Advances in Psychology Volume 102 Cognitive Issues in Motor Expertise

COGNITIVE ISSUES IN MOTOR EXPERTISE

ADVANCES IN PSYCHOLOGY

102 Editors:

G . E. STELMACH

P. A. VROON

NORTH-HOLLAND AMSTERDAM LONDON NEW YORK TOKYO

COGNITIVE ISSUES IN MOTOR EXPERTISE

Edited by

JANET L. STARKES Department of Kinesiology McMaster University Hamilton, Ontario, Canada

FRAN ALLARD Department of Kinesiology University of Waterloo Waterloo, Ontario, Canada

1993

NORTH-HOLLAND AMSTERDAM LONDON NEW YORK TOKYO

NORTH-HOLLAND ELSEVIER SCIENCE PUBLISHERS B.V. Sara Burgerhartstraat 25 P.O. Box 21 I , 1000 AE Amsterdam, The Netherlands

ISBN: 0 444 89302 4 1993 ELSEVIER SCIENCE PUBLISHERS B.V. All rights reserved

No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical. photocopying, recording or otherwise, without the prior written permission of the publisher, Elsevier Science Publishers B.V., Copyright & Permissions Department, P.O. Box 521, 1000 AM Amsterdam, The Netherlands. Special regulations for readers in the U.S.A. - This publication has been registered with the Copyright Clearance Center Inc. (CCC), Salem, Massachusetts. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the U.S.A. All other copyright questions, including photocopying outside of the U.S.A., should be referred to the copyright owner, Elsevier Science Publishers B.V., unless otherwise specified. No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. This book is printed on acid-free paper. Printed in The Netherlands

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Acknowledgement This book took almost two years to complete. The inspiration for the book came as a result of a roundtable on expertise hosted by Anders Ericsson and Jacqui Smith at the Max Planck Institute (Berlin) in 1989. The book was initiated in Japan, while the first editor was a foreign researcher with the Japanese government and Ibaraki University. Colleagues and close friends in Japan are the first people we are indebted to. Crossing hemispheres, preparation of the book continued and we are grateful to the many contributors, without whom the book would not have been possible. Deanna Goral was responsible for converting and formatting each of the chapters for the book and her computer expertise, perseverance, and good humour were much appreciated. Finally, David LeClair has played a special role not just as a co-author but in the preparation of the subject and author indexes. We would like to thank each of these individuals for their invaluable help in preparing the book.

Janet Starkes Hamilton, Ontario

Fran Allad Waterloo, Ontario

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Permissions We thank the following authors/publishers for allowing us to reproduce/redraw previously published figures and tables. Heldref Publications. Figure 15.1 (Keele & Ivry, copyright 1987) Davis & Geck and Ralph K. Davies Medical Center, Figure 12.1 (Alpert, Bucke & Buncke, 1975) University of South Carolina Press. Figure 12.3 (Starkes, copyright 1990) Cambridge University Press. Figure 12.5 (Allard & Starkes, copyright 1991) Psychological Review. Figure 16.1 and 16.2 (Bryan & Haner, copyright 1897) Human Kinetics Publishers. Figure 16.4 (Schmidt, copyright 1988)

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List of Contents V

Acknowledgement

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Permissions

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List of Contents

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List of Conmbutors

Part One

Preliminaries: Approaches to the study of expertise

Chapter 1

Motor experts: Opening thoughts Janet L. Starkes

Chapter 2

Chapter 3

3

Cognition, expertise, and motor performance Fran Allard

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The role of three dimensional analysis in the assessment of motor expertise Heather Camahan

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Part Two

Domains

Chapter 4

Determinants of video game performance Donna M. Baba

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Analyzing diagnostic expertise of competitive swimming coaches Rebecca Rutt Leas and Micheline T.H.Chi

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Declarative knowledge in skilled motor performance: byproduct or constituent? Fran Allard, Janice Deakin, Shane Parker, and Wendy Rodgers

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Chapter 5

Chapter 6

Chapter 7

The relationship between expertise and visual information processing in sport Werner Helsen and J.M. Pauwels

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Chapter 8

Chapter 9

Chapter 10

Chapter 11

Cognitive Issues in Motor Expertise

The perceptual side of action: Decisionmaking in sport Craig J. Chamberlain and Alan J. Coelho

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Knowledge representation and decisionmaking in sport Sue L. McPherson

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Neuropsychological analyses of surgical skill Arthur L. Schueneman and Jack Pickleman

189

The skill of speech production Kevin G. Munhall

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Part Three

Acquisition and Developmental Aspects

Chapter 12

A stitch in time: Cognitive issues in microsurgery Janet L. Starkes, Irene Payk. Peter Jennen, and David LeClair

225

Motor expertise and aging: The relevance of lifestyle to balance Michael J. Stones, Blair Hong, and Albert Kozma

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The development of expertise in youth sport Karen E. French and Michael E. Nevett

255

Chapter 13

Chapter 14

Part Four

Theoretical considerations and evaluations of the approach

Chapter 15

A modular approach to individual differences in skill and coordination Steven K. Jones

213

Three legacies of Bryan and Harter: Automaticity, variability and change in skilled performance Timothy D. Lee and Stephan P. Swinnen

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Chapter 16

List of Contents

Chapter 17

Part Five

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Strategies for improving understanding of motor expertise [or mistakes we have made and things we have learned!!] Bruce Abemethy, Katherine T. Thomas, and Jerry T. Thomas

317

Editors’ Epilogue: Where are we now?

359

Author Index

363

Subject Index

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List of Contributors BRUCE ABERNETHY Department of Human Movement Studies, University of Queensland, St. Lucia, Qld 4072, Australia FRANALLARD Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 DONNA M. BABA The Usability Group Inc., Willowdale, Ontario, Canada, M2J 4V8 HEATHERCARNAHAN Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada. N2L 3G1 CRAIG J. CHAMBERLAIN University College of the Fraser Valley, Abbotsford, British Columbia, Canada, V2S 4N2 MICHELENE T.H. CHI 821 Learning Research and Development Centre, 3939 O’Hara Street, Pittsburgh, Pennsylvania, 15260 ALAN J. COELHO Department of Physical Education, Eastern Washington University, Cheney, Washington, 99004-2499 JANICE DEAKIN Department of Physical and Health Education, Queen’s University, Kingston, Ontario, Canada, K7L 3N6 KAREN E. FRENCH Department of Physical Education, University of South Carolina, Columbia, South Carolina, 29208 WERNER HELSEN Leuven University, Institute for Physical Education, Motor Learning Lab, Tervuunevest 101, 3030 Leuven, Belgium BLAIR HONG Gerontology Centre and Department of Psychology, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada, AlB 3x9

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Cognitive Issues in Motor Expertise

PETER JENNEN Faculty of Medicine, University of Limburg, Maastricht, The Netherlands STEVEN K. JONES Department of Psychology, University of Oregon, Eugene, Oregon, 97403-1227 ALBERT KOZMA Gerontology Centre and Department of Psychology, Memorial University of Newfoundland, St. John’s, Newfoundland. Canada, A1B 3x9 DAVID LeCLAIR Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada, L8S 4K1 TIMOTHY D. LEE Department of Kinesiology, McMaster University, Hamilton, Ontario, Canada, L8S 4K1 SUE L. McPHERSON Department of Health, Physical Education and Recreation, Western Carolina University, Cullowhee, North Carolina, 28723 KEVIN G. MUNHALL Department of Psychology, Queen’s University, Kingston, Ontario, Canada, KIN 6N5 MICHAEL E. NEVETT Department of Physical Education, University of South Carolina, Columbia, South Carolina, 29208 SHANE PARKER Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 J.M. PAUWELS Leuven University, Institute for Physical Education, Motor Learning Lab, Tervuursevest 101, 3030 Leuven, Belgium IRENE PAY K Microsurgery Lab, McMaster University Medical Centre, Hamilton, Ontario, Canada, L8S 4K1 JACK PICKLEMAN Loyola University Medical Centre, Loyola University Chicago, Maywood, Illinois, 60153 WENDY RODGERS Department of Kinesiology, University of Windsor, Windsor, Ontario, Canada, N9B 3P4

List of Contributors

REBECCARUTTLEAS 821 Learning Research and Development Centre, 3939 O’Hara Street, Pittsburgh, Pennsylvania, 15260 ARTHUR L. SCHUENEMAN Loyola University Medical Center, Loyola University Chicago, Maywood, Illinois, 60153 JANET L. STARKES Department of Kinesiology, McMaster University, Hamilton, Ontario,Canada, L8S 4K1 MICHAEL J. STONES Gerontology Centre and Department of Psychology, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada, AlB 3 x 9 STEPHAN P. SWINNEN Department of Kinanthropology, Catholic University of Leuven, 3001 Heverlee, Belgium JERRY T. THOMAS Department of Exercise Science and Physical Education, Arizona State University, Tempe, Arizona, 85287 KATHERINE T. THOMAS Department of Exercise Science and Physical Education, Arizona State University, Tempe, Arizona, 85287

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Part 1 Preliminaries: Approaches to the study of motor expertise

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COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Stakes and F. Allard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 1 MOTOR EXPERTS: OPENING THOUGHTS JANET L. STARKES Department of Kinesiology, McMaster University Hamilton, Ontario, L8S 4Kl This volume contains a series of papers which focus on investigations of expert motor performance. The situations considered range from swimmers to surgeons, with all authors attempting to discover what differentiates expert from novice individuals. The experimental methods used also cover a wide range, from conventional laboratory studies, to protocol analysis, to a re-analysis of data that are nearly 100 years old.

In these opening remarks, I will point out some issues that should be kept in mind while reading the following papers. Many of these issues arise from the application of an approach developed to study the nature of expertise in cognitive tasks to motor tasks. The first such issue is what constitutes a motor expert? What is a motor expert? Intuitively it seems simple to describe a motor expert - someone who's very good at doing something motoric. But how good does someone have to be? How does one measure "good"? How often do they have to be good and how motoric must the task be? Defining what constitutes motor expertise depends very much on one's world view. Some authors (Sloboda, 1991) believe that each of us exhibits some degree of expertise in certain domains, perhaps music, sports, or one's own language. In this view people can achieve competence in a task simply because of its function in everyday life. An analogy might be that many people are expert drivers because of the number of hours they routinely spend in traffic or commuting. If one adopts this notion of expertise the exclusivity of the skill may be subject to cultural interpretation. For example, today we rarely think of one's driving skill as an unusual or exclusive ability. On the other hand if one is a skilled equestrian, we think of that person as unique, perhaps worthy of study as an expert. At the turn of the century, when the primary mode of transportation was horse, one was hardly viewed as an expert if they could ride well. The real expert was someone who could adeptly manage one of the new fangled horseless carriages. So to some extent who we class as an expert depends on the relative exclusivity of the skill. Once a skill becomes part of the repertoire of normal people most cease to view it as a task worthy of study.

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In most cases "exclusivity" seems to be a hallmark of the expertise approach. Salthouse (1991) feels that only those individuals in the highest percentiles of the normal dismbution of skill should be termed "experts". If we think of a normal distribution of skill, these would be individuals who exhibit performance 2-3 standard deviations above the norm. He also suggests that the reason experts seem so impressive is that they have learned to circumvent the normal limits of human information processing. Thus experts often seem to "have all the time in the world' or are "able to see two plays ahead in tasks that involve complex decision making. What then are the normal processing limitations that are inherent in motor skills, and how are they different from strictly cognitive tasks? Again Salthouse (1991) suggests that different tasks present different kinds of limitations for the performer. In his analysis, for chess the limitations may be not knowing what to do and not knowing what to expect from one's opponent. For a musician the limitations are: not knowing what to expect in musical sequences, insensitivity to sensory/ perceptual discriminations such as pitch or timing, and lack of proficiency in producing pitch, timing, intensity, etc. In sports, the limitations inherent might be: not knowing what to expect from one's own or an opponent's actions, insensitivity to critical sensory/ perceptual information such as trajectory of ball flight, and lack of proficiency in performing appropriate actions. The main difference between motor and strictly cognitive skills lies not in fact that information must be anticipated and discriminated but that the most appropriate action must be selected and performed. In general there are two basic approaches to the study of expertise, one we will call the bottom-up approach and one the top-down approach (Salthouse. 1991). T h e bottom-up approach involves three basic steps. First a domain is studied and detailed analyses are conducted to determine what processes are associated with expertise, and what mechanisms are responsible for the processes. Case studies of experts in the domain are usually performed or canied out and hypotheses formed. Next quantitative methods are. used and data are collected on moderate sized groups of experts and novices. The goals in this stage are usually to assess characteristics of experts and determine whether they are consistent between subjects, along the competence continuum, and hold for different procedures and paradigms. The third stage in the bottom-up approach is to determine whether there are common principles underlying expertise across domains. This is a test of generalizability of the model. Unfortunately, while stage three would be the ultimate in theory building, most would say that expert-novice research has been stuck at stage two for some time. There has been very little cross-domain research or work on the generalizability of models by those using the bottom-up approach. In the top-down approach one begins by speculating about what characteristics might be critical to experts in general. The principles are then systematically and empirically investigated. One general characteristic that has been investigated is the ability of experts to circumvent the normal limits of human information processing (Salthouse, 1991). In transcription typing the work of both Gentner (1988) and Salthouse (1984) suggest that skilled typists circumvent limits by taking advantage of the fact that relevant (to be typed) information is constantly available. Thus they can process subsequent stimuli before all of the performance is complete on earlier stimuli. As a result the better the typist the farther ahead in the text they look while typing. If typing were

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a simple reaction time task, typists would exhibit a maximum typing rate of approximately 22 words per minute. Since skilled typists often exceed 75 words per minute they have developed systems for overlapping or parallel processing of information. The task is no longer a serial, discrete task but a dynamic and continuous one that can circumvent the limits imposed by nonparallel processing. While space precludes a more complete discussion of principles in topdown analysis (see Salthouse, 1991) the notion of circumvention of limits could be potentially very valuable in the study of motor expertise. In most cases where speeded motor responses are required (musical performance, human speech, video game controller manipulation) reaction and movement time do not appear to constrain skilled performance. This is one principle that may prove generalizable to many domains. Another controversy that surrounds expert performance is whether task specific sensory information is handled differently by someone once they become skilled. While a substantial amount of work has examined these issues in traditional motor learning studies using learning paradigms, there are little data on established motor experts using real world skills. To date there are three prominent theories, each of which address this question, each of which make very different predictions, and for each of which there is supportive data (Robertson, Collins, Elliott & Starkes, 1992). Pew (1966) was the first to suggest that as one becomes expert in a motor skill there is a reduced need for the continuous monitoring of sensory infomation. That is subjects switch from a control system using sensory feedback and intermittent corrections (closed-loop system) to one in which movement becomes primarily preprogrammed and does not use feedback (open-loop system). A second theory predicts that as one becomes expen the importance of visual feedback is lessened and one begins to rely more on proprioceptive feedback. Since the original theory was proposed (Fleishman & Rich, 1963) others have modified it to suggest that while kinesthetic information becomes very important, motor programs may also differentiate skill levels (Fischman & Schneider, 1985). Finally the specificity of learning theory suggests that learning is specific to the feedback condition in which the skill was acquired. The more practice one has in a skill using a particular source of feedback, the more important that source of feedback becomes (Proteau, Marteniuk, Girouard, & Dugas, 1987). While each of these theories have empirical support, each also has detractors. The advantage of examining these theories using real world experts and skills is that subjects have undergone far more learning and practice n i a l s than one could ever simulate. in laboratory settings. In spite of this most of the current research continues to examine learning on conmved laboratory tasks where amount of learning and practice are necessarily constrained. This is one area where traditional motor learning and expert - novice research could benefit by amalgamating paradigms. Issues unique to the empirical assessment of movement 1. Skills that are assessed on the basis of motor output run the whole gamut from human speech, to transcription typing, to playing the piano, to ballet, to sports, to surgery. How then does one ever begin to get a handle on types of movement output or relative importance of movement output to overall task performance? The most common distinction in the motor

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literature and one still very much of value is the difference between "open" and "closed skills (Poulton, 1957). Closed skills are distinguished by performance in a consistent, usually stationary environment, and open skills are performed in a moving, dynamic environment. Thus skills such as typing, playing a piano, or performing a figure skating routine would be closed skills, and ones like speech, controlling a joystick in a video game, and sports such as boxing or soccer are open skills. In most open sports part of the dynamic nature of the environment comes from the opponent being present during the competition (judo, volleyball). In closed skills, competitors typically take turns competing or performing (gymnastics, ballet). A third distinction in open and closed skills is the role of motor patterns. For a closed skill the motor pattern & the skill, and it's critical that the performer reliably reproduce the standardized pattern. For open skills it is the outcome of the movement that must be effectively produced and a standardized motor pattern is rarely of help. Motor skills need to be further divided depending on their motor requirements. Some skills may require extreme accuracy but deemphasize speed (surgery, darts), others require speed (racquet sports, swimming), others require strength (shot put, pole vault), still others require endurance (triathalon, microsurgery). A task analysis of motor output may indicate various tradeoffs in the underlying requirements of a particular skill. A skill taxonomy of motor requirements is available elsewhere (Allard & Starkes, 1991). Before embarking on any expennovice analysis however, it is important to understand the underlying nature of the skill at hand. In the typical expert -novice paradigm, one or more experts in a particular domain are compared with subjects of lesser skill. In research to date however, the range of skill assessed has been as diverse as the number of domains examined. In one study "experts" might be movement professionals with many years experience, in another varsity level athletes, and in a third developmental study - 12 year olds ranked at a national level. Across studies one person's "expert" is another's "skilled adolescent". Probably the term "expert" is only appropriate for individuals who have spent a significant part of their life in preparation and training within their domain and who perform consistently at a very high level.

