and an improved recruitment of motor units along with an increase in motor unit firing rate. Overall, these articles demonstrated that older adults could increase muscle mass by training at 60% to 85% of their maximum voluntary strength. Older adults could improve the rate of force development, but, just as with the young adults, this required a training intensity greater than 85%. Three to four training sessions per week yielded the best results. Imagine you are a recreation leader who would like to increase the participation of older adults in fitness classes—especially classes that would increase strength—at a recreation center. How would you promote participation to the area’s citizens? What activities would you schedule? How would you minimize the risk of injury for participants? Some professionals are reluctant to recommend resistance training to older adults, especially weight training with high-intensity resistance or isometric exercises. Their fear is that high pressures in the chest during contractions could resist blood flow and trigger cardiovascular or cerebrovascular catastrophes. Lewis et al. (1983) found little pressure difference between isometric and dynamic exercises; however, older adults at high risk for cardiac catastrophe or with osteoporosis (skeletal atrophy) or arthritis should train with light resistance and under the supervision of a knowledgeable professional. Mayer et al. (2011) found no evidence that older adults need to train at relatively lighter loads than young adults in order to avoid injury. Increases in strength were dependent on training at a fairly high intensity. Overall, older adults benefitted from and tolerated classic training regimens of 3 to 4 sets of about 10 repetitions at 80% of 1RM, done 3 times per week for 8 to 12 weeks. Key Point Resistance training is beneficial for increasing strength in preadolescence, adolescence, and young, middle, and older adulthood. Summary of the Development of Strength 556 The typical pattern of strength change over the life span has five phases. In childhood, strength steadily increases. In adolescence, girls continue this steady increase but boys have a spurt of growth in strength. In the 20s and 30s, strength levels are relatively stable. After this, strength gradually declines until sometime in the 50s, when the loss becomes more dramatic. This typical pattern can be changed by resistance training at any point in the life span. Parents, teachers, and therapists can change the movement that arises from the interaction of person, task, and environment by introducing resistance training to those in their care, thus changing an individual structural constraint. Changes in strength tend to parallel changes in muscle mass. However, muscle mass is not the only factor involved in increased strength. Neurological factors play a large role; in fact, strength improvements in childhood with resistance training are largely related to neurological factors. Cultural norms probably also play a role in strength levels by influencing the habitual physical activities undertaken by individuals. Although muscle strength is important for the performance of skills, so is suppleness, or flexibility. Individuals must be able to move through full ranges of motion and position their limbs to undertake movements in sport and dance as well as in daily living. 557 Development of Flexibility Flexibility often benefits maximal performance. Limited flexibility is a factor in sport injuries and in restricted mobility; that is, flexibility can be a rate limiter. Limited flexibility also can influence the type and kind of activities that older adults can enjoy. Older adults whose strength and flexibility are severely limited must have help even to perform activities of daily living. Young athletes sometimes overlook this important aspect of physical fitness, emphasizing endurance and strength at the expense of flexibility. Exceptions to this generalization are dancers and gymnasts, who have long realized the importance of flexibility in their activities. Many young athletes are indifferent toward flexibility because they assume that young people are naturally supple and need no further flexibility training. People typically view lack of flexibility as a problem only for older adults, whose movement limitations are more readily apparent. Many of these generalizations are based on misconceptions about flexibility. This section describes first the pattern of change in flexibility for the average individual and then the effects of training and how it alters the typical pattern. Flexibility is the ability to move joints through a full range of motion. Developmental Changes in Flexibility The range of motion possible at any joint depends on that joint’s bone structure and on the soft tissues’ resistance to movement. The soft tissues include muscles, tendons, joint capsules, ligaments, and skin. The belief that flexibility is related to limb length is incorrect. Habitual use and exercise preserve the elastic nature of the soft tissues, whereas disuse is associated with a loss of elasticity. To improve poor flexibility, a person must move the joint regularly and systematically through an increasingly larger range of motion to modify the soft tissues. Athletes, then, tend to increase the flexibility of joints they use in their sports, whereas laborers who spend much of their time in one posture may lose flexibility in some joints. It is likely that people who do not exercise fully lose flexibility because everyday activities rarely require movement through a full range of motion. Thus, at any age, flexibility reflects the normal range of movement to which an individual subjects specific joints. An important characteristic of flexibility is its specificity; that is, a certain degree of flexibility is specific to each particular joint. An individual can be relatively flexible at one joint and inflexible at another. Childhood Most of us can recall seeing an infant lie on his back and bring his feet nearly to his head. 558 Or, we remember a toddler who can sit on the floor with her bent legs out to the side. We know from experience that infants and toddlers are very flexible. Most observations of children identify a decline in flexibility with advancing age. After reviewing the information available in 1975, Clarke (1975) concluded that boys tend to lose flexibility after age 10 years and girls after age 12 years. For example, Hupprich and Sigerseth (1950) administered 12 flexibility measures to 300 girls aged 6, 9, 12, 15, and 18 years. Most of the flexibility measurements improved across the 6-, 9-, and 12-year-old groups but declined in the older groups (figure 16.8). Krahenbuhl and Martin (1977) found that flexibility in both boys and girls declined between ages 10 and 14, but Milne, Seefeldt, and Reuschlein (1976) reported that second graders in their study already had poorer flexibility than kindergartners. Factors that might be involved in this trend are the addition of muscle mass and the maturation of joint structures with growth, but more research on these topics is needed (Parker & James, 1985). 559 Figure 16.8 These three flexibility measures show that flexibility generally declines with advancing age, although range of motion in some joints might increase until approximately 12 years of age. Adapted by permission from Hupprich and Sigerseth 1950. Assessing Flexibility Because flexibility is specific to a joint, one or two flexibility measures cannot accurately represent one’s overall flexibility. To know the flexibility in a specific joint in a particular individual, it must be measured. Most flexibility measures are made with a goniometer, which is a protractor with two long arms. The axis of the goniometer is centered over the joint to be measured. The limb is positioned at one end of the range of motion, and one arm of the goniometer is aligned with it. The limb is moved to the other end of the range, and the second arm is aligned with it. The degrees between the two arms on the goniometer represent the range of motion at the joint. Taking accurate flexibility measurements is not quite as easy as it sounds (Michlovitz, Harris, & Watkins, 2004). Starting and ending points are sometimes difficult to locate, and measurements often reflect the discomfort individuals are willing to endure to push themselves farther. It is often impractical to give a battery of flexibility measures at various joints, especially if strength, endurance, and body composition are all being assessed at the same time. Fitness test batteries such as Physical Best (American Alliance for Health, Physical Education, Recreation and Dance, 1988), Fitnessgram (Meredith & Welk, 1999), and that used in the National Children and Youth Fitness Study II (Ross, 560 Pate, Delpy, Gold, & Svilar, 1987) employ a single representative measure of flexibility. The sit-and-reach test (figure 16.9) was chosen because trunk and hip flexibility are thought to be important in the prevention and care of low back pain in adults (Hoeger, Hopkins, Button, & Palmer, 1990). 561 Figure 16.9 The sit-and-reach test. The individual sits with the feet against a box corresponding to the 23 cm point along the ruler. Upon reaching forward as far as possible, the individual receives a score of 23 cm plus or minus the distance reached measured at the fingertips (distance A). Reprinted by permission from Hoeger et al. 1990. Some researchers are concerned that the sit-and-reach test reflects body proportions as well as flexibility because it measures flexibility relative to a point even with the feet. A small number of individuals with unusually long legs, short arms, or both are at a disadvantage. A modified sit-and-reach test corrects for limb length bias by measuring flexibility relative to an individual’s fingertips when sitting straight up (Hoeger et al., 1990). The sit-and-reach test has been used as the representative measure of flexibility in fitness test batteries. Norms developed in the National Children and Youth Fitness Study II project (Ross et al., 1987) for children aged 6 to 9 reflect generally stable sit-and-reach performance during childhood. In an extensive cross-sectional study of Flemish girls aged 6 to 18 years, the sit-and-reach scores of girls in the upper percentiles were stable until age 12 and then improved. The scores of girls in the lower percentiles declined from 6 to 12 years, improved somewhat in midadolescence, and then declined again at 17 and 18 (figure 16.10). Thus, the range of scores was wider in successively older age groups (Simons et al., 1990). Belgian boys measured longitudinally improved their sit-and-reach performance from 12 to 18 years of age at a rate of about 1 cm per year (Beunen, Malina, Renson, & Van Gerven, 1988). 562 Figure 16.10 Changes in sit-and-reach test performance over age. Flemish girls at the upper percentiles maintained their flexibility during childhood and then improved in adolescence. Girls at the lower percentiles declined and then only slightly improved in midadolescence. The median scores of girls from the United States (U.S.) and the Netherlands (NDL) are superimposed on the graph. Reprinted by permission from Simons et al. 1990. Generally, then, children maintain their sit-and-reach flexibility, whereas adolescents are able to improve their scores as they grow older. Some children and adolescents lose flexibility or improve very little. Abdominal strength might be a factor in sit-and-reach performance (Beunen et al., 1988) because individuals with strong abdominal muscles can pull the trunk forward to a greater degree of flexion. Performance might therefore be related to exercise and training, both for strength and for range of motion. Girls as a group are usually more flexible than boys (Beunen et al., 1988; DiNucci, 1976; Phillips et al., 1955; Simons et al., 1990). This difference probably reflects the facts that stretching exercises are more socially acceptable for girls than are vigorous exercises and that higher proportions of girls than boys participate in gymnastics and dance, both of which emphasize flexibility. Participation in exercise programs emphasizing flexibility is a far better predictor of flexibility than is sex (figure 16.11). 563 Figure 16.11 Flexibility becomes more variable in adolescence as some individuals exercise and others become sedentary. It is unclear how the changing structural constraints of skeletal system growth and muscle system growth influence flexibility, Researchers, then, document both declines and improvements in flexibility during the growing years. It is possible that because the bones grow in length and then stimulate muscles to grow in length, a temporary loss of flexibility occurs during growth, especially in early adolescence (Micheli, 1984), as muscle growth lags behind bone growth. However, it is not clear that this would result in a measurable decline in flexibility, even over a short period of time. Although some changes might be particular to the joint or joints measured, overall it is apparent that children and adolescents can lose their flexibility if they do not train to maintain or improve it. Flexibility becomes more variable in groups of adolescents because some adolescents train whereas others abandon exercise programs and physical activities. Most of us think of arthritis as a disease of older adulthood, but approximately 1 out of every 250 children in the United States has a form of arthritis and associated joint pain. Expertise in treatment is required because the traditional adult therapy of steroids can stunt children’s growth. Key Point In adolescence, flexibility becomes more variable with advancing age, and some individuals lose a significant degree of flexibility. Think about your own fitness regimen. Does it include activities that can maintain your flexibility over the next 30 or 40 years? Do you need to add to your personal workout plan? 564 Web Study Guide Plot data and examine trends in flexibility over time in Lab Activity 16.2, Examining Trends in Sit-and-Reach Performance, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Adulthood Unfortunately, adolescence does not mark the end of a person’s trend toward reduced flexibility. Holland, Tanaka, Shigematsu, and Nakagaichi (2002) reviewed the research literature on flexibility and older adults. From the mid- to late 20s, maximum range of motion declines; it does so faster in some joints than in others (Bell & Hoshizaki, 1981). For example, the decreases in spinal extension and shoulder flexion are relatively large compared with smaller decreases in hip extension and knee flexion (Einkauf, Gohdes, Jensen, & Jewell, 1987; Germain & Blair, 1983; Roach & Miles, 1991). Both upper and lower extremities showed declines in flexibility (Rikli & Jones, 1999). Various factors contribute to decreases in flexibility, including degeneration of musculoskeletal and soft tissues as well as diseases, especially osteoarthritis and osteoporosis. Collagen increases and elastin degenerates with advancing age, and both of these changes lead to increased joint stiffness (Alnaqeeb, Al-Zaid, & Goldspink, 1984; Gosline, 1976). Disuse magnifies all of these changes, though we do not know how disuse influences the rate of decline. The research studies of range of motion in adulthood have used the cross-sectional design. Longitudinal studies would be needed to determine how much of the decline is due to age-related tissue change and how much is due to disuse. The learning exercise at the end of this chapter gives you an opportunity to observe flexibility in some older adults. Osteoarthritis is a degenerative, chronic disease of the joints. Key Point Flexibility declines in adulthood, especially in little-used joints. Flexibility Training Researchers generally agree that both specialized stretch training and general exercise interventions moderately improve range of motion in older adults, including frail elderly persons. Munns (1981) formed two groups of 65- to 88-year-olds. One group served as a 565 control and the other participated in a 1 h program of exercise and dance 3 times per week for 12 weeks. The exercising group improved significantly over the control group in all six of the flexibility measures taken. Germain and Blair (1983) documented improved shoulder flexibility in adults aged 20 to 60 who participated in a stretching program for shoulder flexion, and Brown and Holloszy (1991) even found a 35% improvement in hip flexion with 5 days of slow stretch and callisthenic training per week over 3 months. Low-impact aerobics, Tai Chi, rhythmic stretching, and general fitness interventions have all yielded improvements (Hubley-Kozey, Wall, & Hogan, 1995; Lan, Lai, Chen, & Wong, 1998; McMurdo & Rennie, 1993; Rikli & Edwards, 1991). Key Point An individual’s flexibility decreases without training at any point in the life span, but specific training can reverse a loss of flexibility at any age. Summary of the Development of Flexibility The range of motion possible in a joint reflects a person’s activity and training more than it reflects his or her age per se. Flexibility declines in the average adolescent and adult as a result of limited daily activity and lack of exercise. Flexibility training can bring about an improvement in the range of motion at any age. For many individuals, then, if flexibility limits a desired movement, appropriate training can change this structural constraint. 566 Summary and Synthesis Muscle strength and flexibility are discussed separately in this chapter, but their interrelationship as individual structural constraints should be noted. Individuals can improve strength or flexibility at any time in the life span with an appropriate training program. Ideally, though, individuals train for both. Having strong muscles to move a joint in one direction such that the joint cannot move through its appropriate range in the opposite direction can limit movement just as much as a lack of strength can. Training for strength and flexibility is not just for athletes; to the contrary, if reasonable levels of strength and flexibility are not maintained, many movements needed for daily living can be difficult if not impossible. Therapists routinely help individuals regain lost strength and flexibility after accidents, injuries, and surgery. Body composition is another component of fitness. A body composition that is high in lean muscle mass and low in fat tissue enhances cardiorespiratory endurance, is associated with increased strength, and permits flexibility as long as the muscles are balanced. Individuals can better maintain that body composition profile if they participate in endurance activities and resistance-training programs. Chapter 17 examines body composition more closely. Reinforcing What You Have Learned About Constraints Take a Second Look Being strong and flexible is an advantage to performance, whereas being weak and inflexible can limit performance. In other words, the musculoskeletal systems can be rate-limiting systems for movement. Training for strength and flexibility can be a healthy activity, and greater muscle mass can make a positive contribution to health. For example, greater muscle mass is associated with expending more calories and maintaining healthy body composition. Greater muscle mass is also associated with greater cardiovascular endurance. Yet, as we have seen so often, the musculoskeletal system does not function in isolation. Individuals who try to enhance their strength and flexibility training with drugs often risk damage to other systems, especially the endocrine system with the use of anabolic steroids. This fact reminds us that the interaction of all systems must be considered when examining the effect of changing a system to give rise to certain movements. Test Your Knowledge 1. How does the rate of increase in strength with growth compare with the rate of increase in muscle mass? How do the rates of decrease in strength and muscle mass compare in aging? 567 2. Can strength and flexibility improve with training? How? 3. How does flexibility change with growth and with aging? 4. What are the sex differences in the development of strength and flexibility in children and adolescents? 5. Consider the older adults in your society. Can you think of constraints (individual, environmental, and task) that might lead to a loss of strength in the older years? 6. How is maturation related to strength in children and teens? 7. What factors are involved in the loss of strength in older adulthood? What effect does resistance training have on these factors? 8. What factors are important to consider in assessing strength? In assessing flexibility? 9. How does the change in functional muscle strength compare with the change in isometric muscle strength during the growing years? 10. How can cultural norms differentially affect assessment of strength and flexibility of the sexes? Learning Exercise 16.1 Older Adult Flexibility Observe the following in two or three older adults and report your findings. 1. When seated on the floor with his back against a wall, can the individual keep the knee of the extended leg flat on the floor as he draws the lower portion of his other leg up against the thigh? 2. Can she raise her arms overhead, fingers pointing to the ceiling, to be even with or behind the ears? 3. When standing facing you, can he keep his elbows tucked in and turn his palms to face you? 4. In a standing position, can she link her hands behind her back and raise them up away from her back to a level even with her waist? Did the individuals pass or fail all four items? Ask individuals about their favorite activities and see if you can account for the maintenance of flexibility by matching body areas to those activities. 568 Chapter 17 Development of Body Composition 569 Chapter Objectives This chapter reviews the effects of exercise on the body composition of children and youths through longitudinal research studies, notes any sex differences in the effects of exercise on body composition, examines the effects of exercise on body composition in middle and older adulthood, and discusses the recent increase in obesity in Western societies. 570 Motor Development in the Real World Obesity It is difficult to pick up a few popular magazines or watch a few television talk shows without encountering concern over obesity. This has been true for a number of years. What has become more common lately are articles and segments about the growing number of children worldwide who are obese. For all the media attention, the trend has not been reversed. Of course, much of the concern surrounds the relationship between childhood and adulthood obesity; obese children are very likely to be obese adults. In fact, obese children are encountering health hazards previously seen only in adulthood. So, not only are obese children at risk of health problems at the present, they will be increasingly at risk as they move into adulthood. Many today even use the term epidemic for this trend of increasing childhood obesity. Concern is ongoing about fitness and fatness, and rightly so. Alarming rates of obesity have prompted more attention to the roles of diet and exercise in body composition and to maintaining a healthy ratio of lean weight to fat weight at all points of the life span. Yet many do not understand the relationship of diet and exercise to body composition over the life span. This is valuable information, both for working in any professional role that involves diet and exercise and for one’s personal well-being. It is imperative that professionals continue to teach key aspects of the relationship and advocate for opportunities for people of all ages to implement healthful practices. Body mass can be divided into two types of tissue: lean tissue—which includes muscle, bone, and organs—and fat, or adipose tissue. The relative percentages of fat-free and fat tissues that make up the body mass give a measure of body composition. Many people care about body composition because it is related to appearance and it can influence individuals’ feelings about themselves. Many societies value a lean body appearance. Obesity may contribute to a negative body concept and negative self-concept, thus making it difficult for an obese person to relate to others. Aside from appearance, body composition is important in a variety of health issues: Higher proportions of lean body mass show a positive link to working capacity, and higher proportions of fat tissue show a negative link. Excess fat weight adds to the workload whenever the body is moved. Excess fat can limit an individual’s range of motion. Obesity places a person at increased risk of suffering coronary heart and artery 571 disease, stroke, diabetes, and hypertension. Body composition is often related to success in executing motor skills; that is, it serves as a structural constraint. For individuals who are overweight, it can also serve as a functional constraint. A body composition high in muscle mass and low in adipose tissue contributes to optimal performance. The muscle mass can be used to exert force, and low adipose tissue means that a performer does not have extra weight to move, both of which constitute advantages in many physical activities. In contrast, a body composition that is high in adipose tissue can make it difficult to move the body, especially for extended times, and difficult to achieve certain body positions. So, in addition to the health repercussions, overweight can be a rate limiter to motor skills. As noted in chapter 5, everyone has some fat tissue, which is needed for insulation, protection, and energy storage. Women need a certain level of fat tissue (approximately 12% of body weight) to support functions of reproduction. Only excess fat weight is negatively related to fitness and health. Attempts to reduce fat tissue to excessively low levels are equally a health concern. 572 Body Composition and Exercise in Children and Youths Genetic and environmental factors affect body composition. People can manipulate two major environmental factors—diet and exercise—to manage the relative amounts of lean and adipose tissue in their bodies. Maintaining body composition is in part a matter of balancing the calories consumed against the metabolic rate and the amount of physical exertion. The metabolic rate is the amount of energy an individual uses in a given amount of time to keep the body functioning. Rates vary among individuals; some use more calories than others do just to keep the body running. The metabolic rate is under the control of various hormones, and it cannot be easily altered in the short term. In contrast, an individual can control exercise level on a daily basis. This discussion focuses on the relationship between body composition and exercise. Because children are not biochemically identical to adults, dividing the body into fat and fat-free mass oversimplifies the changes in body composition that occur with growth. A more extensive breakdown, however, is beyond the scope of this text. This chapter considers what is known about the influence of exercise on fat and fat-free tissues in children and youths. This section considers the research of Jana Parizkova, much of which was published in the 1970s but which still constitutes a meaningful part of the little longitudinal work done on this topic. More recent cross-sectional, or short-term, studies are also considered. Assessing Amounts of Body Fat The amount of adipose tissue in the body can be measured in numerous ways. These measurements can be used directly to track changes with growth and aging, or they can be used to estimate the percentage of the body’s weight that is fat. We also have several methods for measuring lean body mass; they allow an estimation of body fat as well: Measuring the thickness of the skin and underlying (subcutaneous) fat with skinfold calipers. The amount of total body fat can be estimated from skinfold measurements taken at specified sites. This is one of the most common ways of estimating fat weight, especially in children. Weighing a person underwater and contrasting that value with normal body weight. This method estimates body density and, subsequently, the proportion of lean versus fat weight. It is difficult to take this measurement on young 573 children and adults who are afraid of being underwater. Analyzing the intensity of reemitted infrared light emitted by a probe into the biceps brachii muscle with a near-infrared interactance device. This is an easy measurement method to use with children but may not be as accurate as other methods (especially underwater weighing) (Smith et al., 1997). Measuring soft tissue composition with a dual-energy X-ray absorptiometer, which allows a direct measure of body density but requires expensive equipment (Steinberger et al., 2005; Sutton & Miller, 2006). Measuring air displacement (rather than water displacement) by air- displacement plethysmography. This measure requires a BodPod or Body Pod chamber but allows measurement of infants and obese individuals (Dioum, Gartner, Maire, Delpeuch, & Wade, 2005). Web Study Guide Evaluate changes in body mass index in a group of children over a period of 4 years in Lab Activity 17.1, Body Composition in Childhood, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. The Parizkova Studies Fat tissue increases rapidly during two periods: the first 6 months after birth and again in early adolescence. In girls, this increase continues throughout adolescence, whereas in boys the gain stops and may even reverse for a time. Muscle tissue also grows rapidly in infants, followed by a steady period of increase during childhood; it again increases rapidly during the adolescent growth spurt, more dramatically in boys than in girls. This typical pattern may be altered through either diet or exercise. Overeating results in excess fat weight, and starvation can lead to levels of fat so low that the body obtains energy by muscle wasting (breaking down muscle tissue to use as energy). Exercise, of course, burns calories, thus potentially altering a person’s body composition. Resistance training can increase muscle mass, especially after puberty. In cross-sectional and longitudinal studies, researchers have examined the relationship between exercise and body composition. Cross-sectional studies generally show that young athletes have lower proportions of body fat than do more sedentary children (Parizkova, 1973). However, it is impossible to determine from a cross-sectional study whether an active lifestyle results in leanness. (It could be the case that leaner children find activity easier and therefore adopt active lifestyles.) Longitudinal studies, then, are more valuable in the study of the interrelationships between activity levels and body composition. 