2.

3. Another controversy in the assessment of expertise is how much of expert behavior can be explained by experience and training versus how much by individual abilities the subject brings to the task. As Posner (1988) points out, most of us believe that there are important underlying differences among people in how readily they could become experts. Some people also seem to have more potential to develop abilities in one domain, say music, over another. More recent efforts by cognitive psychologists have been directed at measuring the cognitive processes underlying various domains. Some researchers (Deary & Mitchell, 1989; Adam & Wilberg, 1992) feel that high-speed visual processing abilities play a significant role in "open" skills and suggest that performance is related to information processing capacity and rate. Still others suggest that different combinations of abilities may play a critical role in skill at different stages of acquisition and proficiency (Ackerman, 1988; Fleishman, 1966; 1972).

Elsewhere, I have argued that generations of expert-novice research have gained little

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from the assessment of underlying component abilities (Starkes & Deakin, 1984) yet I do believe that a certain proportion of variance in expertise must be accounted for in some way by ability constraints. If different combinations or constellations of abilities become important at different stages of skill then perhaps this has hindered the search for isolated underlying psychomotor traits that would help explain performance. Or perhaps given the amount of time and training it takes to become expert, the problem of producing one may not be so much in finding someone with the underlying prerequisite abilities as finding someone with sufficient motivation to persevere. Another problem in discussing expertise is that within any one expert, skill and experience are inherently confounded. It is very rare to find individuals who consistently excel but have little experience. It is far more common to find people that have significant amounts of experience in a domain but are not experts. If one were able to separate the contributions of each then the relative weighting of ability vs. training could be assessed. 4.

One experimental approach to this nature - nurture question has to date not been used in studies of motor expertise. The approach involves the study of laboratory engineered expertise. In motor skills it would be very revealing to be able to plot the rise of someone to expertise in a longitudinal study. Ericsson and Hanis (1989) (cited in Ericsson & Smith, 1991) have done this in the domain of chess. They were able after 50 hours of practice to train a subject with no chess-playing experience to recall chess positions at a level of accuracy approaching chess masters. Likewise Chase and Ericsson (1981) trained subjects on digit span tasks to exhibit skilled memory processes. Others have decomposed and trained performance in mental calculation (Chamess & Campbell, 1988).Plotting improvements in motor expertise from novice to expert would help us understand learning plateaus, motivational changes, the role of mentors, competitive performance, etc. better. In the real world of skill the ideal situation would be to follow a group of average novices longitudinally,examine changes in performance and determine who of the group emerges as expert and why. 5.

As research has proliferated several authors have attempted to delineate characteristics of those we term experts. Simon & Chase (1973) were the first to observe that over 10 years of preparation and training was required to compete in chess at the international level. Bloom's (1985) insightful study of experts in music, sports and science confirmed that individuals needed a decade of training to excel in any of these fields. Both Bloom and more recently Ericsson and Crutcher (1990) have shown that people who perform internationally probably became interested in their skill domain before age 6 and spent the majority of their adolescence and young adulthood in training. 6. Consistency of performance is critical in assessing whether someone is an expert. Not everyone who gets a "hole-in-one" is an expert golfer. Likewise those who win at games of chance, complete a single major art work or piece of literature don't exhibit the stable performance characteristics of an expert. Expertise is characterized by stable, measurable performance over long periods of time (Ericsson & Smith, 1991). Interestingly many of the

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cognitive tasks assessed in the literature: chess, bridge, Go, etc. have scoring systems where one can assess both performance level relative to others and changes over time. This may or may not be the case in motor skills. For example gymnastics has a well delineated point system for each skill and an international ranking system. Assessing whether an individual surgeon or Super Mario player is an "expert" poses somewhat more of a problem. 7. In many skills like surgery or microsurgery whether one is considered expert is often influenced by one's perceived abilities. This makes the judgement somewhat more social in nature. For research purposes however, subjects should not be designated as experts on the basis of professional credentials, reputation, peer evaluation, or some correlate of competence, but on the basis of actual performance in the task. Patel and G m n (1991) suggest that standardized categories along the competence continuum (layperson, beginner, novice, intermediate, subexpert and expert) would be useful. An alternative category system is provided by Dreyfus and Dreyfus (1986) who see experts proceeding on the continuum in the following sequence: novice, advanced beginner, competence, proficiency, and finally expertise. Nevertheless as Salthouse (1991) notes, establishing boundaries between each subgroup is just as difficult and artificial a distinction as dichotomizing experts and novices.

8. As Ericsson and Smith (1991) note, two features distinguish the expertise approach within what has previously been called a bottom-up analysis. First, it is necessary to design a series of representative tasks that capture the superior performance seen in the domain and elicit it under laboratory conditions. Second it is necessary to discover the mediating mechanisms of superior performance and analyze the types of learning and adaptation of the mechanisms that occur both in real world performance and the laboratory tasks. Within the expertise approach and particularly with motor output the issue of measurement becomes critical. In motor behaviour research there has been an explosion of measurement technology in the last ten years. We now have infrared, auditory, and optical systems for plotting and analyzing movement in three dimensions. From eye movement systems to motion analyzers the complexity of measurement has mushroomed. This has complicated the issue of what are the best measures of expert performance in the lab and in the real world skill. An interesting and innovative study by Ripoll (1991) illustrates the value of this technology in analyzing expert performance. Using a corneal reflection eye movement analysis system he was able to demonstrate that in expert table tennis players the kind of visual information processing engaged in while performing hitting drills is very different from what occurs during actual competition. So we are beginning to understand that the underlying nature of performance even within expert subjects may vary depending on task uncertainty or demands of the specific situation (competition, importance of performance, etc.) Camahan (this volume) considers when three dimensional systems are of benefit to movement analyses and inherent drawbacks in the various systems. A more basic question that must be the focus of every expert-novice comparison is what propomon of the expert's skill is tapped by any one lab task and what proportion of the expert-novice difference can be reliably

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explained by the task. For many reasons the study of movement & different from the study of more traditional cognitive skills. Let's consider for a moment the difference between the recall of digits or letters and the recall of a series of ballet steps. In a digit recall task the subject is shown a list of digits for a limited period and immediately, or after a short time either with or without interference, is asked to recall the letters in sequence. Recall requires very little time, no special planning on the part of the articulators to say each digit, and one digit out of sequence may or may not influence subsequent recall. Serial position effects are common, and delay interval effects performance very little, unless interference has occurred. 9.

Now let's consider a dancer recalling a series of ballet steps. In a lab we might show the subject a series of structured or random ballet steps performed by another dancer on video, or simply list the steps on a stimulus card. Like the digit task the steps could require immediate recall, a short delay or follow interference. Recall consists of performing the steps in sequence and could be scored by a professional or videotaped for later analysis. But this is where the similarity ends. As the dancer sees the stimuli there is a translation process that must occur from labelling and understanding the steps to deciding how one's own body is going to reproduce the steps for recall. In the real world situation dancers use a system called "marking" for doing this. As they watch the steps they use their hands to "mark" where their feet will position for recall. Marking is usually done simultaneously with visual presentation of the stimuli and often in the delay interval before recall - a kind of motor equivalent of verbal rehearsal. Recall differs from digit recall in several ways. First it simply takes longer. Taking longer means that for each sequential step the delay interval has inherently also been manipulated. Because each step follows on another the previous step may serve as interference, and in fact if it was incorrect, may leave the body in a position totally incompatible with performing the next step correctly (on the wrong foot, or with incompatible arm position). As such, an error in recall may have far more serious effects on subsequent items, than would be the case in digit recall. While the serial nature of motor performance may affect recall differently, Salthouse's (1991) observation that expertise may circumvent the limits of seriality may be important. To date we have very little information on how serial production systems of motor behaviour change with expertise.

Another aspect that is difficult to assess in motor recall is the relative size of elements in a sequence. While it is true that digits may be made up of lines, angles, etc. it is fairly well accepted that a string of 8 digits has eight elements. But what constitutes a movement element? A dance step may involve foot, arm,hand and head placements, body elevation, and attitude. When a dancer recalls one step is it necessarily one element or an "inherent chunk" already comprised of several inseparable elements. All of these issues make the assessment of motor recall difficult but far from uninteresting. 10.

From a neuropsychological perspective we also know that motor information is indeed

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handled differently. The classic example of this is the case of Henry M. (Milner, Corkin, & Teuber, 1968), who as a young man underwent bilateral mesial temporal lobe ablations to treat epilepsy. Upon recovery from surgery H.M. suffered permanent memory deficit. He continued to show severe anterograde amnesia and was unable to learn new names or his address. While he could readily remember events from childhood, events prior to surgery were also lost. Further assessment of cognitive and learning skills revealed that H.M. was able to learn a variety of motor tasks. He could retain skill in star-mirror drawing and pursuit rotor, even though he could not remember ever attempting the tasks. In one study of the visual maze he was tested 2 years after training and showed 75% retention in spite of the fact he had no knowledge of ever being tested previously (Milner et al., 1968) In real life H.M. was able to work in a rehabilitation centre, mounting cigarette lighters on cardboard frames, a task he learned to do quite skilfully (Blakemore, 1977). Gardner (1975) has reported similar evidence from a patient with severe amnesic symptoms following a closed head injury. Although unable to recall the teaching sessions, the patient was able to learn and recall piano melodies. The study of amnesic disorders clearly shows that the acquisition and retention of procedural skill is very different from that of verbal on perceptual information. 11. In studies that involve children who are "motor experts" it is necessary to consider several inherent issues. In tasks that are primarily cognitive, examining children's acquisition and representation of knowledge has contributed greatly to our understanding of the development of expertise. An example of this was the pioneering work of Chi, who was able to develop a network representation of a four year old "dinosaur expert" (Chi & Koeske, 1983). Translating these questions to children who become very good at computer games, or tennis for example becomes more difficult.

First. regardless of how good the child becomes at skills that involve gross movements, it is likely that they could still be surpassed by an adult of even moderate ability, simply because of size and power limitations. With child prodigies in games such as chess this is not the case. Second, problems exist in assessing motor skill at various stages of development. A tennis example illustrates this. Take the case of a twelve year old nationally ranked player who is very skilled, consistent in performance, and uses a two handed backhand. The backhand is effective, strong and most importantly shows very low variability. A year later as the player develops she switches to a one hand backhand because with muscular and further neural maturation she now has the strength and control to master it. What was once a high skill, low variability stroke now becomes more efficient in the long run, but temporarily far more variable. Developmentally, children are likely to reach several performance plateaus, characterized by effective performance with low variability. In moving to the next skill level there may be. disruptions in performance and high variability. As long as variability is induced by constant physical changes it is difficult to assess "expertise" levels in motor skills. 12.

A final issue in the study of motor expertise is whether protocol analysis is an appropriate

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methodology for the elaboration of procedural skills. Protocol analysis uses think - aloud reports of an individual's introspections in a systematic way to catalogue a subject's current state of knowledge about a given problem and how the individual progresses to new knowledge by retrieving new inferences or using formal operators specific to the domain of knowledge (Reitman Olson & Biolsi, 1991). Protocol analysis and its methodological considerations and limitations are discussed elsewhere in great detail (Ericsson & Simon, 1984). While protocol analysis has been very useful in eliciting information from experts in such areas as physics problem solving, chess and bridge, it may not be entirely appropriate for use in motor skills. As Ericsson and Simon (1984) indicate, protocol analyses may be performed both concurrent with the task or retrospectively. Concurrent analysis while it is more powerful may be very difficult. and in some cases not be appropriate in skills where the subject is attempting to do the motor skill at the same time. As well, in concurrent analysis the assumption is essentially that the verbal output reflects what is operating in short term memory. In skilled motor performance however, automatic processes may bypass STM and not be accessible for report. A second issue with protocol analysis has to do with the attentional controls necessary to show that their elicitation had no influence on performance. A standard control procedure when using protocol analysis has been to have the subject perform both with and without giving

think - aloud protocols. Obviously having to think aloud should have no detrimental influence on performance. It is difficult to conceive of a motor skill where having to think aloud would not affect performance, and indeed few motor skill researchers have implemented this control procedure. A third criticism of the approach comes from researchers who produce expert systems. Ever since the advent of artificial intelligence, researchers have tried to produce "artificial experts" by having the computer follow rules used by masters in a piuticular domain. While computers have become far faster and more accurate than people in applying rules, master level performance has remained out of reach (Dreyfus & Dreyfus. 1986). Two problems arise in the production of expert systems from protocols.

[An expert's] knowledge is currently acquired in a very painstaking way; individual computer scientists work with individual experts to explicate the expert's heuristics - to mine those jewels of knowledge out of their heads one by one ... the problem of knowledge acquisition is the critical bottleneck in artificial intelligence. (Feigenbaum & McCorduck, 1983, pp. 7980.)

So while the first problem may be accessing the vast amounts of knowledge held by the expert, the second means that protocol analysis may have serious shortcomings. [A]n expert's knowledge is often ill-specified or incomplete because the expert himself doesn't always know exactly what it is

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he knows about his domain. (Feigenbaum & McCorduck, 1983, p.85.)