574 Parizkova conducted a series of studies on body composition and activity levels of boys and girls in Czechoslovakia. The first study was cross-sectional and was one of the few studies to examine very young children; the remainder of the studies were longitudinal. In the cross- sectional study, Wolanski and Parizkova (1976; cited in Parizkova, 1977) compared skinfold measures in two groups of children aged 2 to 5 years. One group of children attended special physical education classes with their parents, whereas the other group did not participate in any type of physical-training program. Even at this young age, children in the physical education group had lower levels of subcutaneous fat. Teenage Boys In an extensive longitudinal study of teenage boys, Parizkova (1968a, 1977) divided nearly 100 boys into four groups by their activity level. Boys in the most active group (group I) were involved in basketball or track for at least 6 h a week. Boys in the least active group (group IV) participated only in unorganized and unsystematic activity. The boys in the other two groups had intermediate activity levels. Key Point In active teenage boys, increased body weight reflects increased lean body mass. Parizkova first tested the boys at an average age of 10.7 years, then followed them in successive years until they were 14.7 years old. Over the 4 years, the children in the most active group significantly increased in body mass while their absolute level of fat weight remained the same; hence, the fat proportion of their total weight decreased. In contrast, the boys in the inactive group increased significantly in absolute fat weight. The two groups did not differ in initial amount of fat weight, but they differed at the end of the 4 years. In the active group, the increase in lean body mass alone accounted for the increase of body weight with growth (figure 17.1, a and b). Physical activity had a beneficial effect on body composition in these boys. 575 Figure 17.1 (a) Relative percentage body fat in the most and least active groups of boys followed by Parizkova from an average age of 10.7 years to 14.7 years. (b) Lean body mass of the same groups in Parizkova’s study. Reprinted by permission from Parizkova 1977. Key Point Physical activity has a favorable effect on boys during the growing years in that it increases lean body mass and minimizes addition of fat weight. Parizkova (1972) then followed 41 of these boys for another 3 years, and the body composition trends of the first 4 years continued. The most active and least active groups differed in total weight by the time they reached age 16.7. The active group was heavier in total body weight because the boys’ lean body mass was greater. The active boys had less total fat weight than the inactive boys, and their fat weight actually declined in some years. Parizkova determined that the groups did not differ in average skeletal age, so the differences noted in body composition cannot be attributed to maturational differences. He also noted that the boys maintained their relative position in the group in both distribution and absolute amount of subcutaneous fat. This means that the relative amount of fat 576 weight and its pattern of distribution in the body were relatively stable over the years of the study. Parizkova followed 16 of these 41 young men for yet another 6 years. Although this number was too small for a reliable analysis by activity level, Parizkova (1977) noted that percentage body fat declined in the group until age 21.7 years, then varied widely among individuals, probably reflecting changes in lifestyle. The Parizkova studies, then, indicate that physical activity has a favorable influence on boys’ body composition during the growing years. If you were a high school principal, what implications would you see in the Parizkova study for the physical education curriculum at your school? Teenage Girls The growth of adipose and lean muscle tissue differs dramatically between the sexes during adolescence. Girls gain proportionately more fat than muscle compared with boys. Even so, the beneficial effect that activity has on body composition in boys also occurs in active girls. Over a span of 5 years, Parizkova (1963, 1977) studied 32 girls who belonged to a gymnastics school and 45 girls who were not engaged in any type of training. The girls were first measured at the age of 12 or 13 years. The gymnasts followed a regular yearly cycle of training in which they attended a rigorous camp in the summer, stopped training in the early fall, and resumed a heavy training schedule from October to December. These cycles are shown (for 11 of the gymnasts) in figure 17.2 as black-outlined bars; the higher the bar, the more intense the training. Measurements of the girls’ fat weight paralleled Parizkova’s findings with boys. The gymnasts remained at the same level of subcutaneous fat during the 5 years, and the total skinfold thickness showed no trend, even though it increased or decreased for short periods. In contrast, the control group gained a significant amount of fat weight. Height and body weight trends in the two groups were similar throughout the 5 years, so the differences were truly in body composition. 577 Figure 17.2 Changes in height, weight, and subcutaneous fat (sum of 10 skinfold measurements) in a group of regularly training female gymnasts (n = 11) during a 5-year period of varying intensity of training (see bottom scale). Reprinted by permission from Parizkova 1977. Key Point Teenage girls in training can increase lean body mass and decrease subcutaneous fat, even when they eat more calories in response to training. The cyclic nature of the gymnasts’ training schedule provided information about their weight and skinfold thicknesses as they progressed through the various training phases. During periods of inactivity the gymnasts gained in both total body weight and skinfold thickness (including subcutaneous fat tissue), but during training they increased in total body weight while their skinfold thicknesses declined. (Note that in figure 17.2, total skinfold thickness goes down when the intensity of training goes up, and total skinfold thickness goes up when training stops for a time.) Total height and weight keep increasing with age. Therefore, the weight increases during the various activity periods resulted from changing ratios of fat and lean body weight. Parizkova also recorded the gymnasts’ caloric intake and found that even though they consumed more calories during periods of intense training, fat deposits decreased and lean body mass increased. If you were a physical education teacher, what results from the Parizkova 578 studies could you use to counsel your teenage girl students on diet and exercise? Comparing Adolescent Boys and Girls These longitudinal studies by Parizkova showed the same general relationship between body composition and activity in both boys and girls, but they did not allow direct comparison of the sexes. So Parizkova (1973, 1977) simultaneously followed 12 boys and 12 girls engaged in swimming training from ages 12 to 16. At age 12, the average height, weight, lean body mass, and fat weight of the two groups were about the same. Lean body mass values for the swimmers were higher than the average levels for teens not in training, which probably reflected the swimmers’ previous training. By age 15, the boys were significantly taller, heavier, and leaner than the girls, but both sexes showed an increased proportion of lean body mass at the expense of fat weight over the first 3 years of training. Although higher in percentage fat than the boys, the girls did not gain as much fat as the typical nontraining adolescent girl. More research on this topic is necessary, especially to determine the length and intensity of training programs that have favorable results with girls. Tremblay, Despres, and Bouchard (1988) did not find a decline in fatness or a gain in lean body mass in girls after 15 weeks of intense training, although boys experienced significant changes in this period of time. How has your body composition changed throughout your life span? What do you think contributed to that change? How might you use your experience in working with others? Short-Term Studies Dollman, Olds, Norton, and Stuart (1999) compared more than 1,400 Australian children who were 10 and 11 in 1997 with a group measured in 1985. The 1997 children as a group were heavier and fatter, though slightly taller, than those in the 1985 group. They were also slower in the 1.6 km walk–run and 50 m sprint. These differences did not occur in the leaner and fitter children but rather in the one-fourth of the children who were fatter and less fit. Although it’s not clear whether one decline caused the other (or whether something else caused both), being fatter coincided with poorer fitness performance. Olds and Dollman (2004) attempted to address the issue of whether poorer performance reflected increased fatness or decreased activity levels by further studying the children from the 1999 study. They matched participants in the two groups for fatness. The 1997 group was still slower in the 1.6 km walk–run, which suggests that decreased physical activity played a role in the decreased performance of the 1997 group. 579 Key Point Increased fatness and decreased physical activity might both play a role in a secular trend toward poorer performance on fitness assessments. The Muscatine Study is a longitudinal investigation of cardiovascular disease risk factors among the residents of Muscatine, Iowa. Participants were measured for fitness, body composition, blood pressure, heart mass, and maturation level in youth. Janz, Burns, and Mahoney (1995) reported on a 2-year follow-up with more than 120 children who were 10 years old at the first set of measurements. They found that increased systolic blood pressure was associated with increased body fatness and decreased physical fitness. Thus, the two factors coincided in a group approximately the same age as that observed by Dollman et al. (1999). In summary, these investigations show that involvement in training programs favorably affects adolescents’ body composition. Limited information suggests that the body composition of preschool children also benefits from activity. Although children and adolescents who engage in active training exhibit the general growth trend of increased weight, this increase represents the addition of relatively more lean body mass and less fat weight than in their nontraining peers. A person’s higher caloric intake during training evidently increases lean body mass rather than fat stores. Key Point Appropriate levels of exercise in youths are associated with a healthier body composition. It is possible for a person to carry training to an extreme wherein the body cannot meet the energy required for continued growth. This condition mimics starvation and can lead to loss of lean body mass and detrimental effects on growth (Lemon, 1989). If you were a teacher or a doctor, what would be your strategy for reversing the trend of declining fitness in today’s youths? Motor Skill and Fitness The relationship between motor skill proficiency and level of physical fitness is of interest and has important implications for programming in childhood and adolescence. For 580 example, if one is concerned about the trends toward increased overweight and declining fitness levels in youths, would the answer be to focus physical education programs solely on fitness activities, or would there be value in focusing on both developing motor skill proficiency and fitness activities? It’s best to take a longitudinal approach for answers to this question. Barnett, van Beurden, Morgan, Brooks, and Beard (2008) measured youths approximately 8 to 12 years old on fundamental motor skills and a shuttle run-type cardiorespiratory test. They then repeated the tests 5 years later. They found that some of the fundamental motor skills—interestingly, the object-control skills rather than locomotor skills—predicted adolescent cardiorespiratory fitness. This was true for both boys and girls. Perhaps the youths with good object-control skills were more likely to participate in physical activities. Hands (2008) took a similar approach but included children who were classified as either low or high in motor competence. She also repeated her measures every year for 5 years. The two competency groups were different in all measures (the high competency group was better) except body mass index, for which no difference was found. The differences remained over the 5 years but widened on the shuttle run test for aerobic fitness and narrowed on a sprint run and a balance test. The children with low motor competency improved over the 5 years but never caught up with the children with high competency. Better motor skill was associated with greater cardiovascular endurance. A study by Vedul-Kjelsås, Sigmundsson, Stendsdotter, and Haga (2011) is a reminder that self-perception in children is related to motor competence and physical fitness. They found this relationship in all aspects of self-perception measured by Harter’s Self-Perception Profile for Children, social acceptance, athletic competence, physical appearance, and general self-worth. The authors suggested that these aspects of self-perception facilitate participation in physical activity. In addition to longitudinal research, Rivilis, Hay, Cairney, Klentrou, Liu, and Faught (2011) recently reviewed 40 studies dealing with the relationship between motor proficiency and physical activity in children with developmental coordination disorder. Their overall conclusion was that poor motor proficiency is associated with high body composition, low cardiorespiratory fitness, lower strength and endurance, lower anaerobic capacity and power, and less physical activity. Flexibility was the only fitness component not clearly related to motor proficiency. Generally, this research demonstrates that children with less motor proficiency are less fit. Of course, many fitness tests, especially cardiorespiratory fitness tests, require some level of motor skill. Better motor skill likely contributes to efficient test performance. Yet, this would be true in training as well. It appears that promoting the development of motor skill in youths can have a positive effect on fitness, and even children with low motor competency can improve over time on both skill and fitness measures. 581 Body Composition and Exercise in Adults In middle age, the average adult loses fat-free body mass and gains fat such that body weight increases and the portion of body weight that is fat increases. Of particular concern is an accumulation of trunk fat, which is associated with increasingly poor cardiovascular health. In old age, fat-free body mass and fat mass decline. It is important to remember that this is the typical profile and that individuals are extremely variable. Also, obese individuals often die before reaching older adulthood, which can change average measurements taken on groups of older adults. Exercise might favorably influence body composition in two ways: It could increase fat-free mass or decrease fat. The increase in fat-free mass could be an increase in muscle mass, an increase in bone density, or both. Some studies, discussed in this section, have tracked these changes in exercising adults. Middle-aged and older adult athletes and regular exercisers tend to maintain their muscle and fat masses, and many compare favorably with younger adult populations (Asano, Ogawa, & Furuta, 1978; Kavanagh & Shephard, 1977; Pollock, 1974; Saltin & Grimby, 1968; Shephard, 1978b). However, we cannot assume from these observations that the same would be true of the population at large or of sedentary older adults who begin training. It is possible that healthier older adults are more able to be active and that what is being observed is good health status rather than the benefits of exercise. For this reason, it is important to longitudinally study older adults for exercise effects. The current number of longitudinal studies is tiny, however, so we often must rely on short-term studies for information. Recent studies of changes in muscle mass with exercise have used computed tomography to document changes in muscle area. A study of men aged 60 to 72 (Frontera, Meredith, O’Reilly, Knuttgen, & Evans, 1988) and a study of men aged 86 to 96 (Fiatarone et al., 1990) reported increases in muscle area in the range of 4.8% to 11.4% after 12 and 8 weeks of training, respectively. Both type I and type II fibers increased. Other studies have found smaller changes (Forbes, 1992). It is clear that individuals are extremely variable. Fiatarone et al. (1990) reported on individual subjects who lost 8% of muscle area with training and subjects who gained 30% even though they trained the same number of weeks. These contradictions make it difficult to predict whether every older adult would see an increase in muscle mass with training. Studies of young athletes have concluded that regular exercise promotes bone growth, but the few studies of older adults have reached conflicting conclusions. This difference might derive in part from weak research methods. Going, Williams, Lohman, and Hewitt (1994) point out that in some studies, the exercise program undertaken by the adults did not stress 582 the body locations that were measured for bone density. Several studies of change in the bone mineral density of lumbar (lower back) vertebrae have shown improvements with weight training in premenopausal (Going et al., 1991; Lohman et al., 1992) and postmenopausal (Dalsky et al., 1988; Pruitt, Jackson, Bartels, & Lehnhard, 1992) women. Another study, however, reported a decline in bone mineral density (Rockwell et al., 1990). Researchers need to do much more work, especially on a wide range of older adults, and they need to determine the type, duration, and frequency of exercise that is helpful. Schwartz et al. (1991) placed 15 men between 60 and 82 years of age on a 6-month endurance-training program. Their training intensity gradually increased so that eventually they were walking or jogging 45 min per session, 5 days per week, at 85% of heart rate reserve. Over the 6 months, their body fat decreased 2.3% and their waist circumferences decreased 3.4%. Although the loss of body fat overall was small, the loss of fat in specific trunk locations was more dramatic, which is significant because of the association between trunk fat and increased cardiovascular risk. Paillard, Lafont, Costes-Salon, Riviere, and Dupui (2004) found that a relatively short-term walking program of 12 weeks for men between 63 and 72 years of age brought about a decrease of fat weight. No increase occurred in lean body mass or bone mineral density, though, for an intervention of this length. Key Point Adults can increase muscle mass and bone density with resistance training and lose fat weight with endurance training, but individuals are variable in their amount of change. Think about your own diet and fitness routines. Do you have habits that will keep your body composition similar to what it is now as you get older, or would a change be warranted? Although more longitudinal research in the area of body composition and exercise is needed, there are clear indications that exercise has a favorable effect on body composition. Observation of those engaged in vigorous activity over the life span demonstrates at the very least that a decline in fat-free mass and an increase in fat mass is not a foregone conclusion for everyone. Web Study Guide Measure skinfolds, height, and weight in Lab Activity 17.2, Comparing Body 583 Composition Measures, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 584 Obesity The prevalence of obesity is increasing around the world and in all age categories. Rates of obesity vary among countries; the prevalence is higher in industrialized nations. Increasing rates among the upper classes in developing countries, however, demonstrate the strong universal trend toward obesity (Kotz, Billington, & Levine, 1999; Rudloff & Feldmann, 1999). Various organizations and researchers use slightly different criteria to determine who is considered obese. The most common definition for adults is having a body mass index (BMI) of more than 30.0 (Kotz et al., 1999). Obesity is most commonly defined as a body mass index of more than 30.0. Body mass index (BMI) is the ratio of body weight (kg) to height squared (m); a normal index falls between 18.5 and 24.9. BMI is the ratio of body weight (kg) to height squared (m); normal is defined as the range from 18.5 to 24.9. This is a convenient measure to use in many settings because one needs only to be able to measure height and weight in order to calculate BMI. Yet body weight reflects both lean and adipose tissue, so the BMI might be misleading for some individuals with above-average lean muscle weight. This limitation should be kept in mind when reviewing research using the BMI measurement. It is challenging to define obesity for children because of ongoing growth, but one frequently used criterion is a weight-for- height measurement over the 95th percentile; another is a triceps skinfold over the 95th percentile (Rudloff & Feldmann, 1999). Obesity is a concern at any point in the life span, yet chances are great that obese children will remain obese into adulthood, and obesity tends to be stable over young, middle, and older adulthood. Hence, there is a sense of urgency about addressing obesity in children even while recognizing its medical and social repercussions at any age. In the United States about one-fourth of children and adolescents are obese, an increase of 54% in children and 39% in adolescents over 20 years (Rudloff & Feldmann, 1999). Parents often believe that their child’s obesity is caused by a metabolic or thyroid disorder. In fact, these disorders account for less than 1% of obesity in children (Dietz & Robinson, 1993). What, then, is the typical cause? Obesity is a good example of the interaction between genetic and extrinsic factors. Certainly genetic factors are related to obesity. BMI is highly correlated in twins, even if they are raised apart, but is poorly correlated in parents and adopted children. However, no single genetic factor is related to obesity in all individuals. Various factors under genetic influence include basal metabolic rate, dietary thermogenesis, appetite control and satiety, and lipid metabolism and storage (Rudloff & Feldmann, 1999). Basal metabolic rate is the amount of energy needed to sustain the body’s vital functions in 585 the waking state. Thermogenesis is the production of heat in the body. The increase in obesity over the past several decades demonstrates the strong influence of extrinsic factors on obesity because genetic influences could not change the incidence rate so rapidly (Rosenbaum & Leibel, 1998). Increasing modernization reduces energy expenditure as laborious tasks are taken over by machines (figure 17.3). A Westernized diet, high in fat and sugar, is also a major factor in obesity (Kotz et al., 1999). Because genetic predispositions are fixed, manipulation of energy intake and expenditure is the most available means for altering body fatness during the life span. Key Point The trends in extrinsic factors that lead to obesity are problematic on two counts: People are less active, and diets increasingly consist of high levels of fat and sugar. 586 Figure 17.3 It is increasingly common among individuals of all ages to devote considerable time to screened devices, such as computers, tablets, smart phones, and televisions. As a result, more people tend to be sedentary rather than active. These environmental and task constraints interact with body systems to create a downward health spiral: Lean body mass decreases, fat tissue increases, and the cardiovascular system develops increased risk for disease. Restricting caloric intake in children is challenging because sufficient energy must be provided to support growth. Overweight children typically do not eat large quantities. Rather, they have a small but daily caloric imbalance (Dietz & Robinson, 1993). A relatively modest adjustment of calories with good nutritional balance in diet can be very effective. However, reduced motor activity is a common characteristic of obese children (Roberts, Savage, Coward, Chew, & Lucas, 1988), and increasing calorie expenditure through exercise has multiple benefits in altering body composition fat. First, it can offset the decrease in basal metabolic rate that accompanies caloric restriction. Second, it can promote the growth of muscle tissue, which requires more calories for maintenance than fat tissue requires (Bar-Or, 1993). This difference is significant because without exercise, 30% to 40% of the weight lost with caloric restriction in adults is from lean body mass (Harris, 1999); this is likely the trend with children, too. Hesketh and Campbell (2010) reviewed 23 studies on interventions for preventing obesity in toddlers and young children up to 5 years of age. Most of the studies reviewed were conducted in preschool or child-care settings and home settings, and about half of the studies were done with disadvantaged participants. Researchers tended to use multiple modes of intervention, such as improved diet, increased physical activity, and reduced 587 sedentary behavior. The nature of the intervention programs was varied and the success of the programs was mixed, but overall the studies demonstrated that interventions could positively affect obesity, especially if parents were involved. A synthesis of reviews and meta-analyses (Khambalia, Dickinson, Hardy, Gill, & Baur, 2012) on obesity interventions in school-aged children suggested that intervention programs were more likely to yield a significant weight reduction in children if they were long term rather than short term. Additionally, programs tended to be successful if they combined improved diet, increased physical activity, and family involvement. Although the research conducted on intervention programs thus far has been helpful, more research is needed on programs that measure both weight-related outcomes and health-related outcomes. Key Point Exercise is an important strategy in altering obesity because it expends calories and offsets a decrease in basal metabolic rate that accompanies caloric restriction. It is well established that obese children do not perform as well as lean children do on a variety of physical fitness and motor skills tests. Malina et al. (1995) selected a group of Belgian girls between 7 and 17 years of age. At each age, the leanest 5% outperformed the fattest 5% on arm-strength and endurance tasks, trunk-strength tasks, the vertical jump, an agility shuttle run, and a balance task. Adolescent boys show the same performance differences (Beunen et al., 1983). On the other hand, participation in regular physical activity reduces fatness in obese children. Sasaki, Shindo, Tanaka, Ando, and Arakawa (1987) found that a 2-year program of daily aerobic activity significantly decreased skinfold thicknesses. Even short-term training programs of 10 weeks and 4 months yielded decreased body fat percentages in obese children between 7 and 11 years of age (Gutin, Cucuzzo, Islam, Smith, & Stachura, 1996; Gutin, Owens, Slavens, Riggs, & Treiber, 1997). Saavedra, Escalante, and Garcia-Hermoso (2011) conducted a meta-analysis of studies on the improvement of aerobic fitness in obese children. They found that programs that lasted more than 12 weeks and provided 3 sessions (more than 60 min per session) per week yielded better results. They also determined that programs focused on aerobic exercise had an effect on aerobic fitness whereas those that combined aerobic exercise with other exercise, especially strength training, did not, most likely because of the extended time spent in strength training. Korsten-Reck et al. (2007) demonstrated that a comprehensive intervention program for obese children brought about favorable changes in body composition, aerobic endurance, and motor ability tasks. These findings imply that activity programs might be relatively difficult for obese children, yet the benefits of regular activity can be significant. Well-designed programs are needed to set the workload appropriately for 588 obese children. If you were a middle school physical education teacher, how might you intervene with your obese students to put them on a path toward improved health? The incidence of obesity increases in men and women from age 20 to age 50 (Kotz et al., 1999; figure 17.4). Obesity puts individuals at risk for hypertension, cardiovascular disease, diabetes, gallstones, osteoarthritis, and some forms of cancer; hence, the obese are at greater risk of early mortality. In fact, a decrease in the prevalence of obesity in the 70s and 80s might reflect the shortened life span of the obese. The association of obesity and mortality is stronger among those whose increased fatness is particularly concentrated in the abdomen (Kotz et al., 1999). 589 Figure 17.4 The prevalence of obesity with advancing age in adults. Declines in prevalence in later decades reflect the greater risk of early mortality in the obese; that is, declines reflect that many obese individuals died at younger ages than their nonobese counterparts. Data from National Health and Nutrition Examination Survey III. As at younger ages, genetic and extrinsic influences play a role in adult obesity, though the relative contributions of each can vary in an individual over the life span (Rosenbaum & Leibel, 1998). It is well known that activity levels are low among adults in Westernized countries. In the United States, less than one-fourth of adults regularly exercise at least 30 min per day, and 24% are sedentary. Of overweight adults, 41% of women and 33% of men are completely sedentary (Cowburn, Hillsdon, & Hankey, 1997). As with children and teens, a combination of caloric restriction and increased activity is the most effective strategy in altering body fatness among adults. Research studies show that progress is greater with a combination than with exercise alone or caloric restriction alone. Even a modest 10% loss of body weight can have a substantial benefit for cholesterol levels, fasting blood glucose levels, and blood pressure (Harris, 1999). 590 Summary and Synthesis Body composition is an important component of physical fitness and is related to physical performance. Although body composition is related to genetic factors, the extrinsic factors of diet and exercise can greatly affect an individual’s relative levels of fat and lean body mass. A person of any age who wishes to change his or her body fatness can manipulate both diet and exercise. Regular exercise can play a large and favorable role in altering body composition because it promotes muscle mass and an increase in basal metabolic rate. In turn, a body composition higher in lean mass and lower in fat mass makes exercise and physical performance easier. Reinforcing What You Have Learned About Constraints Take a Second Look The obesity issue highlights the interactive nature of the body’s structural constraints and movement. Physical activity can alter the structural constraints over time and thus permit movements requiring fitness. In the case of obesity, being physically active contributes to reduction of body weight, thus making movement easier and more proficient. A lack of activity can alter the structural constraints over time. Not only is the body heavier, but the eventual effect on the body systems contributes to restriction of activities and movements, especially those requiring a certain level of fitness. Individuals can change their fitness levels and body composition with training, but age-related differences exist in the effect of training. Teachers, parents, coaches, therapists, and movers themselves must consider how the individual constraints related to the fitness systems interact with environment and task. Test Your Knowledge 1. How does regular participation in physical activity affect body composition in children and adolescents? 2. What are the sex differences in body composition? Does exercise affect the body composition of males and females similarly or differently? How so? 3. What are the best weight-management strategies for obese children? 4. What are the favorable effects of exercise on the body composition of older adults? 5. What negative effects does obesity have on children? On middle-aged and older adults? 