New insights into the modelling of expert performance Symbolic Connectionism Expertise research has evolved through two generations of theory building. The first view of the expert was as one who was particularly skilled at heuristic search. The early insightful works of Newell and Simon (1972) conceived of problem solving as search, and suggested that a relatively small number of heuristic methods for serial search could be applied across a number of domains. Very quickly this hypothesis was refuted by work in chess which showed the importance of large amounts of domain specific knowledge (Chase & Simon, 1973; de Groot. 1965). And hot on the heels of the chess work came studies in domains as varied as physics, Go, diagnosis of X-rays, and basketball. Complex problem solving in such real world tasks brought a certain ecological validity to research, and an excitement about the prospects of engineering real world expertise. This second generation of work on expertise, characterized by domain specific research, seemed to show that domain specific declarative knowledge was the way to go - and the more the better. But declarative memory wasn’t the only answer. Expertise depended on learning how to do something well and so procedural learning became of interest. Previous notions of heuristic search seemed to best describe how novices not experts functioned. As Holyoak (1991) suggests since most of the second generation theories were based on serial production systems (Newell, 1973) there was the first real opportunity for interdisciplinary studies of expertise both in cognitive science and artificial intelligence. In artificial intelligence production systems became the basis of the first expert systems. In cognitive science production systems became the core of Anderson’s ACT theory (Anderson, 1976, 1983, 1987), essentially a theory of knowledge compilation. From knowledge compilation the picture emerged of novices who first solve problems by weak methods (usually working backwards from the goal) and who gradually with more successful solutions develop automatic generation of specialized productions (allowing forward solutions from problem state to the goal). The eventual result is that the expert is able to reach solutions both more quickly and more efficiently. The move toward a third generation of theory building has been fuelled by both the popularity of connectionism or neural networks that has infused most other areas of cognitive science, and the problems that current theories have in accounting for many findings with regard to expert performance. Holyoak (1991) lists all of the major consistencies in expert performance demonstrated by second generation studies as follows: (1) experts perform complex tasks in their domains much more accurately than do novices; (2) experts solve problems in their domains with greater ease than do novices; (3) expertise develops from knowledge initially acquired by weak methods, such as means-end analysis; (4) expertise is based on the automatic evocation of actions by

Motor Expens

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conditions; (5) experts have superior memory for information related to their domains; (6) experts are better at perceiving patterns among task-related cues; (7) expert problem-solvers search forward from given information rather than backward from goals; (8) one's degree of expertise increases steadily with practice; (9) learning requires specific goals and clear feedback; (10) expertise is highly domain-specific; (1 1) teaching expert rules results in expertise; (12) performances of experts can be predicted accurately from knowledge of the rules they claim to use. However, he is then able to point to empirical inconsistencies with regard to each of these findings, and is disappointed with the lack of universality in any of these correlates of expertise. A third generation expertise approach may begin to answer some of the inconsistencies faced by the second generation of domain specific theories. Holyoak (1991) has termed this paradigm shift "symbolic connectionism". Though these terms may at first appear contradictory, he is of the opinion that while there is strength in the connectionist or neural network modelling of knowledge, the demise of the importance of symbols (inherent in second generation theories) is premature at this point in time. The connectionist viewpoint is described well by Tienson (1990) and generally involves a neural network of processing units or nodes that are connected by links. Each node is connected to many others, to and from which signals may be sent. A given node may receive or send signals to just a few other nodes or up to several dozen other nodes. The input to a node is similar to a simple electric message, or synaptic transmission in that it is essentially "on" or "off'. The signal may vary in strength however, and all of the input directed at a particular node determines its state of activation. Finally, sometimes a network allows for signals that may either excite or inhibit activation. Between nodes the links are similar to synaptic connections and thus have some degree of resistance. Consequently, the strength of a signal to node b may be both a function of the strength of the signal from a, but also the strength of the connection between the two nodes. The strength of connections between nodes is termed "weight". A higher weight means that a stronger signal was received along a connection with less resistance. While the properties of nodes are considered fixed, the weights may be determined by experience. Thus connectionist systems are capable of learning and becoming expert is essentially "getting your weights changed" Tienson, 1990, p.387). A primary difference between information processing architecture and connectionist architecture is that with connectionism there is no central processing or executive unit. All connections are local, so that each node knows only what it knows in relation to all of the units to which it is connected. As such no one node in the system knows what the system as a whole knows. Unlike the notion of parallel processing there are not parallel independent processes that are programmed or hardwired, there is simply simultaneous local processing throughout the whole system. While space prevents discussion of characteristics of such a system that allow such phenomena as feed forward processing, dismbuted representations and back propagation, learning occurs within the system. In conventional architecture, information is stored within

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memory systems. In a connectionist system information is actively represented by a pattern of activation. When that pattern is not in use it is nowhere in the system, yet information remains "in" the system'' in the sense that the weights between connections have been changed. Memories are not stored, they are recreated as symbols in active representation again and again. Connectionist theories will continue to have a major impact on how cognitive scientists view processing and learning, and although currently neural networks cannot answer all of the inconsistencies in expert-novice findings they may offer new ways of looking at some of the problems. Just how our view may change is illustrated in the following example. In a later chapter of this volume I discuss the associative learning problems encountered when macrosurgeons begin to learn microsurgical techniques. Essentially the visual cues that used to elicit a series of macromovements (i.e., rotating of wrists to perform sutures), must under the microscope, elicit a whole new set of micromovements. Nevertheless, the old condition-action links must be preserved when the surgeon returns to conditions and performs surgery at the macro level. Holyoak (1991, p.325-326) points out that a connectionist-style solution to this problem might preserve the existing excitatory connections from the visual cues to the required macromovements and add new inhibitory links between the two different sets of visual-to-motor connections. The context then would allow the surgeon to "flip-a-switch'' to choose which set of connections was appropriate for that task. This would allow the new skill (micromovements) to build on the old (by using preexisting connections among condition cues) while minimizing the amount of interference between the two. While symbolic connectionism is only one possible direction for the development of expert-novice paradigms, it certainly merits consideration in future. Many of these issues are addressed in the following chapters, in which the basic method of comparing expert to novice performers is applied across a wide range of skills and situations. We do not promise solutions, but we certainly hope to hold your interest.

References Ackerman, P.L. (1988). Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. Journal of Experimental Psychology: General, 11 7 , 288-318. Adam J.J., & Wilberg, R.B. (1992). Individual differences in visual information processing rate and the prediction of performances in team sports: a preliminary investigation. Journal of Sports Sciences, 10, 261-213. Allard. F., & Starkes, J.L. (1991). Motor skill experts in sports, dance, and other domains. In K.A. Ericsson & J. Smith (Eds.). Toward a general rheory of expertise (pp.123-152). Cambridge: Cambridge University Press. Anderson, J.R. (1976). Language, memory, and rhoughr. Hillsdale, N.J.: Erlbaum. Anderson, J.R. (1983). The archirecrure of cognition. Cambridge, MA: Harvard University Press.

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Anderson, J.R. (1987). Skill acquisition: compilation of weak-method problem solutions. Psychological Review, 94, 192-210. Blakernore, C. (1977). Mechanics of the mind (pp.93-98). Cambridge: Cambridge University Press. Bloom, B.S. (1985). Developing talent in young people. New York Ballantine Books. Chamess, N., & Campbell, J.I.D. (1988). Acquiring skill at mental calculation in adulthood: A Experimental Psychology: General, 117, 115-129. task decomposition. Journal of Chase, W.G., & Ericsson. K.A. (1981). Skilled memory. In J.R. Anderson (Ed.),Cognitive skills (pp.141-189). Hillsdale, N.J.: Erlbaurn. and their acquisition Chase, W.G., & Simon, H.A. (1973). The mind's eye in chess. In W.G. Chase (Ed.) Visual information processing (pp.215-281). New York: Academic Press. Chi, M.T.H., & Koeske, R.D. (1983). Network representation of a child's dinosaur knowledge. Developmental Psychology, 19, 29-39. de Groot, A. (1978). Thought and choice in chess. The Hague: Mouton (Original work published in 1946) D e w , I.J., & Mitchell, H. (1989). Inspection time and high-speed ball games. Perception, 18, 789-792. Dreyfus, H., & Dreyfus, S. (1986). Why skills cannot be represented by rules. In N.E. Sharkey (Ed.), Advances in Cognitive Science 1 (pp.315-335). Chichester: Ellis Honvood Ltd. Ericsson, K.A., & Crutcher, R.J. (1990). The nature of exceptional performance, In P.B. Baltes, D.L. Featheman, & R.M. Lerner (Eds.), Life-span development and behavior Vol.10 (pp.187-217), Hillsdale, N.J.: Erlbaum. Ericsson, K.A., & Simon, H. A. (1984). Protocol analysis: verbal reports as data. Cambridge, MA: Bradford Boods / MIT Press. Ericsson, K.A., & Smith, J. (1991). Prospects and limits of the empirical study of expertise: and introduction. In K.A. Ericsson & J. Smith (Eds.) Toward a general theory of eqmiw (pp. 1-38). Cambridge: Cambridge University Press. Feigenbaum, E., & McCorduck, P. (1983). The fifih generation: artificial intelligence and Japan's computer challenge to the world. Reading, MA: Addison-Wesley. Fischman, M.G., & Schneider T. (1985). Skill level, vision and proprioception in simple onehand catching. Journal of Motor Behaviour, 17, 219-229. Fleishman, E.A. (1966). Human abilities and the acquisition of skill. In B.A. Bilodeau (Ed.), Acquisition of skill.(pp.147-167). New York Academic Press. Fleishman, E.A. (1972). Structure and measurement of psychomotor abilities. In F. Urbach (Ed.). The contribution of behavioral science to instructional technology. V01.3. The psychomotor domain of learning. Washington, D.C.: Gryphan House. Fleishman, E.A., & Rich, S. (1963). Role of kinesthetic and spatial-visual abilities in perceptual motor leaming. Journal of Experimental Psychology, 66(5), 301-3 12. Gardner, H. (1975). The shattered mind. New York: Alfred Knopf. Gentner, D.R. (1988). Expertise in typewriting. In M.T.H. Chi, R. Glaser & M.J. Farr (Eds.). The nature of expertise (pp. 1-21). Hillsdale, N.J.: Erlbaum. Holyoak, K. (1991). Symbolic connectionism: toward third-generation theories of expertise. In K.A.Ericsson & J. Smith (Eds.) Toward a general theory of expertise (pp.301-336).

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Cambridge: Cambridge University Press. Milner, B. Corkin, S., & Teuber, H.L. (1968). Funher analysis of the hippocampal amnesic syndrome: 14 year follow-up study of H. M. Neuropsychologia, 6: 215-234. Newell, A. (1973). Production systems: Models of control smctures. In W.G. Chase (Ed.) Visual informarion processing (pp. 463-526). New York: Academic Press. Newell, A., & Simon, H.A. (1972). Human problem solving. Englewood Cliffs, N.J.: PrenticeHall. Patel, V.L.,& Groen, G.J. (1991). The general and specific nature of medical expertise: a critical look. In K.A. Ericsson & J. Smith (Eds.). Toward a general theory of expertise (pp.93125). Cambridge: Cambridge University Press. Pew, R.W. (1966). Acquisition of hierarchical control over the temporal organization of skill. Journal of Experimental Psychology, 71, 746-77 I . Posner, M.I. (1988). Introduction: What it is to be an expert? In M.T.H. Chi, R. Glaser & M.J.Farr (Eds.) The nature of experrise (pp.xxix-xxxvi). Hillsdale, N.J.: Erlbaum. Proteau, L., Marteniuk, R.G., Girouard, Y., & Dugas, C. (1987). On the type of information used to control and leam an aiming movement after moderate and extensive training. Human Movement Science, 6 , 18I - 199. Poulton, E.C. (1957). On prediction is skilled movements. Psychological Bulletin, 54,467-478. Reitman Olson, J., & Biolsi, K.J. (1991). Techniques for representing expert knowledge. In K.A. Ericsson & J. Smith (Eds.) Toward a general theory of expertise (pp.240-285). Cambridge: Cambridge University Press. Ripoll, H. (1991). The understanding-acting process in sport: The relationship between the semantic and the sensorimotor visual function. International Journal of Sport Psychology, 22, 221-243. Robertson S.. Collins, J., Elliott, D., & Starkes, J. (1993). The influence of skill and intermittent vision on dynamic balance. Manuscript submitted for publication. Salthouse, T.A. (1984) Effects of age and skill in typing. Journal ofExperimenra1 Psychology: General, 113,345-371. Salthouse, T.A. (1991). Expertise as the circumvention of human processing limitations. In K.A. Ericsson & J. Smith (Eds.) Toward a generul theory of expertise (pp.286-300). Cambridge: Cambridge University Press. Simon, H.A., & Chase, W.G. (1973). Skill in chess. American Scientist, 61, 394-403. Sloboda, J. (1991). Musical expertise. In K.A. Ericsson & J. Smith (Eds.) Toward a general theory of expertise, (pp. 153-17I). Cambridge: Cambridge University Press. Starkes, J.L., & Deakin, J. (1984). Perception in sport: a cognitive approach to skilled performance. In W.F. Straub & J.M. Williams (Eds.) Cognitive sport psychology, (pp.115-128). Ithaca, New York: Sport Science Associates. Tienson, J.L. (1990). An i n d u c t i o n to connectionism. In J.L. Garfield (Ed.) Foundations of cognitive science. (pp.381-397). New York: Paragon House.

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L. Stakes and F. Allard (Editors)

0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 2 COGNITION, EXPERTISE, AND MOTOR PERFORMANCE FRAN ALLARD Department of Kinesiology, University of Waterloo Waterloo, Ontario, N2L 3Gl

The purpose of this book is to collect and evaluate recent work on the study of experts in motor performance. Some of the following chapters deal with describing differences in motor performance that discriminate skilled from less skilled individuals, and some of the chapters deal with cognitive differences. It is the latter proposition-that cognitive skill is an important component of skilled motor performance-that is contentious. Understanding how the motor system is controlled to produce even simple movements has proven to be very difficult (see, for example, Jordan & Rosenbaum, 1989). For sure, it is not feasible that a "central executive" specifies the contractions of each and every muscle involved in performing a movement; the control of movement must be distributed throughout the nervous system. How then could cognition be relevant to such a system in any role other than determining the overall goal of the movement? Or as baseballs' Yogi Berra said about batting: "How can you hit and think at the same time?" (Dickson,l992). This chapter will make the case that the study of expert sport performers shows the importance of cognition in the production of movement, however messy this proves to be for theories of motor control. The approach used by many investigators in this book is known as the "expertise" approach (Ericsson & Smith, 1991). In this approach, recognized experts in a particular skill domain are compared to non-experts in tests thought to reflect components required to perform well in the domain. If experts are better than non-experts at the experimental tasks, the tasks must reflect some knowledge or ability required for expert performance. The archetypal use of the expertise approach is Chase and Simon's (1973) investigations of the nature of expertise in chess. One of Chase and Simon's tasks required players of different skill levels to study a chess board for 5 seconds, then to recall the positions of as many pieces as possible on a second board. Half of the boards presented were actual game positions, while the remaining boards contained randomly placed pieces. Better players correctly recalled the position of more pieces than lesser players only for the actual game boards, leading Chase and Simon to conclude that chess skill was a function of understanding and encoding the relationships between pieces, rather than superior memory skill. Therefore, in the case of chess, expertise is something acquired through practice at the game rather than an inherent difference between players. The notion of

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expertise as something acquired by the performer, rather than an innate talent possessed by the individual, be this talent thought to be general (IQ) or specific abilities (spatial ability, movement speed), is the defining characteristic of the expertise approach. Expertise is the interaction of the individual with a particular environment, not a main effect of individual or of task (environmental) demands. From this brief description of the expertise approach, it is apparent that any experimenter wishing to use this approach is faced with two problems: determining a criterion for what constitutes an expert, and selecting tasks that evaluate the components of the skill. Ericsson and Smith (1991) have recently proposed solutions for both of these problems. They define expertise as "cases in which the outstanding behaviour can be attributed to relatively stable characteristics of the relevant individuals" @.. 2), thus confining the use of the term expert to individuals with a consistent record of excellence in a particular domain. Ericsson and Smith's advice on task selection in a particular skill domain is to "capture the essence of superior performance under standardized laboratory conditions by identifying representative tasks" (p. 12). "Representative tasks" are tasks that allow evaluation of knowledge needed to perform the full-blown skill, for example, making an opening bid for bridge players (Charness, 1979). These tasks, because they are parts of the whole skill, permit an analysis of the cognitive processes important for their Performance, processes such as search, recognition, computation. The final step in understanding expertise is describing how it is acquired what kinds of knowledge are critical for the particular domain? What type of learning best produces the knowledge? (See Ericsson and Smith for a much better statement of what is summarized here). The expertise approach has been utilized in many cognitive domains such as the study of the nature of skill in the games of chess (Chase & Simon, 1973) and bridge (Chamess, 1979). in medical diagnosis (Patel & Groen, 1991). in computer programming (Adelson & Soloway, 1988), in music (Sloboda, 1991), and in physics problem solving (Anzai, 1991). The work on expertise in cognitive skills is the focus of two recent books: Chi, Glaser, and Farr (1988) and Ericsson and Smith (1991). But what is the application of this approach to the study of expertise in motor performance? Before looking at the role of knowledge in motor skill, we will look at other explanations that have been proposed to explain experthovice differences in the context of sport and motor performance. Approaches to the Study of Expertise in Skilled Motor Performance The study of what makes one athlete better than another has been of interest to sport scientists for many years; this work has resulted in a rich and multifaceted literature in the traditional fields of exercise physiology, biomechanics, sport sociology, social psychology, and motor leaming/control. Many investigators in motor conuolflearning have been concerned with issues of defining and describing expertise, and have adopted a number of different approaches to the problem. Investigators have conceptualized expert motor skill as being a feature of the individual, as being dependent on the pickup of information readily available in the environment,

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or as being the product of a particular individual in a particular environment. In order to contrast the different ideas on the nature of skill offered up by investigators in motor learning and control, let us focus on one particular skill: the skill involved in hitting a rapidly moving ball, as must be done in baseball or cricket batting, and in racquet sports such as tennis, table tennis, badminton, and squash.