6. How is body composition assessed? What are the advantages and disadvantages of specific techniques at various points in the life span? Learning Exercise 17.1 591 Children and Obesity The increase in the number of children around the world who are obese is of great concern to all of us. In the past few years this topic has received much attention in the popular press, and many people have tried to identify the causes and suggest interventions in order to reverse the trend. Conduct an Internet search and find three solutions that have been proposed to reverse the trend of increased obesity in children. Do the writers present evidence that would lead you to believe the solutions would work? Are you persuaded that there is one solution that would work, or do you believe that multiple solutions are necessary? Why? 592 Chapter 18 Conclusion: Interactions Among Constraints Applications to Movement 593 Chapter Objectives This chapter allows you to examine, all at one time, the individual, task, and environmental constraints and their interactions affecting an individual; encourages you to see each individual as unique; demonstrates how you can use the model to manipulate constraints for a specific educational or therapeutic purpose; gives you practice in structuring developmentally appropriate learning environments and designing developmentally appropriate learning tasks; provides a framework for charting constraints to both enhance developmentally appropriate teaching and track and assess progress; and provides case studies so that you have the opportunity to apply your knowledge of motor development to real-life situations. 594 Motor Development in the Real World The Paralympic Games The following quote is from the official website of the 2004 Athens Paralympic Games: “The Paralympic Games is the top sports event in the life of every Paralympic athlete . . . . In the Paralympic Games, athletes engage in obstinate, noble, and sustained competition to achieve the highest sports distinction. Their efforts are guided and shaped by a unique strength and determination. Their strength and ability to overcome hardship becomes a shining flame, a pole of attraction for everyone who values sports as the highest expression of humanity.” The Paralympic Games have been held the same year as the Olympic Games since 1960 and in the same city since 1988. The London Games in 2012 were the largest to date, with 4,302 athletes from 164 countries competing in 21 sports. The events include, among others, four wheelchair sports, sitting volleyball, swimming, powerlifting, judo, and, of course, track and field. What does this description of the Paralympics mean to you now that you have finished reading this text on life span motor development? We hope that you view Paralympians as people who have unique sets of constraints along with many others that are common to all humans. The structural and functional individual constraints of these Paralympians do not stop them from participating in physical activity at the highest levels. Their constraints allow them to move in activities of daily living and compete at a high level of athletics. These interacting constraints include high levels of strength (structural), strong motivation (functional), a supportive environment (sociocultural), and high-tech equipment (task), among others. Put these elements into the context of a sporting event and the results are elite, record-breaking athletes. Years ago Paralympians might have been labeled disabled, just as special needs children once were. We hope that you now see individuals not as labels but rather as unique persons who possess unique combinations of individual functional and structural constraints. Those individual constraints interact with the environmental and task constraints to provide both possibilities and challenges in moving. Not everyone can be—or wants to be—an elite athlete, but everyone moves constantly, every day. All movement occurs in a context and results from an interaction of constraints. Certain constraints may influence movement behavior more at a particular time than others do, and some can change drastically over the course of the life span. But all exist and 595 interact, allowing movement to emerge. Why have we spent so much time emphasizing this point? Throughout this text we use a developmental perspective in discussing the different types of constraints that affect motor development across the life span. To conceptualize how constraints work, we separate them into individual, environmental, and task constraints. It is important to realize, however, that although one type of constraint may be more influential at any given time, all are present and constantly interacting. In fact, something can act as a constraint only when it interacts with an individual in a movement context, which means that you must understand how constraints affect each other. At first glance, this may seem somewhat confusing. However, assessing the influence of constraints on each other is what we have been doing all along. Remember the girls’ volleyball team discussed in chapter 4, where the younger players were taller than the older players? The significance of the story is related to individual constraints (height, strength, maturation), environmental constraints (the height of the net, the weight of the ball, sociocultural expectation of playing volleyball), and task constraints (rules of volleyball, the goal of a particular skill) all acting together. If any of the constraints are changed, motor development will change. For example, what if these girls were growing up in a country where volleyball was not as popular a sport? Looking at the interaction of constraints is helpful in understanding life span motor development. The most important message of this text is that manipulating constraints can be useful in influencing movement and motor development (figure 18.1). Manipulating a constraint at any given time may produce a functional change in movement. However, in motor development, we are most concerned with changes in movements over time, particularly ones that become more permanent or structural. We emphasize this point: If short-term change in a constraint leads to a short-term change in the interaction among constraints, it can lead to long-term change in motor behavior. In other words, we can influence our own motor behavior and that of others by manipulating constraints to make them more developmentally appropriate. Isn’t that the point of teaching and rehabilitation? 596 Figure 18.1 By manipulating task constraints—in this case, providing a prosthetic limb—a person with unique individual constraints can participate in many different physical activities in the same capacity as a person with typical individual constraints. 597 Using Constraints to Enhance Learning in Physical Activity Settings In everyday life, people frequently modify constraints in order to change movements. These adjustments can enable movements that otherwise might have been difficult or impossible. For example, individuals using wheelchairs can perform activities of daily living more easily if their household appliances (e.g., sinks, stoves, and countertops) are scaled to their relative (seated) height. Sometimes without even thinking, an individual alters something in the relationships between himself or herself, the environment, and the task at hand. For example, we would probably slide a heavy book across the table and closer to us before picking it up rather than attempt to lift it at arm’s length. Or consider children learning to play the violin: Because children have small hands and arms, music teachers often provide them with smaller instruments, scaled to their body size, so they can succeed in learning to play skillfully. These examples illustrate that modifying a task or environmental constraint can allow for a more developmentally appropriate, functional motor skill. For movement educators, it is important to consider all types of constraints and how they interact. As you might expect, interaction is a dynamic process and may result in a change in one or all of the interacting constraints. Movement educators, therefore, must attempt to manipulate constraints to allow their students or clients to perform skills more proficiently or to achieve a certain goal (Gagen & Getchell, 2004). Key Point Constraints can often be manipulated to make a movement developmentally appropriate. Physical educators often manipulate constraints when designing play experiences for students in their classes. Let’s use the example of basketball. Thinking of Newell’s triangle, imagine a child in a movement setting with the structural constraint of being short, the environmental constraint of the basket being high, and the task constraint of the goal of shooting the ball into the basket. Most of the situations in teaching and rehabilitation can be even more complicated than this. Let’s adapt Newell’s model by moving away from the triangle and using another shape to represent a larger number of relevant constraints. In figure 18.2, a hexagon is used to illustrate two structural constraints (being short and possessing moderate strength), two environmental constraints (a high basket and a large and heavy ball), and two task constraints (to shoot the ball through the basket and to use a one-hand set shot). Clearly, this combination of constraints is not likely to result in a movement of successfully shooting the basketball through a basket at the regulation height of 10 ft (3 m). Movement educators can adjust this play experience by altering one or more 598 constraints. 599 Figure 18.2 Using a hexagon instead of a triangle allows a model of constraints to represent more than one individual, environmental, and task constraint. Theoretically, one can manipulate all constraints—individual, environmental, and task. Practically speaking and on a day-to-day basis, however, educators cannot change children’s structural constraints. Over the course of a semester, individual constraints such as height and weight may change very little, and changing functional constraints such as fear or motivation may require longer time periods. Therefore, teachers must accept that individual constraints cannot be easily manipulated on a given day in the gym. However, by modifying environmental or task constraints, movement educators manipulate the interactions between the constraints and thereby facilitate change to allow and encourage more proficient or desired movements. Returning to our basketball example, a teacher or coach realizes that the structural constraints cannot be changed in the short term and that the task constraints or goals can remain if adjustments are made to the environmental constraints. Figure 18.3 depicts the basket height. In the original scenario the hoop was at the standard 10 ft height, but if the teacher or coach lowers the hoop, the movement arising from the interaction of the constraints pictured might be the desired one. The teacher or coach could also provide a ball that is slightly smaller and somewhat lighter, making the desired movement even more likely to result from the interaction of these constraints (see figure 18.4). In addition, the children will be more successful, which will keep them engaged and excited about movement experiences. 600 Figure 18.3 Changing one environmental constraint, basketball basket height, while the other constraints remain the same changes the interaction of this constraint with all the others. 601 Figure 18.4 Changing the other environmental constraint, size and weight of the basketball, instead of changing basket height again changes the interactions among the constraints. Of course, structural constraints change over time and with growth, maturation, and experiences of individuals. Individuals get taller and stronger (see figure 18.5). As they do, basketball teachers and coaches can adjust the basket height, eventually to that established in rules for mature players, and can provide bigger and heavier basketballs until players again use a standard-size ball. Adjusting the environmental constraints in relation to the changes in structural constraints permits the same movement outcome. When teaching a learner over a time of growth and maturation, an instructor who keeps the environmental constraints developmentally appropriate permits the desired movement outside to arise from the interaction of all the constraints. If the instructor stubbornly maintains an environment intended for bigger and stronger movers, the movement outcome will be very different. Therapists take a similar approach. They adapt environmental and task constraints to an individual’s current structural constraints. As individuals improve by increasing their range of motion or strength, therapists adjust the constraints in concert. The relationship remains approximately the same among the constraints, but with small adjustments in environmental and task constraints, structural constraints that are limiting gradually improve to—or even surpass—the level before an injury or surgery. 602 Figure 18.5 Young persons’ individual constraints clearly change with experience, growth, and maturation. This model represents an increase in height and an increase in strength. Teachers and coaches can change environmental constraints at a rate consistent with the change in individual constraints so that the movement outcome is the same. That is, the environmental constraints of basket height and ball size and weight are scaled up (e.g., moved toward regulation dimensions) as the individual constraints change. This way the movement will match the task goal of shooting a one-hand basketball set shot can be attained throughout the period of growth and maturation. Key Point Although children’s structural constraints cannot be altered in the short term, environment and task can be manipulated so that the resulting movement is developmentally appropriate. They can be continuously adjusted as an individual’s structural constraints change. In Paralympians, environmental and task constraints are often changed in concert with the athletes’ structural constraints to permit the same sport movements that can be executed by other athletes. In archery, a bow can be adapted so that an individual missing an arm can draw the bow with his or her teeth. This is an adjustment to equipment. In sitting volleyball, adjustments are made to equipment and to the rules. The net is lowered to be slightly over 1 m high and the rules require that some part of a player’s body between the buttocks and shoulders remain in contact with the floor when a ball is played. In other cases, a prosthetic, as an additional piece of equipment, allows a desired movement. Constraints can also be adjusted to help push movers to different movement patterns. Recall the developmental sequence for upper arm action in the overhead throw. Imagine that a teacher observes many children in her class using a step 1 movement pattern: 603 throwing with the elbow pointing downward rather than aligned with the shoulders. The teacher wants students to perform a throwing motion with an aligned upper arm. The teacher designs a task (i.e., structures a learning environment) that is more likely to result in a step 2 movement pattern (see figure 18.6). The instructor designs a game called “clean house” in which players throw small balls up and over a volleyball net until all of the balls in play are on the other team’s side of the net. The teacher provides small balls that the children can throw with one hand and puts a volleyball net at a height that requires them to throw up and over but not so high that they cannot get balls to the other side. It is difficult to throw over the net with the elbow pointing down, so the environmental and task constraints encourage the children to use a movement pattern that brings the upper arm closer to parallel. This is another way that movement instructors can manipulate constraints in a developmentally appropriate way and encourage a new movement. With time, the new movement pattern becomes the preferred pattern for throwing. 604 Figure 18.6 The expanded model of constraints can help teachers and coaches identify ways to create a new movement outcome. In this figure the constraints are set to encourage young throwers to align the upper arm and elbow with the shoulders (arm step 2 in the throwing for force developmental sequence). Web Study Guide Use the model of constraints to assess a playground space in Lab Activity 18.1, Assessing a Play Space, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. Structuring the Learning Environment In this textbook, you have read about many developmental changes that occur across the life span. Movement educators should keep these changes in mind as they structure their learning environments. For example, it’s easy to consider a gymnasium a static environment. However, factors such as wall color can influence how proficiently a child catches a ball. Remember, younger children have greater difficulty discriminating objects from the environment and benefit from more salient visual cues. To provide these cues, a physical educator can purchase equipment that is multicolored or distinct from the wall color. What if new equipment is not an option? Why not tape white paper to the wall as a backdrop? In a rehabilitation setting, the environment can be structured to be more ecologically valid, or more like the real world. The setting could be restructured to resemble a home or work environment, which would facilitate movement in that context. When young children run at play, the running surface dictates how fast they run and how 605 well they can remain on their feet. Long, clumpy grass presents different problems to running children than do blacktop playground surfaces or slippery tile gym floors. Weather is another environmental aspect that may influence activities. Running a mile on a hot, humid day, when breathing is difficult for most anyone, will be nearly impossible for some. Planning more strenuous activities for days when the temperature is cooler, the humidity is lower, and the air is clearer of pollen or pollutants (e.g., after a rain) will allow students to be more successful (Gagen & Getchell, 2004). Let’s not forget the sociocultural environment. When selecting activities and games, teachers can choose games that do not promote success based on sex, race, ethnicity, or socioeconomic status. For example, when activities are gender neutral, both boys and girls feel comfortable playing and succeeding. Some new games promote the same movement skills as traditional games but do not have particular sociocultural associations—team handball or sepak (a sport in which players kick a ball over a volleyball-type net) are examples. This approach opens opportunities to children who otherwise might not participate in a more traditional American sport that has been stereotyped. Different types of environmental constraints influence the structure of the learning environment. Manipulating the environment—or at the very least, being mindful of its influence—will allow movement educators in many fields to create a setting that promotes movement proficiency. Key Point More proficient movement might emerge when environmental and task constraints are adjusted for an individual’s structural and functional constraints. Designing the Learning Task No matter what the setting, movement educators design learning and rehabilitation tasks. The model of constraints provides a methodical means for designing the tasks. Consider how the interaction of relevant constraints encourages particular movement skills, keeping in mind that making changes can make skills easier or more difficult to achieve. How do the goals and rules of the task, as well as the equipment used in a task, interact with students’ unique individual constraints in the class environment to allow the children to perform a desired, successful movement? Task goals are the behavioral outcomes of the lesson. Teachers choose the goal of a task to encourage certain desired movements. Consider the task of throwing a ball. How a child throws depends on the goal of the task: to throw the ball as far as possible, as high as 606 possible, as accurately as possible, or as quickly as possible. The throwing movements that result from each of these different task goals will differ substantially. If children are young, small, or not very strong, some task goals are developmentally inappropriate and will not result in the practice of good throwing technique. For example, competitive games such as “pickle” (a game that simulates a baseball runner caught between two bases while two throwers attempt to get the runner “out”) encourage children to throw quickly but may inhibit the use of appropriate throwing technique. When children focus on competition, they may simply pick up the throwing objects and use any method to propel them in the interest of speed. This does not encourage children to set their feet and use an appropriate backswing or aiming technique, nor does it promote correct use of all the body parts that should be sequentially involved in applying force and direction to the throw. In this example, then, if the task goal is proficient throwing technique, competitive games should be avoided with young children. Movement educators can carefully manipulate the task goals so that children achieve an intended skill without being conscious of the intent. If a teacher places large pieces of paper against a wall and asks her students to “make the biggest noise possible,” the students will throw harder without any potentially negative comparisons with their classmates for not throwing as far as another child. In this case, throwing harder without throwing farther will often encourage children to use better technique; throwers will get feedback from the noise of the paper targets as the balls hit them, but the students need to control the throw in order to hit the target. The game “clean house” described previously is an example of encouraging a specific movement by using a manipulation of a learning task in the form of a game rather than direct instruction. Teachers can also modify the rules of a task to elicit a desired movement behavior. Teachers often modify games (change the rules) to encourage different movements or levels of participation. Playing three-on-three soccer on small fields is a game modification that allows shorter, more controlled kicking and receiving of the ball and more participation by each child, thus changing the focus of the game from running and chasing to movement technique. Playing volleyball with rules that allow the ball to bounce once will often give children the time to move into a better striking position, thus allowing them to use more appropriate striking technique. Requiring three passes before a shot in basketball promotes team play and cooperation. Such modifications can also be made so that children with disabilities can participate with their typically developing peers (Getchell & Gagen, 2006). For example, volleyball can be played from a seated position similar to the Paralympic sport, allowing for the inclusion of children who have a disability involving the lower limbs (e.g., certain types of cerebral palsy or spina bifida). The game can be enjoyable for a class of students, even those capable of playing “stand up” volleyball! Body scaling is a relatively easy way to manipulate task constraints by modifying the equipment and play spaces in proportion to the physical size or strength of the movers. 607 Movement educators and rehabilitation specialists often scale equipment and play spaces to assist movers who have smaller stature or less strength. Bats, rackets, golf clubs, and balls designed for women are often smaller and lighter than those designed for men, and those designed for children are smaller and lighter still. Soccer fields and base paths for children’s leagues are often shortened to better match the shorter legs of younger children. Shorter volleyball nets and 6- and 8-foot basketball standards are thought to promote more effective ball skills in younger performers (Chase, Ewing, Lirgg, & George, 1994; Davis, 1991). Thus, when teaching, coaching, or rehabilitating people, movement educators should very carefully think through the process of choosing equipment to match physical size and strength. Smaller balls that fit into smaller hands are easier to throw, but larger balls are easier to catch (Payne & Koslow, 1981). Therefore, the overall goal of the task (e.g., throwing or catching) must always be kept in mind. Key Point Body scaling the equipment and the play spaces used in an activity is a way to modify task and environmental constraints to permit certain movements. Let’s consider the example of a striking task for children, that of batting a ball. A range of bat characteristics must be considered in relation to the child: the weight of the bat (the child’s strength interacting with gravity to allow her to swing the bat using correct technique), the length of the bat (the child’s ability to judge where the barrel of the bat will be relative to the ball and his own body), the grip size of the bat (so that the child’s hands can fit around the grip to hold it well), and perhaps the size of the barrel of the bat (a wider barrel provides more surface area and perhaps a greater chance of contacting the ball). Certain choices can allow the child to swing the bat easily, whereas other choices can lead to difficulty in swinging it. A good bat choice for very young children might be lightweight with a small grip, and short but with a wider barrel. When educators work with a group of children, they should expect a wide range of size, strength, and maturation levels. Providing bats with a wide range of characteristics gives each child the opportunity to select a bat that he or she can succeed with (Gagen & Getchell, 2004, 2006). Ecological Task Analyses: Charting Constraints to Enhance Developmentally Appropriate Teaching Imagine that you are coaching for the first time a volleyball team made up of 10-year-olds. Height, weight, and skill level vary widely in the group. How do you approach coaching this team? You could simply teach the skills they need to learn by showing them the “correct” form, then making them practice over and over again. This approach might or might not provide results over the long run, but it will probably prove to be frustrating or 608 boring for all involved. Is there a better way to teach motor skills? By using ecological task analysis (Burton & Davis, 1996; Davis & Broadhead, 2007; Davis & Burton, 1991), you can create developmentally appropriate lesson plans and assess movement ability. In general, a task analysis is exactly what it sounds like—an analysis of how a particular task or skill is accomplished, focusing on critical components that influence movement. This analysis is usually performed by a teacher, therapist, or coach who is interested in developing motor proficiency in children performing the task or skill. Once a task analysis is developed, it can be used as a guide to help advance motor performance in that task in small, sequential steps. When someone uses traditional task analysis to teach or coach, he compares the movement pattern of an individual with the “correct” form; in that way, traditional task analysis provides an error model. Each person moves somewhere on the continuum of incorrect to correct. The instructor teaches skills by interceding in the production of the skill wherever it deviates from the ideal performance and correcting that portion of the movement. What could be improved in this approach to teaching movement skills? For one thing, the traditional task analysis doesn’t account for the different individual constraints each person might have. Second, no real consideration is given to the ways in which the environmental and task constraints might act in conjunction with individual constraints. Ecological task analysis, in contrast, does both of these things. The term ecological is a nod to the theoretical overview from which it was developed, the ecological perspective (see chapter 2). As we have discussed throughout this text, the ecological perspective acknowledges that action does not happen in a vacuum; rather, movement is influenced by the mover’s environment as well as the goals and rules of the task. Ecological task analysis acknowledges the confluence of constraints and uses them to the advantage of the teacher or coach so that a developmentally appropriate, or skill-level-appropriate, challenge can be provided to students (Newell & Jordan, 2007). Furthermore, each person moves on a developmental continuum, which enhances the current ability of the performer rather than labels it as correct or incorrect. Creating a Developmentally Appropriate Ecological Task Analysis Burton and Davis (1996) outline four steps involved in creating an ecological task analysis. The initial step involves establishing the task goal through structuring the environmental constraints. Next, the movement educator should allow the mover to solve the movement task in a variety of ways. In other words, don’t provide one “solution” to the movement task (e.g., “Throw like this”); instead, let the mover pick from a variety of available movements. In the next step, which comes into play after the individual moves, the educator manipulates the mover, environment, or task in a way that allows more proficient 609 movement to emerge. Finally, the movement educator should provide instruction to assist in a more proficient performance. What does a movement educator do as a first-time teacher who has never attempted to make a constraints-based task analysis? Here is a practical method of working through the process using a constraints perspective. Essentially, teaching any given skill involves three steps. Begin by considering the most important individual constraints related to that skill. Of course, there may be many, but try to narrow the list to the two or three that seem most important or influential. Consider the skill of kicking a ball. Three important individual constraints include balance (an individual must balance on one foot while striking the ball with the other), coordination (an individual must sequence and time the action within and between legs), and strength (an individual must strike the ball with sufficient power). Now consider ways to change the environment or task to make the skill easier or more difficult in relation to the individual constraints. This is the basis of the constraints-based task analysis. If coordination is an important individual constraint to kicking, what changes can be made to the task to make it easier or more difficult? How about changing the movement of the ball? Kicking a stationary ball is easier, and kicking a moving ball is more difficult. This change in task constraint is also related to balance. To account for strength, changing the distance to be kicked changes the individual–task constraint interaction. To develop an ecological task analysis, systematically scale environmental and task constraints to accommodate individual constraints in a developmentally appropriate manner. To summarize, the process is as follows: Pick out a skill or task to teach. Determine the individual constraints that are most important for this skill. Pick several environmental or task constraints for this particular task that can be manipulated in relation to each individual constraint. For each environmental or task constraint, determine a practical range (from easy to hard) for the learner. When scaling, keep in mind that small changes in a constraint can lead to large changes in performance. The finished product is an ecological task analysis for a particular skill (figures 18.7 and 18.8). There are two ways to use your task analysis. First, you can use it to structure lesson plans or teaching progressions. If an individual or group has difficulty with the task as initially designed, the task analysis suggests changes in task and environmental constraints to make the challenge more appropriate. As individuals progress in skill level within a particular profile, the task analysis suggests ways to make the task more difficult by scaling up one or more of the constraints. This keeps the task interesting, rewarding, and challenging for learners. Individuals are more likely to succeed when a new challenge is slightly more difficult than what they can achieve easily, so it is best to be conservative in the number of constraints made more difficult at any one time. Building success on success through small steps in difficulty contributes to confidence. Varying combinations of 610 constraints also creates many practice activities, which keeps the learning process interesting and challenging. 