Skill is in the person: The Abilities Approach According to this approach, each individual is comprised of bundles of fundamental motor abilities, which are combined in the performance of more complex skills. If an individual is assessed as having lots of a particular ability (i.e., speed of response), this individual should perform better than lesser scoring individuals in any task requiring this ability (i.e.. returning a hard serve in tennis). As well, this speedy individual should be good at all tasks requiring speeded responses: batting a baseball, flying a fighter jet, playing video games. (a)

Edwin Fleishman is the individual who has been most influential in presenting the case for the importance of abilities in human performance. According to Fleishman, "An ability refers to a more general capacity of the individual related to performance in a variety of human tasks ... Both learning and genetic components underlie ability development" (Fleishman & Quaintance, 1984, p. 162-163). Fleishman distinguishes between abilities and skills, with skills being defined as "... the level of proficiency on a specific task or group of tasks. The development of a given skill or proficiency on a given task is predicated in part on the possession of relevant basic abilities" (Fleishman & Quaintance, 1984, p. 163). Fleishman's factor analytic studies have shown there to be eleven psychomotor abilities: control precision, multilimb coordination, response orientation, reaction time, speed of arm movement, rate control, manual dexterity, finger dexterity, arm-hand steadiness, wrist-finger speed, and aiming. (Fleishman & Quaintance, 1984). As well, there are nine physical proficiency factors that have been described by Fleishman and his colleagues: extent flexibility, dynamic flexibility, explosive strength, static strength, dynamic strength, trunk smngth, gross body coordination, gross body equilibrium, and stamina (cardiovascular endurance). (Fleishman & Quaintance, 1984). By determining which abilities are important for particular tasks, Fleishman's measures should be useful for job selection. However, the relationship between abilities and actual performance is complicated by the finding that the pattern of abilities required in a particular task changes with practice (Fleishman & Hempel, 1954). In terms of predicting which individuals will do best on a complex motor task, the abilities approach has not proven to be particularly powerful (see Baba, chapter 4, this volume). As well, Starkes and Deakin (1985) have shown that field hockey players of differing levels of expertise (national team, university team, a class of beginners) do not differ on tests of abilities that would appear to be components of the game (dynamic visual acuity necessary to follow a rapidly moving ball, simple reaction time necessary for reaction to opponents moves, coincident timing necessary for predicting when a ball or an opponent will be at a particular point). Thus,

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athletes of different performance levels cannot be discriminated by measuring their abilities.

Were all motor skills to be constructed from the building blocks of abilities, individuals who are good at a particular motor skill such as tennis should also be good at other skills that require the same kind of abilities, for example squash. As Jones (this volume) points out, the specificity of learning that is so characteristic of motor skills (see Schmidt, 1988) is conuary to this idea. There is something so compelling about the notion that abilities determine motor skill that a second generation of abilities research has evolved (the idea that would not die). Jones (this volume) describes a "modular approach" to motor skill performance, with separate modules replacing abilities in being responsible for the timing, force, and sequencing of skilled motor acts.

In yet another variation on the abilities theme, Ackerman (1987) has united abilities with the idea that a motor skill learner passes from a cognitive stage to an automatic stage (Fitts, 1964) as a skill is acquired. Since skill learning begins with the cognitive stage, it is general cognitive ability which should predict early performance. Only following much practice, when the learner is in the automatic stage, will psychomotor abilities such as perceptual speed be related to performance. Thus psychomotor abilities constrain the final level of skill acquisition, rather than predict initial acquisition. Others have proposed that the ability to perform basic information processing operations is related to sport skill. For example, Deary and Mitchell (1989) have reported a substantial correlation for inspection time and cricket batting average. Inspection time is the exposure duration needed by a subject to determine with 85% accuracy which of two parallel vertical lines is longest when the lines are followed immediately by a mask. The ability to make rapid discriminations about visual stimuli would seem to be important in fast ball games such as cricket. The ability to allocate attention has been advanced as an important aspect of sport skill

(see Nougier, Stein, & Bonnel, 1991, for a recent review), although it is difficult to distinguish between attentional differences in individuals caused by participation in a sport, and trait-like individual differences in attention. Finally, Whiting (1991) proposes that the relationship between abilities and skill may be non-linear. This conclusion is based on a study of table tennis players of varying abilities who were tested for choice reaction time and for time to decide direction (right, left, straight) of a projected table tennis ball (Whiting & Hutt, 1972). CRT was highly correlated with decision time (r;.83) for non-players, moderately correlated for average players (r=.67), but not significantly correlated for advanced players (p.41).Thus abilities may be important early in learning, while other factors dominate after exposure to the game.

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Inspite of the popularity of the idea, there is little experimental support for the notion that the level of accomplishment of an individual performer can be predicted from measured abilities. The role of such higher level abilities as timing or inspection time await further studies to confirm their importance in skilled motor performance.

All the information is in the environment: the ecological perspective Ecological psychology has made a major impact in the area of motor learninglcontrol. According to this perspective, perception and action are woven together such that perception guides action and action provides richer perceptual data. The perception-action link is so direct that no cognitive activity intervenes between them.

(b)

Investigators working from this perspective often use highly skilled performers, for example Lee, Lishman, and Thomson’s (1982) study of the use of vision to regulate the approach to the take-off board in long jumpers, and Bootsma and van Wieringen’s (1990) study of table tennis players hitting an attacking forehand drive. Both of these studies illustrate the exquisite motor control of the expert performers observed, and show how variability across trials in the performance of experts is often functionally linked to accomplishing the goal of the action. Thus the long jumper varies stride length over trials as she approaches the take-off board in order to decrease footfall position and hit the board with greater precision. Table tennis players show a coupling between the initiation time of the drive and the acceleration of the swing, such that drives initiated early are performed more slowly (with less acceleration). Neither Lee et al. (1982) nor Bootsma and van Wieringen (1990) have compared experts to novices on long jumping or on hitting table tennis drives. Because novices have the same perceptual-motor systems as do skilled performers, the same coupling of perception with action should be a feature of the performances of both types of subjects. Presumably, much of the variability in the performance of novices is due to noise in the system which occurs in addition to functional variability. On the other hand, variability in experts’ performance is functionally related to goal acquisition, making the nature of the relationship between the action and the environment easier to see in experts. Thus, experts are simply more experienced with exwcting the perceptual invariants or in producing adjustments to performance as a consequence of perceptual information than are less skilled performers. Thus ecological investigators would expect no influence of cognition on motor performance, and little difference in the perceptual information that controls the performance of skilled and less skilled performers. However, there are many studies that have shown differences in the nature of the information used by skilled and less skilled athletes in the performance of sport related experimental tasks, studies done from an information processing perspective. Skill is the product of both the individual and the environment: information processing differences in skill According to the information processing approach to skilled motor performance, any skilled action is the product of a chain of events, beginning with the analysis of sensory data,

(c)

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followed by a decision about what movement to perform, and ending with the execution of the selected motor pattern (e.g., Marteniuk, 1976). This approach attempts to determine what happens at each step in the journey taken by information as it passes from one stage to the next by careful experimentation which isolates each relevant stage of processing. Of major concern in many information processing studies is the time it takes to perform the component task being investigated. In a classic example of this approach, Keele and Posner (1968) determined the time it takes to utilize visual information to correct an ongoing movement by holding the required movement constant and varying the presence of vision. Subjects learned to perform a six inch movement from a home position to one of two targets in a set of specified movement times. While subjects performed the timed target acquisition task, the room lights were extinguished on half of the trials as soon as the subject lifted off the home position, and did not go on again until the subject touched the target. Subjects moving at the shortest movement time (target time 150 msec., actual time 192 msec) showed no decrement in performance for trials in which the lights went out. For the longer movement times, lights on trials were significantly more accurate than lights off trials. This study shows that it takes a subject somewhere around 200 milliseconds to implement visual information about the accuracy with which a movement is being executed. A critical problem for the information processing approach comes in generalizing from laboratory findings to the performance of real world motor skills . That the results of the Keele and Posner study are generalizable has been established in a very clever study by Peter McLecd (1987, Experiment 1). He tested skilled cricket batsmen hitting balls bowled by machine. Pieces of dowel parallel to the flight of the ball were placed under a carpet at the point where the ball bounced. On one third of the mals, the ball did not hit the doweling, and the ball came straight through. On the other trials, the ball hit a piece of dowel and kicked left or right. A marker was placed on the end of the bat, allowing McLeod to analyze movements of the bat from a film record. The critical point was when in time the movement of the bat was different for balls moving left or balls moving right, a measure of how fast the batsman could react to the pitch. McLeod found this time to be 192 msec, virtually identical to Keele and Posner’s results with a pointing task. By its very nature, the information processing approach produces many small bits and pieces of knowledge about human performance, such as the estimate of the time it takes to utilize visual information that has just been reviewed. The problem comes in putting all the pieces together in order to understand the performance of a skilled motor act. The information processing approach is like taking apart an alarm clock; it is always easier to take the clock apart than it is to put it back together again. To provide an example of the alarm clock problem in skilled motor perforniance, consider the information processing steps involved in batting a baseball. According to an information processing analysis, batting a pitched ball involves first perceiving the speed and location of the incoming pitch, deciding on whether or not to swing at the pitch, and, should the decision to swing be positive, executing the swing. In the best tradition of information processing, Slater-Hammel and Stumpner (1950) set out to assess the

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time taken by these operations, studies done in order to determine how long a batter could afford to watch a pitch and still have enough time left to execute a swing. Slater-Hammel and Stumpner measured two types of "batting reaction time"; the time taken for the subject to initiate a swing of the bat in response to a visual signal (starting RT) and the time to adjust the trajectory of the moving bat by lifting it off a rail along which it was being moved (movement RT). Starting RT was measured to be .206 sec and movement RT was .269 sec. Translating these time into distances from the plate for a 95 mph and a 70 mph pitch, led Slater-Hammel and Stumpner to conclude that "To have sufficient time for a starting reaction time, the ball would have to be from 22 to 30 feet from home base. A movement reaction time would require that the ball be from 28 to 38 feet from home base." (p. 355). Hubbard (1955) pointed out several problems that arise when Slater-Hammel and Stumpner's data are applied to the real world task of hitting a baseball. Slater-Hammel and Stumpner's calculation of ball location at swing initiation considered only the time to initiate the swing, or to initiate a correction to the ongoing swing. The time it takes to actually swing the bat over the plate, the movement time of the bat, was not included in the calculation, and when it is included, there are great problems for an information processing approach. Slater-Hammel and Stumpner assumed batting to be a serial information processing task, involving a decision followed by a response. Thus the information processing time for batting can be determined by adding together batting reaction time (starting or movement RT) and batting movement time. Hubbard used Slater-Hammel and Stumpner's values of .21 sec for starting RT and .27 for movement RT, and the values of .16 seconds or .12 sec for movement time in his calculations. The time the batter actually has to decide and swing is a function of the time it takes for the pitched ball to reach the plate, a time which ranges from .83 seconds for a 50 mph pitch to .43 seconds for a 100 mph pitch. As Slater-Hammel and Stumpner had done, Hubbard translated times into distances, and calculated the distance of the ball from the plate when the batter had to have completed all information processing required to swing at the pitch. Unfortunately, for most pitches, the alarm clock problem emerges; the batter does not have enough time to do the required information processing. In Hubbard's words: To find the point at which the batter would have to get his signal to start to react, we multiply ball flight (60 ft. roughly) by the fraction, interval to "react and move" divided by interval of ball flight at the speeds of pitched balls. A ball travels 60 ft. at 50 mph in.83 sec., at 70 mph in .58 sec., at 95 mph in .43 sec., and at 100 mph, in .41 sec. Using mean figures for "starting reaction time" plus "movement time", we get for 50 mph. 32 ft., for 70 mph. 45 ft., for 95 mph, 52 ft., and for 100 mph. 54 ft. Using mean figures for "movement reaction time" plus "movement time", we get corresponding estimates of 32, 45, 60, and 63 ft. Using .20 sec. "movement time" the corresponding distances for "starting reaction time'' would be 30,42,57, and 60 ft., and for "movement reaction time" 34,49,65, and 69 ft. Note that for fast balls or for something like "movement reaction time" the batter may have to get his signal us or before rhe pitcher releases the bull. (Hubbard, 1955, p. 368).

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Hubbard (1955) also makes the point that it is incorrect to think of batting as a reaction time task. Unlike the standard RT task where the subject has little idea as to when the signal to respond will be given, the batter can see the pitcher wind up in preparation for the pitch, and has a very good idea of when the ball will become visible. In fact, Hubbard and Seng (1954) filmed major league batters during batting practice and showed that the step with the front foot made in anticipation of the actual swing was stvted at the release of the pitch, the start of the swing occurred very shortly (.04 sec) after the front foot was planted, and the temporal duration of the step was related to pitch speed. They also observed highly consistent swing times for their batters, a finding that has also been observed for cricket batsmen (McLeod & Jenkins, 1991). and for table tennis players (Bootsma & van Wieringen, 1990). Hubbard (1955) argues that batters gather visual information continuously and use the accruing visual information to decide whether to continue or to check the swing. A main difference between the information processing approach and direct perception is the emphasis of information processing on laboratory investigations of performance elements suspected as being important for skill. As has been shown, this approach has identified temporal constraints on motor behaviour, such as the time taken to make corrections to an ongoing movement on the basis of visual information. It has not done a good job of integrating the findings of the many lab studies that have been done into a coherent model of skilled motor performance. Modern Studies in Information Processing in Skilled Motor Performance Despite serious problems in explaining performance in fast ball games and heavy criticism from experimenters in direct perception, sport information processing research is flourishing (e.g.. the special issue on Information Processing and Decision Making in Suort of the International Journal of Suon Psvchology (Ripoll, 1991). Modem information processing research deals with the temporal constraints posed by fast ball games by proposing that motor skill experts use predictive information, information that is available before the pitch is released or the ball is served, to guide their response. Abernethy (1991) has summarized a series of studies performed in his lab at Queensland showing skilled badminton players are better than novice players at predicting the landing position of a shuttle when viewing film of an opponent’s stroke that is cut off at various times before, at, or immediately following racquet contact. Further investigations have been done in which films of players performing different strokes were edited to eliminate critical aspects of visual information (player’s arm,racquet, arm and racquet, head and face, unessential background information). Both expert and novice players showed poorer prediction of shuttle landing position when arm and racquet cues were not present. When arm cues were available, only the experts showed a significant improvement in accuracy, showing experts were able to anticipate the landing position of the shuttle from arm or racquet cues, while novice players relied on racquet cues. Eye movement recordings for both expert and novice players confirmed that increased attention was being paid to arm and racquet cues. Ripoll and his colleagues (summarized in Ripoll, 1991) have also used recordings of eye

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movements to determine the information pick up of skilled motor performers. Ripoll distinguishes between two different functions of visual information available to an athlete. "Semantic" visual information is used mainly in open sport situations, and acts to "identify and interpret the situation" (p. 222). The experiments of Abernethy (1991) just summarized show the use made by semantic visual information by badminton players. The second function of visual information -"sensorimotor" visual functioning- is vision used in the service of actually performing an action, "for example, calculating the time to contact needed to release the strike and coordinate the visual and motor systems involved in the stroke" (p. 222). Ripoll experimentally discriminated sensorimotor from semantic functiorhg by measuring the eye movements of expert table tennis players in two situations. The first situation had the players return balls in drill situations where there was no uncertainty about the stroke to be used in the return. This situation assessed the sensorimotor function of vision: the only information required to make the stroke was the flight path of the incoming ball. The second situation involved playing a match, with lots of uncertainty about what the opponent would do next. Eye movements observed in the drill condition were anticipations, looking ahead of the current position of the ball and in the direction of the shot just played. Rarely was the opponent fixated in this condition. In the match condition, the opponent and the early flight of the ball became the focus of attention as the player determined the type and location of the incoming stroke. Ripoll's data are important because they show that ecological perception is only a part of the story. In the drill situation, the players well might be using time to contact to hit the return stroke. However, when the situation becomes more complex, the expert player seeks predictive information, as would be expected from Abemethy's work, in addition to the strictly sensory information utilized in the drill condition. As mentioned earlier, Nougier, Stein, and Bonnel (1991) have proposed that experts in motor performance differ from novice performers in how efficiently attentional resources are deployed. Thus, another major difference between experts and novices is that experts reduce the amount of information that needs to be processed in the very short periods of time available to them. Nougier et al. provide examples of how conceptual models of attention used in cognitive psychology (i.e., controlled vs. automatic processing, cost-benefit analysis, signal detection theory) may be used to assess attentional differences in expert and novices. As well as differing in cognitive information processing, expert athletes well might differ from novices in the variability with which they perform skill elements. "Variability" here means that what is requested of the motor system is executed with greater consistency for experts. Such consistency of performance is critical for the closed skills of figure skating, gymnastics, and diving. As well, studies of timing variability for skilled open skill performers in table tennis ( Bootsma & van Wieringen, 1990) and cricket (McLeod & Jenkins, 1991) show little movement time variability. Indeed, McLecd, McLaughlin, and Nimmo-Smith (1985) have shown that unpractised subjects are capable of a high degree of consistency and accuracy in timing the initiation of a striking movement. Clearly, more work needs to be done to evaluate actual

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performance differences in expert and novice athletes. These studies require experimenters who have enough experience with sophisticated biomechanical techniques to measure and analyze three dimensional records of complex movement patterns (see Carnahan, this volume). Thus, there am many possible locations to search for information processing differences between expert and novice motor performers. One problem is that no matter where you look for differences. you most always find them. Rarely is any effort ma& to determine how much each new expert-novice difference in information processing contributes to overall differences in performance. McLeod and Jenkins (1991), commenting on the many studies of information processing differences for expert and novice athletes, point out that the magnitude of the cognitive differences observed is often much smaller than differences in sport performance for the two groups. The interesting fact about many of these studies is not that there is an expertnovice difference (it would be remarkable if there were not one), but how small the effects are in many of the studies. Take Abernethy’s (1989) study of experthovice differences in the ability to predict where a badminton shot would send the shuttle, for example. His expert group included players up to national level; his novices had never played the game competitively. It would be difficult to imagine a wider span of skill differences than this, and yet he found a superiority of about 10% for his experts. Similarly small differences appear in many other studies of experthovice differences. The task for sports science is not to go on showing experthovice differences in yet more sports. Their mere existence is neither surprising nor interesting. What is required is to show whether the differences that exist are sufficient to explain the dramatic differences in performance between experts and novices or whether we should be looking elsewhere. (McLeod & Jenkins, 1991, p. 291). I n Summary To summarize, the existing literature in motor controVleaming shows little support for expednovice differences being primarily due to differences in simple abilities such as speed of response or visual acuity. The importance of more complex ideas about the nature of abilities, for example, the idea that abilities are modular, remains to be determined, as does the contention that simple abilities consaain the final level of performance rather than predict the initial level of performance.