611 Figure 18.7 General task analysis for throwing behavior. Reprinted by permission from Herkowitz 1978. 612 Figure 18.8 General task analysis for striking behavior. (a) Profile of a general task analysis for a relatively simple striking task (dotted line). (b) Profile of a general task analysis for a relatively complex striking task (solid line). Reprinted by permission from Herkowitz 1978. How do the model of constraints and ecological task analysis relate to one another? Imagine that the columns of a task analysis chart represent various constraints and show how each constraint could be scaled for a particular individual or group of individuals with a common characteristic. Figure 18.9 illustrates this relationship. The constraints placed on the hexagonal model can be entered as column headings on a task analysis chart. The easiest relevant level of a constraint can be written at the top of the column, the most difficult relevant level at the bottom of the column, and intermediate levels in between. The tasks analysis chart helps an individual identify changes to the constraints that scale to the other constraints so that the desired movement outcome arises from their interaction. 613 Figure 18.9 A hexagonal model of constraints can be converted to an ecological task analysis table by using the constraints as column headings. The levels along the continuum of change for each constraint are then entered into the table in order from easy (at the top of the column) to difficult (at the bottom of the column). The ecological task analysis also provides a way to standardize a test environment so performers can easily be compared with each other (or with themselves on different occasions). In this case, select a particular profile and set up an assessment environment accordingly. Using these standard task and environmental constraints, assess students’ developmental levels for that skill within that profile. This approach allows for a more systematic assessment between students and over a set period of time. In many programs, instructors or therapists are held accountable for the progress of learners and patients. Task analysis charts can provide a written record of how an initial developmentally appropriate task was progressively made more difficult. It can indicate the combination of constraints that yielded a successful performance outcome, perhaps to some criterion such as four out of five attempts, at a particular point in time. If instructors and therapists prepare the task analysis chart ahead, they can quickly circle the constraints for a successful performance. Charts can be completed for individuals or groups and can both suggest an adjustment based on performance and document the performance for a specific combination of levels of constraints. 614 Interacting Constraints: Case Studies By manipulating constraints we can make immediate, short-term, and long-term changes in motor development and behavior. Often, knowing what changes to make is simply an issue of understanding the varying degrees of influence exerted by different constraints. Sometimes, even small changes in one constraint will allow a wide variety of behaviors to emerge. For example, providing an infant with support can account for both strength and posture, allowing for many different upright movements. With this in mind, read the following case studies; try to determine the most important constraints and what you can change to allow certain motor behaviors to appear. After identifying the constraints to be changed, analyze how these constraints interact with one another (if they do) and whether the interaction helps achieve the goal or works against it. Case Study A: Gender Typing of Physical Activities You are the movement educator for a class of 25 fourth graders. During your first class, you attempt to teach gymnastic skills. The boys in your class show an obvious dislike for the activities, and one exclaims, “Gymnastics is for girls!” What can you do to modify the task so that the children learn the skills you want but are not put off by gender typing? Case Study B: Older Adults AB is a 76-year-old man who recently lost his spouse of 45 years. He lives in a suburb of a large metropolitan area. Since the death of his spouse, AB has not gone on the daily strolls they used to take together. He is losing strength and flexibility, and his arthritis is flaring up. How can you reintroduce physical activity into AB’s life? Case Study C: Teaching Fundamental Motor Skills You are coaching an under-10-year-old soccer team. You’ve noticed a wide diversity of individual constraints (height, weight, skill level) among the players. How can you make practices challenging for all the players? Case Study D: Cerebral Palsy You are teaching an 11th-grade physical education class. In your class is an individual with cerebral palsy. He can walk, but he has some muscle spasticity and rigidity. You would like your class to participate in an activity or game in which everyone can be equally involved 615 without having to change or modify the rules. What kind of activity could you play? Case Study E: Middle-Aged Adults CW is a 50-year-old woman who plays doubles on a tennis team. Women from their 20s to their 60s participate in this tennis program. CW weighs about 15 pounds more than she did in her 20s, but she does walk for a half hour each day. CW’s team has one match per week and one practice per week for 8 months of the year. The top teams qualify for a series of playoffs—district, sectional, then national—in which the winning team advances to the next level. CW’s team lost in the semifinals of the sectional tournament last year and has established the goal of getting to and winning the finals of the sectional tournament for the upcoming year. CW wants to be a major contributor to the team’s success. What constraints could prove to be a challenge to CW’s success, and how would you recommend that she address those constraints? Case Study F: Age Grouping MF was born in July, just before the cutoff date for her parents to be able to delay school entry for a year. Therefore, she is the youngest girl in her elementary school class. A youth sports program is available at the school, and children participate with their grade level. This means that MF is one of the youngest players on her team every year and for every sport. MF listens to her coach and works hard in practices, but she doesn’t have the coordination required to be among the better performers in her elementary school years. If you were MF’s parent, how would you talk with her about her youth sports experiences? On which constraints would your conversation center? Case Study G: Grade-Level Expectations You are a physical education teacher about to start a new school year, and you have just received documents identifying the grade-level expectations for each of the six levels of physical education classes you teach. Grade-level expectations indicate what skills or knowledge a student should have upon completing a particular grade. What tools could you use to ensure that your students progress to the expected level by the end of the school year? A sample grade-level expectation for the fifth grade is “Demonstrate ability to follow rules, cooperate with teammates, and apply a simple strategy in a variety of sport-specific lead-up games.” How could you use the tools you identified to plan a sequence of activities over the year to achieve this expectation? Web Study Guide 616 Apply the model of constraints to examine the interaction of players in a soccer game in Lab Activity 18.2, Examining Constraints in a Context: Soccer, in the web study guide. Go to www.HumanKinetics.com/LifeSpanMotorDevelopment. 617 Summary and Synthesis The challenge confronting motor developmentalists, teachers, coaches, and parents is to tailor goals and expectations to individual capabilities and characteristics. Optimal motor development is likely related to the degree to which practice opportunities and insightful instruction are matched with individual constraints. It is complex and time consuming to individualize motor development goals and instruction in most institutional settings, but findings from motor development research at various stages of the life span point in this direction. Continued research and observation of motor development will undoubtedly yield a better understanding of developmental processes, but our task remains to find ways of using our knowledge to foster optimal motor development in every individual. Reinforcing What You Have Learned About Constraints Take a Second Look The Paralympic Games are an excellent example of a program that changes task and environmental constraints in conjunction with individual structural constraints to provide an appropriate challenge for those who would like to compete. Individuals with disabilities can be tested at the highest level by competing with and against like individuals. People with disabilities affecting their locomotion can play wheelchair basketball or sitting volleyball. Individuals with the use of only one arm can hold a bowstring with their teeth to compete in archery. An important realization for all of us is that although Paralympic athletes might have obvious and permanent disabilities, all of us have individual characteristics that can influence our ability to move in a certain way. Those characteristics might exist because we are still growing, because we are aging, because we are injured, because we lack confidence in our ability, or because we don’t have much experience with an activity. Whatever the reason, adapting environmental and task constraints to those individual constraints can allow us to move in a way that is enjoyable and healthful. Test Your Knowledge 1. How might a teacher or coach use either a model of constraints or an ecological task analysis to design a sequence of lesson or practice plans? 2. How might a teacher or community program leader use a model of constraints or an ecological task analysis to allow for the involvement of a disabled child in a group activity? 3. How might a teacher, coach, or program leader use a model of constraints or an ecological task analysis to develop individualized instruction plans? 4. Teachers are charged with providing a challenging learning task for students whose 618 physical growth and maturation status, level of coordination, and experience with a task vary. How could a teacher use a model of constraints to plan four stations of varying difficulty levels for the class? Learning Exercises The case studies in “Interacting Constraints” serve as the learning exercises for chapter 18. 619 Appendix: Skinfold, Body Mass Index, and Head Circumference Charts 620 Figure A.1 Calf plus triceps skinfolds: boys. 621 Figure A.2 Calf plus triceps skinfolds: girls. 622 Figure A.3a Body mass index for age percentiles for boys aged 2 to 20 years. Adapted from CDC 2000. 623 Figure A.3b Body mass index for age percentiles for girls aged 2 to 20 years. Adapted from CDC 2000. 624 Figure A.4a Head circumference-for-age and weight-for-length percentiles for boys, birth to 36 months. Adapted from CDC 2000. 625 Figure A.4b Head circumference-for-age and weight-for-length percentiles for girls, birth to 36 months. Adapted from CDC 2000. 626 References Abernethy, B., Wood, J.M., & Parks, S. (1999). Can the anticipatory skills of experts be learned by novices? Research Quarterly for Exercise and Sport, 70, 313–318. Acosta, V., & Carpenter, L.J. (2008). 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About the Authors
Kathleen M. Haywood, PhD, is a professor and associate dean for academic programs at
the University of Missouri at St. Louis, where she has researched life span motor
development and taught courses in motor behavior and development, sport psychology,
and biomechanics. She earned her PhD in motor behavior from the University of Illinois at
Urbana-Champaign in 1976.
Haywood is a fellow of the National Academy of Kinesiology and the Research Consortium
of the Society for Health and Physical Education (SHAPE). She is also a recipient of
SHAPE’s Mabel Lee Award. Haywood has served as president of the North American
Society for the Psychology of Sport and Physical Activity and as chairperson of the Motor
Development Academy of SHAPE.
Haywood is also the coauthor of four editions of Archery: Steps to Success and of Teaching
Archery: Steps to Success, published by Human Kinetics. She resides in Saint Charles,
Missouri, and in her free time enjoys fitness training, tennis, and dog training.
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Nancy Getchell, PhD, is an associate professor at the University of Delaware in Newark.
For nearly 30 years, Getchell has investigated developmental motor control and
coordination in children with and without disabilities. She teaches courses in motor
development, motor control and learning, research methods, and women in sport.
Getchell is a professional member of the North American Society for the Psychology of
Sport and Physical Activity, the International Society of Motor Control, and the
International Society for Behavioral Nutrition and Physical Activity. She is a research fellow
for the Research Consortium of the American Alliance for Health, Physical Education,
Recreation and Dance (AAHPERD). From 2005 to 2009, Getchell served as editor for the
Growth and Motor Development section of Research Quarterly for Exercise and Sport.
Getchell has also served as the chairperson of the AAHPERD Motor Development and
Learning Academy.
Getchell obtained her PhD from the University of Wisconsin at Madison in 1996 in
kinesiology with a specialization in motor development. In 2001, Getchell was the recipient
of the Lolas E. Halverson Young Investigators Award in motor development.
Getchell resides in Wilmington, Delaware, where she enjoys hiking, geocaching, and
bicycling.
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Cover Page
Check Out the Web Study Guide!
Life Span Motor Development
Copyright Page
Dedication
Contents
Preface
Acknowledgments
Credits
Part I: Introduction to Motor Development
Chapter 1: Fundamental Concepts
Chapter 2: Theoretical Perspectives in Motor Development
Chapter 3: Principles of Motion and Stability
Part II: Physical Growth and Aging
Chapter 4: Physical Growth, Maturation, and Aging
Chapter 5: Development and Aging of Body Systems
Part III: Development of Motor Skills Across the Life Span
Chapter 6: Early Motor Development
Chapter 7: Development of Human Locomotion
Chapter 8: Development of Ballistic Skills
Chapter 9: Development of Manipulative Skills
Part IV: Perceptual-Motor Development
Chapter 10: Sensory-Perceptual Development
Chapter 11: Perception and Action in Development
Part V: Functional Constraints to Motor Development
Chapter 12: Social and Cultural Constraints in Motor Development
Chapter 13: Psychosocial Constraints in Motor Development
Chapter 14: Knowledge as a Functional Constraint in Motor Development
Part VI: Interaction of Exercise Task and Structural Constraints
Chapter 15: Development of Cardiorespiratory Endurance
Chapter 16: Development of Strength and Flexibility
Chapter 17: Development of Body Composition
Chapter 18: Conclusion: Interactions Among Constraints
Appendix: Skinfold, Body Mass Index, and Head Circumference Charts
References
About the Authors
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21 R
Skills and Knowledge of Skills
Schmidt
Chapter
1
1 – 14