Ecological psychologists who study perception-action links have almost always studied skilled performers; for skilled performers, variability in performance is typically functional, while for less skilled individuals, variability is also produced by noise somewhere in the system. A major plank in the platform of ecological psychology is to eliminate explanations of motor performance that rely on cognition. It is difficult to know to what non-cognitive explanation

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is able to account for the reduction in the noise in the system with practice. Finally, information processing studies have shown many differences between expert and novice performers, especially for skills that must be performed very rapidly such as hitting a ball or a badminton shuttle. Most notably, experthovice differences have been shown for picking up information predictive of what is to come and for attentional focus as evaluated through eye movement recording. Whether the magnitude of experthovice differences observed in these tasks is sufficient to account for experthovice differences in performance has not been established.

The Expertise Approach and Motor Skill: Can Knowledge Influence Performance? This chapter began with a brief outline of the study of expertise as investigated by cognitive psychologists, then described what has been discovered about the nature of motor skill expertise by investigators in motor learning and sport science. Before making a case for the application of the expertise approach used in cognitive psychology to motor skill, it is important to consider the relationship between cognitive and motor skills: are there any similarities between cognitive and motor skills? One major difference between the two types of skill comes from the declarative nature of much of cognitive skill and the procedural nature of much of motor skill. Because of their declarative nature, cognitive skills are amenable to investigation using such experimental tools as protocol analysis. Motor skills are performed rapidly and, in comparison to cognitive skills, unconsciously. Given such differences, is there any evidence that a factor shown to be critical for skill in cognitive tasks, knowledge, is important in skilled motor performance? In other words, can declarative knowledge impact motor performance? There are two different types of knowledge that are of importance to expert motor performers. First, as in all human activities, motor experts perform in context, in a particular environment. Basketball players, for example, need to know the rules of the game, tactics and strategies, and terminology, in order to play the game effectively. This type of knowledgeknowledge about the game-is strictly verbal or declarative, and may be related to performance skill. However, sports commentators and sports fans, individuals who often are unable to perfom the skills required of the game, share this declarative knowledge with the coaches and players who are able to perform the skills. Thus knowledge about the game may be more a product of familiarity with the particular sporting environment than an essential component to skilled performance. This issue is the focus of the chapter by Allard, Deakin, Parker, and Rodgers in this book. The second category of knowledge is knowledge that can be used in the effective performance of motor skills in a particular domain-knowledge required for performance. Thus the baseball batter must know the speed and location of the incoming pitch in order to know when and where to swing the bat. The second category of knowledge sounds identical to Ripoll’s (1991) sensorimotor function of vision. A difference is that Ripoll’s concern is with describing the different uses to which visual information can be put in performing motor skills. The concern

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here is with knowledge-the contents of the mind-and not with information from the senses. Is there any evidence that skilled motor performers are able to use such “inside the head” knowledge to improve ongoing performance? Indeed, what form would such evidence take?

A first criterion for the impact of knowledge is that it should improve performance. Therefore, a particular player should perform better at those times when he or she has knowledge than those times when knowledge is not available. Knowledge about what is likely to happen next is used all the time in sports. Knowing that a hockey player almost always shoots high to the left lets a goaltender adjust his or her position before the shot is taken to be optimally placed to make the save. Such anticipatory preparation is performed before an actual movement takes place, and does not impact the actual performance of the skill. Thus, a second criterion for a skill able to show the impact of knowledge is that knowledge can only operate during the actual execution of the skill. Finally, ecological psychology emphasizes the close relationship between perception and action, such that incoming perceptual information can be used to drive action in a virtually continuous fashion. A third criterion for showing evidence for the role of knowledge in motor control is that the skill is performed rapidly, with little opportunity for the intervention of incoming sensory information. Thus, data supportive of the importance of knowledge for skill should have the following characteristics: 1. Adding knowledge while all other sources of information remain constant should result in improved performance. The knowledge should not produce a physical adjustment performed in advance 2. of the movement that improves performance. There should be minimal time during the execution of the skill for guidance or 3. correction based on incoming sensory information. A sport skill that fulfils all the above requirements is batting a baseball. According to the rules, the batter must remain in the batters’ box for the duration of a pitch, and can consequently adjust only position (i.e., take a position in the front or back of the batters’ box ) and posture (is., take an open or closed stance) before each pitch. The time taken by a major league pitch to travel to the batter is very short, ranging from 580 msec for a 70 mph pitch to 410 msec for a 100 mph pitch. It takes the batter from 280 msec to 190 msec to swing the bat to make contact with the pitch (Breen, 1967). a time that remains constant for a particular batter (Hubbard & Seng, 1954) regardless of the speed of the pitch. According to Kirkpatrick (1963), the ball crosses the plate in about 10 msec., making the timing of the swing crucial. As well as timing the swing, the batter must also control the location of the bat. Kirkpatrick (1963) describes the physical factors that come into play when striking a ball with a bat as follows.

The state of the bat at the moment of contact with the ball is defined by 13 independent variables, all of which are subject to the batter’s control. These qualities are the 3 positional coordinates of the mass centre (or other reference point) of the bat, 3 coordinates of angular orientation, 3 of linear momentum, 3

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of angular momentum, and 1 coordinate of time. In his control of any one of these variables, the batter may err in either the positive or the negative sense, so it appears that he is faced at the outset with 26 roads to failure. (p. 606) It is important to point out that the task for a batter in a real game is not simply to make contact with the ball: the ball must be hit so that it lands within the field of play, and so that it eludes the best efforts of the defensive team to make the batter out. Most often, for reasons of strategy, the demands are even more specific; for example, the batter may be called upon to hit a long fly ball to the outfield (a "sacrifice fly") in order to score a runner from third base, or to hit to the right side of the field to advance a runner who is on first base. Given the demands on the batter, is there any evidence that knowledge aids the performance of the batter? Each confrontation between a pitcher and a batter during a baseball game is known as

an "at bat". The goal of the pitcher is to get the player out. This can be done in a variety of ways, the main ways being by throwing three strikes (a smke being any pitch the batter swings at or any pitch passing across the plate in an area roughly between the batter's knees and chest) past the batter, by the batter hitting a ball in the air that is caught by a defender, or by the batter making contact with the ball but not making it to a particular base before the ball does. The batter's goal is to get at least to first base which can be accomplished by hitting the ball out of the range of the defenders, or by having the pitcher throw four balls. Each pitch thrown during an at bat occurs in the context of a particular ball-strike count. There are twelve possible counts, and according to baseball lore, some counts work in favour of the pitcher, while others favour the batter. In particular, those counts in which the batter has two strikes are thought to favour the pitcher, if the batter does not swing at the next pitch, he/she faces the possibility of striking out. Such an anxious batter is likely to swing at a bad pitch. Other counts work in favour of the batter, in particular, those counts in which the batter has two or three balls and no strikes. If the pitcher does not throw a smke on the next pitch, he/she faces the possibility of walking the batter. Knowing that the pitcher cannot afford to miss the smke zone means the batter can watch for a pitch that is easy to hit. As sports writer Leonard Koppett describes the confrontation between pitcher and batter: The first rule of effective pitching is to stay ahead of the hitter most of the time. If the first pitch is a smke, the arithmetic shifts way over in favor of the pitcher. Now he can miss the strike zone three times without issuing a base on balls, and has to hit it only once to give the hitter the problems that come with a two-strike count. In other words, if the first pitch is a strike, the pitcher has plenty of margin for error in wying to get the batter to swing at borderline strikes. Even if only one of the next four pitches hits the mark, he still has an even chance with the count 3-2. On the other hand, if the first pitch is a ball, things are not so good. If the second pitch is also a ball, the pitcher is in a real hole with a 2-0 count; he musr come in with three of the next four pitches, and the chances of throwing

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F.AIlard something the hitter wants are much greater. (Koppett, 1991, p. 56-7.)

Any data that show hitting performance changes with the count would qualify batting a baseball as evidence for knowledge impacting motor control according to the three criteria described earlier. Baseball is a sport justifiably famous for the size of the statistical data base that exists for almost every component of the game. In fact, each pitch thrown in a major league game is recorded for posterity. Using the pitch by pitch data, Dewan and Zminda and Stats, Inc.(1990, p. 257) have calculated the probability of a hit occurring as a function of the count on which the at bat ended. The averages include the at bats of all major league players excluding pitchers in both American and National Leagues for the 1989 season, a total of 576,150 at bats. These data shown that batters are more successful for favourable counts (3-0,3-1,2-0), with the mean of the averages for these counts being ,343, than for unfavourable counts (0-2, 1-2, 2-2), the mean of these averages being .175. The data show even more dramatic differences for slugging percentage (.244 and 3 8 ) and on base percentage (.179 and .638). Thus major league hitters do show improved hitting as a function of the ball-strike count.

It might be argued that batters, realizing that the ball will most likely be a strike for favourable counts, decide to swing and hope for the best. Were this the case, there should be a higher percentage of swings taken in favourable counts. In fact, the number of swings taken (the sum of swinging strikes, foul balls, and balls in play) as a percentage of pitches seen is lower for favourable counts (36% of pitches) than for unfavourable counts (58%). Whatever the players are doing to improve hitting, it is not a simple "response bias"; players do not swing more often in favourable counts (data from Dewan et al., 1990). The batting data are important because the information available from the environment does not change as a function of the count. This means the same information about time to contact is available in all counts. Pitchers work very hard to not "telegraph" the type of pitch by throwing different pitches with the same arm motion, thus reducing predictive cues as much as possible. This is not to say that time to contact and advance cues are not important for batting. It is to say that professional batters use all the information available in the situation, including declarative information such as the ball-smke count and knowledge of the pitcher's repertoire. The improved performance shown by batters is a warning that skill must be studied in context: it would be impossible to observe effects of knowledge were batters to be evaluated while hitting balls delivered by a pitching machine or while making decisions based on controlled views of single pitches. The use of knowledge to aid motor performance is not unique to batting a baseball. The game of cricket, so incomprehensible to the North American mind (this one, anyway) is even more strategic because of fewer constraints on the positioning of the fielders, the incredible variety of pitcher a batsman must face, and the length of time of a match. A cricket batsman must certainly be able to make contact with the ball, but equally important in scoring runs are such factors as where the shot is placed, and how many runs are taken.

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The batting data show that knowledge is an important aspect to motor control. How professional batters use knowledge to improve performance remains to be determined; possible candidate explanations include pre-programming the swing, becoming more selective when in a favourable count, or having less uncertainty about the location of the up coming pitch. It is interesting that baseball players and managers, as well as close observers of the game such as sports writers, have often spoken of the importance of knowledge for successful hitting. Ty Cobb, a prodigious hitter with a life time batting average of .366, was not known for his intellectual approach to the game. Even Cobb realized the importance of knowledge to his skill:

The longer I live, the longer I realize that batting is more a mental matter than it is physical. The ability to grasp the bat, swing at the proper time, take a proper stance, all these are elemental. Batting rather is a study in psychology, a sizing up of pitcher and catcher, and observing little details that are of immense importance. It's like the study of crime, the work of a detective as he picks up clues. (cited in Dickson, 1992, p. 86.) According to Thomas Boswell: Inside the mind, that's where the secrets are in this game of timing and deceit, anticipation and disinfoxmation. (Boswell, 1990, p. 242) To recapitulate, it is the contention of this chapter that knowledge-cognition is vital in real world skilled motor performance. Knowledge is important for formulating the intended goals of actions; as well, knowledge facilitates actual performance. Salthouse (1991) conceptualizes the study of expertise as the study of how people have learned to overcome normal constraints on information processing; in his words, skilled performance boils down to a "circumvention-oflimitations" (Salthouse, 1991, p. 299). As an example of this perspective, Salthouse cites his work on the acquisition of skill in transcription typing (Salthouse, 1984). Transcription typing requires reacting to visually presented material by making successive keysmkes. Thus, typing speed should be constrained by the time it takes for a visual reaction time to be made. As Salthouse describes below, the constraints should be serious indeed.

...reaction times to successively presented stimuli in a recent study (Salthouse, 1984) averaged more than 550 msec per response, which, assuming 5 characters per word, would correspond to a maximum typing rate of less than 22 words per minute. Even the rate of repetitive finger tapping, without any requirement to choose among alternative stimuli or to select among distinct responses, seems to place severe constraints on maximum speeds of typing. As an illustration, in the same study in which reaction times were measured, I found that the interval between successive finger taps averaged 163 msec, which would yield a typing rate of less than 74 words per minute. (Salthouse, 1991, p. 296)

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In actual fact, Gentner (1988) estimates a professional typist averages 50 words per minute, with some individuals capable of typing at much faster rates. Salthouse's (1984) work shows that typists circumvent reaction time limits by parallel processing. Highly skilled typists look further ahead in the material to be typed than do slower typists, which allows them to overlap successively performed keystroke movements. The consequence of this parallel processing is, in Salthouse's words, to convert "a serial and discrete task, subject to severe processing limitations, into a dynamic and continuous task in which those constraints are relatively unimportant". (Salthouse, 1991, p. 299). The challenge remains to determine how other types of motor skill experts are able to overcome constraints. Perception and action well may be linked in a seamless web, but the study of sport experts shows that knowledge and action are also tightly coupled, such that "The intent of a manoeuvre is always an intrinsic part of its execution" (Koppett, 1991, p. 11).

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theory of expertise (pp. 153-171). Cambridge: Cambridge University Press. Starkes, J.L., & Deakin, J.M. (1985). Perception in sport: A cognitive approach to skilled performance. In W.F. Straub & J.M. Williams (Eds.), Cognitive sport psychology (pp.115-128). Lansing, NY: Sport Science Associates. Whiting, H.T.A., (1991). Action is not reaction! A reply to McLeod and Jenkins. Infernutiom1 J O W M ~of Sport Psychology, 22, 296-303. Whiting, H.T.A., & Hutt, J.W.R. (1972). The effects of personality and ability on speed of decision regarding the directional aspects of ball flight. Journal of Motor Behavior, 4 , 89-97.

COGNITIVE ISSUES IN MOTOR EXPERTISE J.L.Starkes and F. Allard (Editors) 0 1993 Elsevier Science Publishers B.V. All rights reserved.