26 T
Processing Information and Making
Chapter
2
19 – 38

Decisions

28 R
Attention and Performance
Schmidt
Chapter
3
39 – 55

FEB
02 T
Attention and Performance
Schmidt
Chapter
3
55 – 61

Sensory Contributions to Performance
Schmidt
Chapter
4
63 – 73

04 R
Sensory Contributions to Performance
Schmidt
Chapter
4
74 – 86

Movement Production and Motor
Schmidt
Chapter
5
89 – 97

Programs

09 T
Movement Production and Motor
Schmidt
Chapter
5
97 – 121

Programs

11 R
Movement Production and Motor
Schmidt
Chapter
5

Programs

Individual Differences
Schmidt
Chapter
7
149 – 156

159 – 169

16 T
Lab One
Handout

Assigned Lab 1 due Tuesday, Feb 23

18 R
TEST ONE
Schmidt
Chapters 1 – 5, 7; Lab One

Online

23 T
Physical Growth and Maturation
Haywood
Chapter
1
6 – 14

Haywood
Chapter
4
58 – 75;

Development of Body Systems
Chapter
5
78 – 82

Assigned Labs due completed

25 R
Development of Body Systems
Chapter
5
82 – 97

Early Motor Development
Haywood
Chapter
6
102 – 119

MAR
02 T
Development of Human Locomotion
Haywood
Chapter
7
124 – 136

Ballistic Skills
Haywood
Chapter
8
158 – 169

04 R
Manipulative Skills
Haywood
Chapter
9
186 – 202

Sensory Development
Haywood
Chapter 10
210 – 224

09 T
Sensory Development
Haywood
Chapter 10
224 – 233

11 R
Perception and Action in Development
Haywood
Chapter 11
236 – 251

READING LIST
HP 4368
Spring 2021

16 T
Cognitive Development
Haywood
Chapter
14
290 – 302

18 R
Physical Fitness over the Life Span
Haywood
Chapter
15
306 – 324

On line Labs due – 15.1 and 16.1 due
Chapter
16 326 – 344

Read Labs 7.1 & 8.1, Online

23 T
Analysis of Running and Throwing
Haywood
Labs
Tentative

Complete Labs 7.1 & 8.1

25 R
TEST TWO
Haywood
Chapters 1, 4, 5, 6, 7, 8, 9,

10, 11, 14, 15, 16

30 T
Preparing for the Learning Experience
Schmidt
Chapter
8
175 – 193
Speed, Accuracy, and Coordination
Schmidt
Chapter
6
125-126, 130-
Assignment – Juggling Lab

139

APR
01 R
Skill Acquisition, Retention,
Schmidt
Chapter
9
212 – 225

and Transfer

06 T
Skill Acquisition, Retention,
Schmidt
Chapter
9
197-207

and Transfer

08 R
Organizing and Scheduling
Schmidt
Chapter
10 227 – 240

Practice

13 T
Organizing and Scheduling
Schmidt
Chapter
10
240 – 253

Practice

15 R
Organizing and Scheduling
Schmidt
Chapter
10

Feedback for Skill Learning
Schmidt
Chapter
11
255 – 262

20 T
Feedback for Skill Learning
Schmidt
Chapter
11
255 – 262
Juggling Lab Due

22 R
Feedback for Skill Learning
Schmidt
Chapter
11
262 – 273

27 T
Feedback for Skill Learning
Schmidt
Chapter
11
273 – 285

MAY
01 S
Final Exam Session @ 4:30 P. M.
Schmidt
Chapters
6, 8, 9, 10, &, 11

and concepts used

throughout the semester