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CHAPTER 3 THE ROLE OF THREE DIMENSIONAL ANALYSIS IN THE ASSESSMENT OF MOTOR EXPERTISE HEATHER CARNAHAN Department of Kinesiology University of Waterloo, Waterloo, Ontario, N2L 3GI Many of us are interested in what makes one individual more skilled than another, and the factors that influence the development of motor expertise (see Allard & Starkes, 1989; Schmidt, 1988). There are many perspectives that can be taken to address the topic of skill ranging from examining the manner in which skills are learned, to comparing the motor performance of expert and novice individuals. Those who study motor skill learning have investigated the variables that influence the acquisition and retention of skill, for example, practice scheduling and the presentation of feedback (for reviews see Chamberlin & Lee, 1992; Salmoni, Schmidt & Walter, 1984). While this approach has examined variables that affect the end result or final product of a movement, little attention has been paid to how the form of a movement changes with learning. Gentile (1972) however, states that effective motor skill teaching requires an analysis and understanding of the nature or characteristics of the to be learned skill. Thus, before we can teach skills, we have to understand what is it about a movement that makes it "skilled". An alternative approach to looking at skilled performance is to look at expert and novice differences, where the perceptual abilities or cognitive strategies associated with skilled performance are examined and compared (Allard & Bumett, 1985; Allard, Graham & Paarsalu, 1980; Allard & Starkes, 1980; Charness, 1979; Starkes & Deakin, 1985). In this situation, expert and novice performers are often categorized based on years of experience or national and international competitive ranking; not on some objective measure of quality or form of movement. On what basis are we defining motor skill? Welford (1976) describes skill as a quality of performance which is developed through training, practice and experience. While this definition is acceptable, to actually apply this definition to the categorization of real movements is very difficult. Rarely do we objectively quantify the form of an expert performer when we investigate skill. The goal of this chapter is to review how our definitions of skill may be altered depending on how we define what constitutes a skilled movement. As well, I hope to touch on how information about the form of a movement may influence our thinking about what really is expert performance. When the acquisition of skilled performance is evaluated, or we want to investigate the

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role of perception or cognition in skilled performance we are left with one common problem; that is, how do we quantify skill? In real life, we may use summary statistics like batting average or runs batted in to describe the skill level of a baseball player (see James, 1991; Siwoff, Hirdt, Hirdt & Hirdt, 1992) . Entire volumes are published each year full of statistics which attempt to capture the essence of baseball skill. We want to be able to say that if one player has better stats than another player, then he/she is the more skilled player. This rationale even has legal precedent, and is used in arbitration settlements when professional athletes negotiate their salaries which are directly related to their playing skills. If we as researchers could also adequately define skill based on one summary performance number or statistic then our job would be much easier. When we assess skill acquisition in a laboratory setting, performance is often described in terms of the final result of a movement, by using measures such as reaction time, movement time, terminal accuracy, force output, etc. (see Schmidt, 1988). These measures tell us nothing about how the movement was performed, only about the end result of the action. However, it is quite possible to have very different movement patterns produce identical movement outputs. If the final output is the same, would both movement patterns represent equally skilled movement? Before we can look at the sorts of variables that influence skill, we need to define precisely what constitutes skilled movements. However, this is not a very straightforward task. Is skill based on outcome only, or does the form of the movement matter? Can an individual be considered skilled if their outcome is poor but their form is perfect? Most likely these two aspects of skill (outcome and form) are very highly related, that is, superior movement form will result in superior outcome. For example, the shot putter that can adequately coordinate the generation of forces in all their joints will most likely put the shot the farthest, or the skater with the best technique will jump the highest and spin the fastest. In these examples, superior form will generally result in superior outcome. However, this is not always m e . We can all think of examples where a performer is extremely successful in terms of outcome, with a particularly unconventional style or motor pattern. The converse is also possible, in which a performer has a consistent and acceptable motor pattern, but is unable to produce a highly successful movement outcome (Gentile 1972). Form also plays a critical role in activities like diving, dance, gymnastics, or skating where the actual form of the movement is the main objective of the skill. These types of skills can be categorized as "closed" skills, which are performed in a static, unchanging environment (Poulton, 1957). The evaluation of success in these types of skills is based on how the skills are performed. As Allard and Starkes (1992) point out, "for closed skills, motor patterns ARE the skill; it is critical that the performer be able to consistently and reliably reproduce a defined, standard pattern" (pp.127). In these types of closed skills, it is difficult to quantify and thus understand the movement attributes that characterize expert performance. For years, judges have attempted to quantify whether one movement form is superior to another. But, anyone who has watched diving, figure skating or gymnastics competitions knows that the classifications made by judges are not without question. It is difficult to not let prior experience, political views and opinions interfere with judgements of movement form (Ste-Marie & Lee, 1991). A

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more objective approach would be to quantify or measure movement form with some sort of tool. However, individuals studying motor expertise have rarely done this. This is partly because until recently, the technology necessary to accurately quantify natural human movement has not been available, either because the technology has simply not existed, or it has been too expensive. But, over recent years, two and three dimensional video and optoelectric systems have been developed that are now accessible to many motor behavior researchers. While these systems allow an objective and systematic analysis of movement patterns, they art not the only types of systems that can be used for quantifying movements. There are direct measurement techniques which rely on devices such as goniometers, accelerometers, graphics tablets or manipulanda hooked up to potentiometers. These systems generally constrain movements to one plane and will not be discussed in this chapter in any detail. For a discussion of these types of systems see Winter (1990). Instead I would like to focus on the quantification of natural unconstrained movements using imaging measurement, techniques which are most often used in the quantification of manual reaching and grasping, gait, and complex skills like diving, running and throwing.

How Are the Data Collected? Two and three dimensional imagery systems can be divided into three main types: cinematography, optoelecmc and video, with the latter two being the most frequently used. With video systems, video television cameras are used to record movement on a video tape, after which the video image of the movement is digitized and the position of the image recorded by a computer. With optoelecmc systems, small markers which emit infrared light are attached to the subject, and specialized infrared cameras record the position of the markers; no image of the actual subject is recorded. Each system has advantages and disadvantages which depend on the environmental conditions and type of movements to be measured. There are many parameters that must be considered in deciding what type of measurement system is preferred. Below are listed some of the most important ones: Sampling Rate The sampling rate of most video systems is limited to 60 Hz. (High speed video cameras can sample at higher frequencies but their cost is often prohibitive). This is generally acceptable for most human movement, which is usually low frequency (3 to 10 Hz). However, if higher sampling rates are needed optoelecmc systems can sample up to several thousand Hz. Higher sampling rates are preferable for monitoring a skill in which a high-impact is being monitored, such as hammering. Impact results in a high frequency component to the movement, and this is more effectively measured with higher sampling rates. Higher sampling rates are also useful if acceleration is going to be examined, essentially because the process of differentiation involves calculating differences between sample points, so with more samples the derivative is more representative. Sampling theorem states that the minimal sampling rate for a signal should be 2N+1, where N refers to the frequency component of the signal being measured. Thus, if human movement is 10 Hz, then a minimum of 21 Hz sampling rate will be adequate (Winter, 1990). However, a more generous estimate of 10 times the frequency component of the signal is probably more acceptable.

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Range and Accuracy Video systems generally have a larger range. Optoelectric systems are limited in both potential distance away from the camera and working space volume, based on the power or range of the light emitted from the diodes. Although it depends on calibration of the system and the size of the volume being used, optoelecmc systems are currently more accurate than video based systems. Some optoelecmc systems (e.g. Optotrak) are accurate to the fraction of a millimeter at close volumes. However, for most types of motor skills that may be of interest, this degree of accuracy is probably not necessary. A carefully calibrated video system should provide adequate accuracy (e.g. 2 to 5 mm error). Rotation Video systems are a little more forgiving of markers rotating out of view of the cameras than are optoelecmc systems. With an automatic digitizing video system, if a marker goes out of view, the position of the marker can be hand digitized to replace the missing portions of data. This is because a video image of the subject exists and the human operator can "guesstimate" the position of the missing marker even if it is not in view. The disadvantage of this of course is that a human operator is necessary during the digitization process and this can be extremely time consuming. As well, error is introduced into the system. With an optoelecmc system, data is automatically digitized. However, if a marker if obscured or rotates out of view, the data can only be replaced with interpolation techniques where missing portions of curves are reconstructed using various splines. However, if the appropriate order of spline is not used, the reconstructed data could misrepresent the original missing part of the curve. Regardless of how the data are gathered (and even when using 3 dimensional systems), movements generally have to be planar for all the markers to be seen by all the cameras. Once a subject rotates away from the camera, the markers on their body become obscured from camera view. However, with improved software and multiple camera systems, this is less of a problem. With enough cameras, a marker can be tracked from one set of cameras to an adjacent set. However, this is an expensive solution since additional cameras are needed, as well as complicated software to integrate the information from the various camera pairs. This approach has been successfully used by biomechanicians, however, because of the complexity of the procedure, has not yet been adopted by those looking at skilled performance from a cognitive perspective

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Physical Constraints With video systems, small adhesive markers are placed on the subject demarcating points of interest such as the wrist, fingers, elbow, knee etc. The subject is free to move naturally after the markers have been positioned. With optoelecuic systems, small light emitting diodes are taped to the body. Subjects are constrained to some extent by the wires leading to the light emitting diodes, yet the impact of this restraint will depend on the type of activity subjects perform. Another constraint to consider is that of the actual physical or geographical location of the data collection session. Video systems are generally more flexible in where they can be used. For example, they can be set up out of doors or in actual competitive settings. Actual

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diving competitions have been filmed and later digitized for biomechanical analysis of the dives (Miller. Hennig, Pizzimenti, Jones & Nelson.1989; Miller, Jones, Pizzimenti, Hennig & Nelson, 1990). Optoelecmc systems are generally constrained to the indoor laboratory setting. Since they depend on detecting the location of small infrared lights, any additional infrared light in the testing environment (like that produced by the sun) will interfere with the accuracy of the system. As well, an optoelectric system could not be used for quantifying aquatic activities such as swimming or diving because of the electric feed required to power the light emitting diodes which are placed on the subject. Volume of Data An important factor to consider when assessing the merits of motion analysis is the volume of data generated with video and optcelectric systems (Winter, 1990; 1991). If movement time or accuracy is used to describe a movement, then one or two numbers can be used to represent the performance. However when kinematic or kinetic information are used to describe movement many hundreds or even thousands of data points are collected. For example, if only one marker was placed on the arm to represent the three dimensional translational motion of a reaching movement, which took one second, and was sampled at 100 Hz,there will be 300 data points to represent that skill. You can imagine how many data points are involved in representing the movement of an entire arm and hand where markers are placed on the fingers, wrist, elbow and shoulder. These large volumes of data increase the cost of research because the volumes of numbers are time consuming to deal with, require powerful computers, and take up large amounts of disk space etc. There is a need to evaluate whether the added expense in dollars and time is providing sufficient unique information to warrant the investment. Will this abundance of information be used to develop new theories regarding motor skills, or will we get caught in the fashionable urge to collect volumes of data, for its own sake? While I may have painted a somewhat discouraging scenario, it is my opinion that this approach is warranted, and that as additional data are collected, patterns will emerge which will direct theoretical development.

How Are the Data Analyzed? Once the data are collected there are many ways they can be represented (see Enoka, 1988; Winter, 1990; 1991). At the first level of analysis are the temporal measures; these measures deal with the timing aspects (e.g., movement time) of the whole movement. AS previously mentioned, these are the types of measures that have typically been used to quantify skilled movement. At the next level of analysis are the kinematic measures, which describe linear or angular motion, but do not consider the forces involved in the movement. Imaging measurement systems will provide an output of displacement as a function of time, and other kinematic variables such as velocity, acceleration or jerk can be derived from displacement. More detail regarding how kinematic information can be used to describe skill will be outlined later. The next level of analysis involves kinetic variables, which describe movement in terms of the forces required to generate the motion. Kinetic variables can be calculated from the kinematic information. Of primary concern in this type of analysis are the individual muscle forces or moments of force generated by the muscles across a joint. A related level of analysis

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is termed energetics. One energetic variable, power, is the rate of doing work or the rate of energy change, and is the product of both a kinetic (force) and kinematic (velocity) variable. Power patterns reveal the rate of generation or absorption of force by the muscles (see McFadyen, 1990; 1991; Winter, 1990; 1991). Kinematics There are many stages to data analysis and of course the nature of the analysis chosen depends on how the data were collected, and the questions being asked. In his recent book Winter (1991) does an excellent job of describing the most common kinematic parameters measured for gait. The approach described by Winter could be applied in the analysis of most action patterns. Below, is a description of how the most common data analysis stages are applied to the analysis of reaching and grasping movements, since currently most movement analysis research involves the upper limbs or fine manual skills. Data Smoothing The first stage of data analysis involves removing the noise or unwanted portion of the signals. Polynomial fitting, harmonic analyses, spline curve fitting and filtering will all remove noise, with filtering probably being the most satisfactory (Winter, 1991). Smoothing the data is usually necessary if the curves are going to be differentiated to examine velocity (Winter, Quanbury, Hobson,Sidwall, Reimer, Trenholm, Steinke & Shlosser, 1974) . However, with any type of signal processing, because small distortions may be introduced into the signal, the nature of the distortion is dependent on the characteristics of the raw signal and the specific technique used to process the signal. Caution must be used when interpreting processed data, because a deviation in a curve may not be due to a particular physiological process (e.g. visually based correction in a movement) but instead could be a signal processing artifact caused by something like an underdamped filter. A recent trend in the evaluation of the role of visual feedback in the control of manual aiming has been to evaluate the number and nature of oscillations in acceleration profiles of arm movements. These deviations have then been interpreted as indications of visual feedback processing (van Donkelaar & Franks,l991; Young, Allard & Marteniuk,l988). However, it has not yet been clearly established that the deviations in acceleration profiles are actually corrections. A post-hoc approach has been used to define corrections; that is, if there is a deviation in a profile then feedback must have been used to modify the trajectory. However, an alternative explanation is that a motor program is used to generate an aiming movement with little use of feedback. However, as the movement evolves, noise is introduced into the motor system, resulting in trajectory deviations. Recent evidence has shown however, that in situations where visual feedback is available, there are more trajectory deviations, when compared to no visual feedback situations (Chua, 1992). Thus, although there is mounting evidence that oscillations in acceleration curves are associated with feedback corrections, it has still not been clearly established what causes a deviation in a movement trajectory. The speed at which expert and novice volleyball players can use visual information has

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been examined in a study in which players were required to detect the presence of a ball in a slide of a game situation (Allard & Starkes, 1980). These researchers found that skilled volleyball players were faster than non players at visually detecting balls in slides of volleyball settings. Subjects indicated their perception of the ball with a verbal response. While a verbal indicator of perception was used in this study, in a real game situation, a physical response would be required (most likely a movement directed toward the ball). It is possible that similar, and potentially more sensitive measures of perception could be determined by monitoring how the kinematics of part of the skill (for example the volley action, or the leg action) is affected by perceptual skill level. How the use of visual information changes with training and improved skill could perhaps be investigated by examining the trajectory deviations in skilled and unskilled movement. This approach was used in a recent laboratory study where subjects were required to reach towards and grasp small illuminated dowels in a semi-darkened mom ( Paulignan, MacKenzie, Marteniuk & Jeannerod, 1990). Unexpectedly, on some mals as the subjects initiated their reach towards the dowel, the small light beneath the dowel was extinguished and a light under another dowel, which was located either to side of the original dowel was illuminated. This gave the illusion to the subject that the dowel position has actually jumped to a new position. Paulignan et al. (1991) found that modifications made to the reaching movement in response to this visual perturbation occurred very early in the movement trajectory, even though subjects reported that the dowel seemed to move just before the hand actually reached the target. Applying these findings to the volleyball situation, it is possible that kinematic modifications to reaching or spiking movements in response to visual stimuli (the ball) could be occurring much sooner than would be recorded if subjects only produced a verbal report. This is another example, where information about how a movement is performed may provide unique insights into cognitive abilities or strategies.

Angles One way to describe the motion of the limb is to define joint angles and to measure how each angle changes and varies with the other. An angle can be defined by any three markers, with the middle marker being placed on the axis of joint rotation. One problem with this, however, is that external markers never truly represent the joint center, thus adding error to the calculation of the true angle. A complicated but more accurate alternative is to place several markers on the two limb segments involved in the angle, and use this information to mathematically define them as rigid tubes. Then, an instantaneous joint center can be calculated to provide a more accurate measure of the angle. This approach however, is computationally much more difficult, so a researcher may choose to accept the error associated with the easier method. The amount of error acceptable in calculating a joint angle is of course dependent on the reason it is being measured and the goal of the study . Independent of how the markers are placed on a limb to calculate the angle, the angle can be defined several ways: First, the angle can be described relative to itself, which means that regardless of how the joint angle is oriented in space, the angle described by two joined

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segments remains the same. Terms like flexion or extension describe angles in these terms. Alternatively, joint angles can be described in terms of a world based reference or planes, that is sagittal, frontal or horizontal planes. In this case, the orientation of the joint angle relative to external spatial coordinates is important. Once again, the way one chooses to describe a joint angle depends on the type of inferences one is attempting to make from the data. Figure 3.1 illustrates wrist and elbow angle changes when the subject throws a small ball.

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Figure 3.1. This Figure shows the wrist joint angle (created by markers placed on the knuckles, wrist and elbow) and elbow joint angle (created by markers place on the wrist, the elbow, and the shoulder) of a subject throwing a small ball. The angles are measured relative to themselves. You can see that as the wrist is drawn into flexion, the elbow angle remains unchanged, then both the elbow and wrist joints extend.

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Cross Correlation Once the joint angles of a limb arc defined, they can be compared to each other by a technique called cross correlation. Two angle curves (the curve is the plot of joint angle as a function of time) are correlated to each other. One curve can then be shifted in the time domain, and the remaining overlapping points are then correlated. Using this procedure, the phase shift at which the curves are the most highly related can be determined. This type of analysis can quantify similarity in the shape of two curves, and will reveal at what time lag the similarity in shape is maximal. It is a useful technique to use when you suspect that the shapes of two curves are similar, but they are phase shifted in the time domain. Figure 3.2 illustrates cross correlation.

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Figure 3.2. This Figure shows how two angles (in this case the wrist and elbow angles plotted in Figure 3.1) can be crosscorrelated to examine similarity in trendr. The overlapping points along the two curves are correlated, then one curve is shifted, and the remaining overlapping points are again correlated.

The results of a cross correlational analysis can be interpreted in two different ways. With the first perspective, the assumption is made that if a movement is highly skilled, the movements of the joint angles are highly related. This approach has been used in the comparison of skilled and unskilled dart throwers (Leavitt, Marteniuk & Camahan, 1988). In

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this study expert and novice dart throwers were required to throw darts at a regulation dart board. Movements of the throwing arm were recorded with an optoelecmc system, and angles of the wrist, elbow and shoulder (in the sagittal plane) were crosscorrelated. Higher correlation values between the joints were found for the expert when compared to the novice dart throwers. Thus, the assumption was made that the more skilled an individual is, the more highly correlated the joint angles will be. However, an alternative perspective has also been used to describe skill; expert performers possess the ability to uncouple movements of the joints. In a recent study, Swinnen, Walter, Beirihckx, and Meugens (1991) had subjects perform skills in which the two hands were required to move together, at different tempos and in opposite directions. In this situation, expert performance was manifest by an uncoupling of the two limbs, or in other words a controlled discoordination. Others (Kelso, Putnam & Goodman, 1983; Kelso, Southard & Goodman, 1979; Marteniuk, MacKenzie & Baba, 1984) have shown that when making bimanual movements, the trajectories of the two hands are coupled. However, Swinnen et al. (1991) showed that with practice, movements of the limbs can be uncoupled. Subjects in the Swinnen et al. study were required to generate unsynchronous flexion and extension movements about the left and right elbows. Although this was difficult early in practice and tended to be very "unnatural", with practice subjects were able to achieve the skill. In the Swinnen et al. (1991) study , skill was defined by a dyscoordination or uncoupling of the motion in the left and right elbow joint angles. That is, the elbow joints were eventually able to move at differing tempos. Thus, it appears that depending on the nature of the skill, skilled performance can be defined by either the coupling or uncoupling of the limbs or joint angles. This apparent contradiction can be found in real life examples as well. For example, in a situation where maximum force is required, such as throwing an implement, it makes biomechanical sense to have the angles of the elbow, wrist and shoulder extend in unison to generate maximal force. However, there are different kinds of skills where it is important to have independence of the effectors, such as playing a piano, where the fingers of the hands must play different tempos, and flex and extend independently.

The Grasp (Aperture) Within the past ten years, many researchers have become involved in investigating how reaching and grasping movements are controlled (see Jeannerod. 1988 for a review). The initial step in these investigations was to determine how typical prehension movements unfold. It was proposed that prehension movements are comprised of two phases, the transport and the grasp (Jeannerod, 1984). The grasp phase is usually quantified by measuring the hand aperture, or the distance between the thumb and the forefinger. This measure is used to represent how the hand is opening up and closing around an object. Jeannerod (1984) has shown that the size of the peak aperture is highly correlated with the size of the object subjects are reaching towards to grasp. Thus, it seems to be a relatively effective measure. However, it has been suggested that it would be more fruitful to examine the grasping characteristics of the entire hand. The preshaping and grasping posture of all the fingers should be quantified in order to really understand grasp fomiation (Proteau, 1992). Normal healthy adults appear to be very expert at grasp formation. However, one might not think of simple reaching as a skilled activity since we all seem to be very good at it. But, when an individual has some type of brain injury or

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nervous system disorder, the ability to generate the appropriate aperture size when grasping can be lost. After stroke it has been shown that subtle disruptions in reaching performance can be quantified by monitoring the kinematics of reaching, grasping and pointing movements (Charlton, Roy, Marteniuk & MacKenzie, 1988; Fisk and Goodale,1988; Goodale, Milner, Jakobson & Carey, 1990). Goodale et al. (1990) have argued that kinematic analyses of reaching movements reveal differences between patients and normals that may not be clinically observable, and that this approach could be used to evaluate recovery of function. Thus, a measure as simple as hand aperture can be used to quantiiy the "skill " inherent in reaching and grasping. To illustrate this Figure 3.3 is data on hand aperture for a normal subject reaching to grasp an object.

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Figure 3.3. This shows an aperture profile of a subject reaching to grasp a stationary object located on a table in front of him. These data were collected by an optoelectric system at 200 H z , and were filtered at 7 H z with a dual pass buttenvorth filter.

Transport The transport phase of a reaching movement describes how the limb moves through space to reach a target location. When the kinematics of the transport phase of a reaching movement are evaluated, distinct differences can again be found between skilled and abnormal movement (caused by brain injury). A dependent measure that is often used to describe the

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transport phase of a reaching movement is the velocity of the limb. The velocity profile is usually characterized by a bell shaped curve (Hollerbach & Atkeson, 1987 ) with the skewness of the curve being affected by such factors as the accuracy of the task (MacKenzie, Marteniuk. Dugas, Liske & Eickmeier,l987; Marteniuk, MacKenzie, Jeannerod, Athenes & Dugas, 1987). In individuals with brain damage, the smoothness of the curve can be used to quantify the magnitude of the motor control deficit Deviations in a kinematic profile can be interpreted to suggest patients are relying on visual feedback as opposed to preprogrammed control, or alternatively, deviations can suggest patients have more "noise" in their neural system. Measures like the velocity of the limb or closure of the hand around an object are very sensitive measures, and may be used as tools for assessing cognitive processes in performing skilled movement. Marteniuk et al. (1987) have shown that the symmetry of the velocity profile during reaching is influenced by the context of an object or task. For example, when individuals generate reaching movements towards objects of similar visual impact, a tennis ball and a light bulb, very different reaches are generated. Subjects use the information they already have acquired through experience regarding the fragility of objects when picking up the light bulb and spend a larger proportion of their movement trajectory slowing down so that they can make a very controlled grasp of the bulb. Conversely, when individuals pick up the tennis ball, they spend a smaller proportion of the trajectory slowing down before contact with the object. All of these adjustments are made prior to contact with the object, and are based on subject's prior experience and expectations in dealing with these types of objects. This is just one example of how a kinematic approach can be used to gain insight into cognitive processes, and the types of information individuals use in planning and controlling a reaching movement. Figure 3.4 shows wrist velocity changes in a reaching and grasping task, while Figures 3.5 and 3.6 arc the same task completed by a patient with a neural disorder.

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Figure 3.4. This shows a wrist resultant velocity profile for the same trial shown in Figure 3.3. The Formulae for calculating resultanr is 2 = 2 + )? + 2.

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Statistical Approaches As shown in Figures 3.1 to 3.5, kinematic variables are often represented as curves.

While there is a lot of information inherent in a curve, there are a limited number of statistical approaches that can be used. One approach is to use variables such as coefficient of variation to describe the variability associated with a group of curves, however inferential statistics such as analysis of variance (ANOVA) are not used to make comparisons between groups of curves. An alternative approach is to summarize a curve by picking off landmark points (e.g., peak velocity, peak aperture, time to peak velocity etc.) and entering these data into an ANOVA. MANOVA or similar analysis. This procedure provides a way to statistically deal with the variability between mals or subjects, but by doing so, information about the rest of the curve is lost (Winter, 1987). Peaks should not be picked in isolation; instead they should be analyzed in conjunction with qualitative analysis of the curve shapes. ._1

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Figure 35. The Figure shows several velociry and aperture profiles for a patient wirh an undiagnosed neural disorder, as he reached and grasped a small object on a table top. These curves are for the parienr's leji hand which was severely affected.

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Figure 3.6. These curves show the right hand (which was normal) of the same patient in Figure 3.5. Kinetics A moment of force is the sum of muscular, ligament and friction forces which act on the angular rotation of a joint (Winter, 1991). However, friction and ligament forces are assumed to be negligible, so the net moment is generally considered to reflect the forces due to muscular activity. Thus, moments of force reflect the muscular activity that causes the kinematic patterns we observe. Put another way, they are one step closer to the neural signal. Several variables go into the calculation of moment of force; ground reaction forces (which for gait are derived from a force plate imbedded in the ground and for free upper limb reaching moments are considered to be zero), kinematic information for the linked segments involved in the analysis, and tabled information from an anthropometric model which includes lengths and masses of the

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segments. The most extensive kinetic analyses have been conducted on walking, which at first glance might not be thought of as "skilled' activity. Often when we consider expert performance we tend to imagine sporting or work activities. But, we should not let this preclude the examination of more everyday activities such as grasping and walking, where the most thorough kinetic analyses have been conducted. However, sports biomechanicianshave compiled kinetic descriptions of athletes performing many skills, and perhaps this information should be examined by those individuals interested in the development of expertise (e.g.. de Koning, de Groot & van Ingen Schenau, 1991; Miller et al., 1989; 1990; Schot & Knutzen, 1992). At each progressive level of analysis (temporal, kinematic, or kinetic) the complexity of the analysis and interpretation increases. Although we may see one particular pattern of kinematics, there are a multitude of patterns of muscle force that could create that pattern (Winter, 1984). Winter has shown that kinematic gait patterns can be produced by very different muscle patterns and forces about the leg. It is apparent that in trying to define or describe skill, there are many levels of analysis that can be used. When assessing sports like gymnastics or diving we tend to see a particular predefined kinematic pattern and label that as a skilled performance. However, one must be aware that more than one pattern of muscle activation can be adopted to produce a single kinematic pattern. There has not been enough research examining kinetic patterns during skilled performance or learning to satisfactorily address this issue. However, in a recent study, the kinematic patterns of the leg were evaluated during the learning of a kicking task (Young, 1990). Subjects in this experiment were required to generate time constrained (400 ms) kicking movements, with a 1.67 kg weight strapped to their foot. An optoelecmc imaging system was used to record movement kinematics for the leg. As you would expect, the temporal accuracy of the movements increased over trial blocks. More interesting however, was the finding that kinematic variability did not decrease as a function of practice. Variability We tend to associate skill with consistency of performance, especially if we are thinking in terms of kinematic patterns (see Roy, Brown & Hardie, in press). However, the opposite might be m e . Perhaps motor skill involves the ability to utilize various differing muscle patterns to generate similar kinematic outputs. Variability. or the ability to respond to it in the environment, might be an important atmbute of skill. If this is m e , then the way we think of the cognitive processing associated with skill would need to change. Perhaps a skilled performer does not plan to be consistent. Instead, a skilled performer could ,with experience, develop a repertoire of movement strategies, or the ability to deal quickly with various sources of feedback to amend movements in response to both environmental and internal perturbation. This suggestion is not really very different from Abbs, Gracco and Cole's (1984) updated description of a motor program, "a program is more likely the representation of the dynamic processes whereby the appropriate sensorimotor contingencies are set up to ensure cooperative complementary contribution of the multiple actions to a common, predetermined goal" (pp.214215).

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The notion of motor equivalence addresses the observation that there are many kinematic solutions to a particular movement goal. That is, a single movement goal can be reached in a multitude of ways and from various different starting points. If the movement goal is to reach a particular position in space with the hand (as in picking up a ball), there are many different ways the arm can do this, because of its multiple degrees of freedom (Bernstein, 1967). This is especially helpful if there is an obstacle between an individual's starting position and their reaching target (Cruse, 1986). A temporal or spatial (accuracy) analysis would not be sensitive to this flexibility. For example, movement time or accuracy may not reflect the type of path chosen to reach a goal, or the muscles selected to move the arm. However, if the kinematics or kinetics describing the movement are examined, then the functional variability in achieving a seemingly consistent movement goal becomes apparent. Perhaps skill is the ability to successfully vary kinematic and kinetic patterns in response to physical and cognitive influences. It has been demonstrated that kinematic trajectories can change as a function of the intent of a movement. Maneniuk et al. (1987) showed that subjects spent a larger proportion of their reaching trajectories slowing down before picking up an object when their intent was to place it carefully after picking it up, as opposed to throwing it into a large box. Even though the object and the environment remained the same, the objective of the two tasks differed, and this resulted in altered kinematic patterns. Which Comes First, Kinematics o r Skill? When we describe a motor act as being skilled, generally this judgement is based on the outcome of the movement. That is, the athlete that jumped the furthest, hit the most home runs, or shot the arrow the most accurately is the most skilled performer. Our strategy has then been to use kinematic or kinetic measures to more fully describe the movement characteristics of the skilled performance. However, when enough normative data have been collected, and "kinematic norms" have been established, it may be possible to then predict whether or not a movement will be successful based on a particular kinematic pattern. It may even become possible to successfully alter existing kinematic patterns to resemble ideal movement patterns in order to facilitate the development of skill. This approach is being used in gait research, where movement parameters of pathological gait can be compared to a pool of nonnative data (Winter, 1991). While using kinematics in this manner may be a long way off, the flexibility exhibited by kinematic and kinetic patterns may reflect control strategies utilized by skilled performers and should be considered in theories of cognition and motor skill.

References Gracco, V.L.,& Cole, K.J.(1984). Control of multi-joint movement coordination: Abbs, J.H., Sensorimotor mechanisms in speech motor programming. Journal of Moror Behavior, 16, 195-231. Allard, F.,& Burnett, N. (1985). Skill in sport. Canadian Journal of Psychology, 39, 294-312. Allard, F., Graham, S., & Paarsalu, M.E. (1980). Perception in sport: Basketball. Journal of Sport Psychology, 2, 14-21. Allard. F., & Starkes, J.L. (1989). Motor skill experts. Paper presented at "The Study of

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Expertise: Prospects and Limits". Berlin, June. Allard, F., & Starkes, J.L. (1980). Perception in sport: Volleyball. Journal of Sport Psychology, 2 , 22-23, Bernstein, N. (1967). The co-ordinarion and regulation ofmovemenrs. Oxford: Pergamon Press. Chamberlin, C.J. & Lee, T.D. (in press). Arranging practice conditions and designing instruction. In R.N. Singer, M. Murphey & L.K. Tennant (Eds.), Handbook on research in sport psychology. New York Macmillan. Chamess, N. (1979). Components of skill in bridge. Canadian Journal of Psychology, 33, 116. Charlton, J.L., Roy, E.A., Marteniuk, R.G. & MacKenzie, C.L. (1988). A kinematic analysis of prehension in apraxia. Society for Neuroscience Abstract, 14, 1234. de Koning, J.J., de Groot. G. & van Ingen Schenau, G.J. (1991). Speed skating the curves: A study of muscle coordination and power production. International Journal of Sport Biomechanics, 7, 344-358. Enoka, R.M. (1988). Neuromechanical basis of kinesiology. Champaign, IL: Human Kinetics. Chua, R. (1992). Visual regulation of manual aiming. Unpublished master's thesis, McMaster University, Hamilton, Ontario. Cruse, H. (1986). Constraints for joint angle control of the human arm. Biological Cybernetics, 54, 125-132. Fisk, J.D. & Goodale, M.A. (1988). The effects of unilateral brain damage on visually guided reaching: Hemisphere differences in the nature of the deficit. Experimenral Brain Research, 72, 425-435. Gentile, A.M. (1972). A working model of skill acquisition with application to teaching. Quesr, 17, 3-23. Hollerbach, J.M. & Atkeson, C.G. (1987). Deducing planning variables from experimental arm trajectories: Pitfalls and possibilities. Biological Cybernetics, 56, 279-292. James, B. (1991). Stars 1992 major league handbook. Lincolnwood, IL: Sports TeamAnalysis & Tracking Systems, Inc. Jeannerod, M. (1984). The timing of natural prehension movements. Journal of Motor W , 16,235-254, Goodale, M.A., Milner, A.D., Jakobson, L.S. & Carey, D.P. (1990). Kinematic analysis of limb movements in neuropsychological research: subtle deficits and recovery of function. Canadian Journal of Psychology, 44(2), 180-195. Kelso, J.A.S., Putnam, C.A. & Goodman,D. (1983). On the space-time structure of human interlimb coordination. Quarterly Journal of Experimental Psychology, 35A, 347-375. Kelso, J.A.S., Southard, D.L. & Goodman, D. (1979). On the nature of human interlimb coordination. Science, 203, 1029-1031. Leavitt, J.L., Marteniuk, R.G. & Camahan, H. (1987). Arm movement trajectories and movement control strategies of expert and non-expert dart throwers. Neuroscience Abracts. MacKenzie, C.L., Marteniuk, R.G., Dugas, C., Liske, D & Eickmeier, B. (1987). Three dimensional movement trajectories in Fitts' task: Implications for control. Quurterly Journal of Experimental Psychology, 39A, 629-647.

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Marteniuk, R.G., MacKenzie, C.L. & Baba, D.M. (1984). Bimanual movement control: Information processing and interaction effects. Quarterly Journal of Experimental Psychology, 36A, 335-365. Marteniuk, R.G.,MacKenzie, C.L. Jeannerod, M., Athenes, S. & Dugas, C. (1987). Constraints on human arm movement trajectories. Canadian Journal of Psychology, 41, 365-378. McFadyen, B.J. (1990). A "power plane" technique for analysis of goal-directed mechanical strategies. Proceedings of the Sixth Biennial Conference of the Canadian Society for Biomechanics Quebec. (pp. 141-142) Quebec, Canada. McFadyen, B.J. (1991). A power portrait and its application to the study of human movement. Proceedings of the Annual International Conference of the IEEE-EMBS, 13, (pp. 2210221 I). Miller, D.I., Hennig, E., Pizzimenti, M.A., Jones, I.C., & Nelson, R.C (1989). Kinetic and kinematic characteristics of 10-M platform performances of elite divers: 1. Back takeoffs. International Journal of Biomechanics, 6 , 60-88. Miller, D.I., Jones, 1.C.. Pizzimenti, M.A., Hennig, e., Nelson, R.C. (1990). Kinetic and kinematic characteristics of 10-M platform performances of elite divers: 11. Reverse takeoffs. International Journal of Sport Biomechanics, 6 , 283-308. Paulignan, Y.,MacKenzie, C., Marteniuk, R.G.,& Jeannercd, M. (1990). The coupling of arm and finger movements during prehension. Experimental Brian Research, 79, 431 -435. Poulton, E.C. (1957). On prediction in skilled movements. Psychological Bulletin, 54,467-478. Proteau, L. (1992). Personal Communications. Roy, E.A., Brown, L., & Hardie, M. (in press). Movement variability in limb gesturing: Implications for understanding apraxia. In K. Newell & D. Corcos (Us.), Variability in Motor Control. Champaign, Illinois: Human Kinetics. Salmoni, A.W., Schmidt, R.A., & Walter, C.B. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 95, 355-386. Schmidt, R.A. (1988). Motor control and learning: A behavioral emphasis (2nd ed.). Champaign, 1L Human Kinetics. Schot, P.K., & Knutzen, K.M. (1992). A biomechanical analysis of four sprint start positions. Research Quarterly for Exercise and Sport, 63, 137-147. Siwoff, S., Hirdt, S., Hirdt, T., Hirdt, P. (1992). The 1992 Elius baseball analyst. New York: Simon & Schuster. Ste-Marie, D.M., & Lee, T.D. (1991). Prior processing effects on gymnastic judging. Journal of Experimental Psychology: Learning Memory and Cognition, 17, 126-136. Starkes, J.L., & Deakin, J.M. (1985). Perception in sport: A cognitive approach to skilled performance. In Straub, W.F., & Williams, J.M. (Eds.), Cognitive sport psychology. Lansing, NY: Sport Associates. Swinnen, S.P.. Beirinckx, M.B., Meugens, P.F., Walter, C.B. (1991). Dissociating the structure and metrical specifications of bimanual movement. Journal of Motor Behavior, 23,263279. van Donkelaar, P., & Franks, I.M. (1991). The effects of changing movement velocity and

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complexity on response preparation: Evidence from latency, kinematic, and EMG measures. Experimental Brain Research, 83, 6 18-632. Welford, T. (1976). Skilled performance: Perceptual and motor skills. Glenview, Illinois: Scott, Foreman & Company. Winter, D.A. (1990). Biomechanics and motor control of human movement. New York: John Wiley & Sons. Winter, D.A. (1987). Are hypotheses really necessary in motor control research? Journal of Motor Behavior, 19, 216-279. Winter, D.A. (1984). Kinematic kinetic patterns in human gait: Variability and compensating effects. Hwnan Movement Science. 3, 51-76. Winter, D.A. (1991). The biomechanics and motor control of human gait: Normal, elderly and pathological. Waterloo, Ontario: University of Waterloo Press. Winter, D.A., Quanbury, A.Q., Hobson, D.A., Sidwall, H.G., Reimer, G.D., Trenholm, B.G., Steinke, T., & Shlosser, H. (1974). Kinematics of normal location: A statistical study based on T.V. data. Journal of Biomechanics, 7, 419-486. Young, R.P. (1990). The nature of motor-control strategies underlying the learning of a kicking task. Unpublished doctoral dissertation, University of Waterloo, Waterloo, Ontario. Young, R.P., Allard, F., & Marteniuk, R.G. (1988). The kinematics of visually-feedback based error corrections. SCAPPS Abstracts, 19, 26.

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COGNITIVE ISSUES IN MOTOR EXPERTISE J.L. Stakes and F.Allard (Editors) 0 1993 Elsevier Science Publishers B.V.All rights reserved.

CHAPTER 4 DETERMINANTS OF VIDEO GAME PERFORMANCE DONNA M. BABA The Usability Group Inc. Willowdab, Ontario, M2J 4V8 The purpose of the studies conducted was to obtain an initial understanding of what underlies or determines performance in video games. Although the popularity of playing video games has grown rapidly through the 1980's and 1990's. the study of video game skill is largely uncharted territory. What do people learn when playing video games? What comprises skill in this domain? Why are some people better at video games than others? Seeking answers to these seemingly simple and basic questions became a labyrinthine search employing several different approaches to the study of perceptual motor skills, including an individual differences in psychomotor abilities approach, an expert-novice approach to skilled performance, and various learning paradigms before some preliminary answers became apparent. While the majority of this paper focuses on sharing the preliminary answers eventually found, a brief summary of the earlier, less fruitful searches also is provided. In some respects, the null results are equally informative in that they run counter to "common sense" explanations and indicate what is unlikely to underlie video game performance. A complete description of all of these studies can be found in Baba (1986). Psychomotor Abilities and Video Game Performance An appealing, common sense explanation of what determines video game performance

and skill lies in the domain of psychomotor abilities. It is presumed that individuals differentially possess some general trait or set of abilities that are particularly supportive of video game performance. This explanation suggests, for example, that some players are better than others because they have faster reaction speed, better manual dexterity, and better hand-eye coordination. This type of explanation is assumed to underlie some real world applications of video games. For example, video games are appearing in classrooms for learning-disabled children and neuropsychology clinics, where therapists prescribe various games to exercise different cognitive and perceptual motor components such as: memory, reaction time, hand-eye coordination, reasoning, and sequencing ability (Stewart, 1983). And, the United States military

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has been examining whether video games could be used for personnel training, and as reliable and valid aptitude tests for various military jobs like, pilots, radar operators, and artillery operations (Jones, Kennedy & Bittner, 1981; Kennedy, Bittner, Harbeson & Jones, 1982). All of these uses and intended uses of video games are based on the assumption that video games have something in common with real world skills. The assumption is that the same set of basic abilities underlie or determine performance in both domains. Therefore, training and improving the basic abilities in one task domain (i.e., video games) should benefit performance in another task domain (i.e., real world skill). While this assumption is implicit in the use of video games, therapists, educators and military personnel are careful to point out that they have little beyond anecdotal evidence to support this assumption.

To substantiate this view empirically, measures of psychomotor abilities must be found to correlate strongly with video game performance, and/or strong correlations must be found between individuals’ performances on various video games. That is, if psychomotor abilities do, in fact, determine video game performance then, the ordering of individuals based on their levels of psychomotor abilities ought to strongly predict the ordering of these same individuals on their video game performance, and/or the ordering of individuals should be essentially the same across different video games. Several video game experiments were conducted to test this hypothesis, and neither of these lines of substantiating evidence was found for video games. In one study employing 175 subjects, individuals’ performances on 2 different video games had low correlations with their measures on 10 psychomotor abilities (e.g., Fleishman, 1972) thought to underlie performance in these games (correlations corrected for attenuation ranged from -.26 to .25). In another study of 17 different video games, it was found that test-retest reliability of video game scores was low to high (reliability .09 to .92). however, the majority of intercorrelations among the different video games were very low to moderate in strength. Furthermore, in a study of group differences, when the same 10 psychomotor abilities of highly skilled and novice groups of video game players were compared, there was little difference between these groups in their psychomotor abilities, even though there was a substantial difference in their video game performance. These results found with video game performance are in keeping with past research findings in perceptual motor skills in general. Past research does not support the assumption that individuals’ performance on one perceptual motor task can be used to predict their performance on another task even though both tasks appear to require the same basic abilities (see Henry, 1958; Marteniuk, 1974, 1976; Schmidt, 1982 for review). The correlation between performance on different tasks is typically low (range = -.40 to +.40). Also, the research on mining of motor skills shows that the amount of transfer - the gain (or loss) in proficiency in one skill as a result of practice on some other - is positive but quite small unless the two tasks are so similar as to be practically identical (Henry, 1958; Schmidt, 1982).

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The apparent lack of relationship found in the video game experiments makes the psychomotor abilities explanation for video game performance difficult to defend. It should be noted that these results do not negate the fact that improvements in real world skills could occur with coincident video game playing. Rather, these results seriously challenge the common sense reason (i.e., improved psychomotor abilities) for any observed improvements. Expert-Novice Difference and Video Game Performance In a further attempt to identify determinants of video game performance, several experiments were conducted to examine expert-novice differences in video game performance. The basic strategy of this skilled performance approach is to compare the performance of highly skilled performers and less skilled performers on tests of components that appear important for determining performance. If a component is in fact important for determining performance, then highly skilled performers should perform better on the component test than less skilled performers. The video game performance components examined were movement control, and game-specific knowledge. Nine subjects served in these studies, who were expert performers on a video game called "Lady Bug" by Coleco (1982). Their experience with the Lady Bug game ranged from approximately 200 to 350 hours of play, and the weakest player could reliably achieve a minimum score of 80,000 points while the strongest player's minimum reliable score was 500,000 points. In contrast, the 9 novice players who served as test subjects achieved an average score of 8,673 points over 18 games (2 games each). The Lady Bug game is a maze-running game where the player moves the Lady Bug with a joystick control through the maze (similar in concept to the popular "Pac Man" video game). The object of the game is to a attain a high score by having Lady Bug eat the dots, letters, hearts and vegetables along the maze pathways, while avoiding the skulls or being eaten herself by one of four predatory insects. When the player clears the screen, a new screen is presented and each successive screen consists of faster and "smarter" predatory insects. There are turnstiles within the maze that can be moved only by the Lady Bug, and these can be used to dodge and escape the insects. Thus, much of the maze configuration can be changed at will by the player. In observing game play, one obvious difference between expert and novice performance is the skill with which Lady bug movements are controlled and executed. In one study, this apparent movement control difference was examined directly and quantified by having the subjects follow predefined paths in the Lady Bug maze. The pathways differed in their complexity in terms of the number of turns required, and ease/difficulty of turn execution. Over all pathway conditions, the novices attained only 73% of the speed of experts, and made twice the number of errors as experts on easy turns and 24 times the number of errors on difficult turns. These large and robust differences indicate that movement control and execution skill is an important determinant of video game performance. In contrast to the experts, the Lady Bug movements under the control of novices are slow, gross and jerky. Novices have difficulty

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making direction changes in one smooth movement, and they cannot gently nudge turnstiles into closed position. This lack of reliable movement control is often directly responsible for Lady Bug’s demise. The fact that expert video game players display superior movement skills in comparison to novices is not surprising. Superior movement control on the part of highly skilled performers is readily apparent in most perceptual motor skills. The more interesting questions pertain to the nature of skilled movement performance and its contribution to the overall skill demonstrated by the experts. For example, is skilled Lady Bug performance mostly determined by superior movement control, and does it matter what control device an expert uses to play the game? This same question pertains to many sport skills. Most elite athletes have superior movement control, and specific preferences when it comes to equipment characteristics. The question is, to what extent is their elite performance dependent on the equipment characteristics? Answers to these types of questions can provide insight into the nature of movement skill, and the relationship of movement skill to skilled performance. To obtain answers to these questions, the expert and novice Lady Bug players played two different yet, in many respects, similar video games (Lady Bug and Ms. Pacman) with two different joystick controls (Coleco joystick and Atari joystick). In total there were 4 experimental conditions: Lady Bug with Coleco joystick, Lady Bug with Atari joystick, Ms. Pacman with Coleco joystick, and Ms. Pacman with Atari joystick, and all subjects participated in all conditions. In both games the object is to clear the maze of game elements while avoiding being eaten by 4 pursuing creatures. As a player advances in either game, the creatures become quicker and “smarter” to thwart the player’s advancement. The subjects played each game-joystick combination for 20 minutes, and the results for total points and mean game score are shown in Figure 4.1. Analysis of variance for total points and mean game score revealed main effects of skill F(1.14) = 18.77, p Mne = 28.8 %), the free back (Me = 16.95 % < > Mne = 5.15 %; t=2.54, p < 0.05) and the free space (Me = 5.91 % < > Mne = 1.14 %; t=2.47, p c 0.05). The non-experts, however, looked more to the attackers, the goal, and the ball. In this context, it has to be emphasized that the location of fixations had a great impact on the response adequacy, since non-experts often passed the ball to the wrong teammate. As discussed elsewhere for off-side positions (Helsen & Pauwels 1990, 1991). they also repeatedly passed the ball to the teammate in off-side position, instead of making a more appropriate dribbling movement, as did the experts.

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Discussion In general, the hypotheses were confirmed by the results of the research. In terms of a product evaluation, the experts did, indeed, respond faster and more correctly than did non-experts. In terms of process evaluation, a certain perceptual automation in the visual exploration seems to occur as a function of increasing expertise. This automation is characterized, as confmed in the literature, by greater efficiency and an increase in selectivity and processing speed. These factors give rise to shorter information processing times and, hence, shorter response times, and better performance. Clearly this shows the importance of the way in which the available information is analyzed and applied (process) in terms of the speed and correctness with which the ultimate decision is made (product).

In the first instance, the conswction of a task-specific declarative knowledge base is apparent in the visual exploration of the two skill groups. %or to the transition from a tactical decision into a motor response, soccer experts make better functional use of the defenders in general and the free back and the free space, in particular. while non-experts look primarily at the ball, the attackers, and the goal. That experts make better use of the position game of the free back can be explained by the enhanced task of this player in recent years. He must not only act as the organizer of the defense but also provide the attack impulse as the fust and undefended attacker from the second line. Non-experts,however, cannot assess the proper value of the free back position. In the same way, non-experts make much less use of the errors that are made in the defense and of the information available in the free space, as noted by other researchers (Bard & Carrikre, 1975; Bard et al., 1975; Bard & Fleury, 1976a, 1976b, 1976c. 1980, 1981). The same reasoning can be applied in relation to the position of the goalkeeper in a penalty-kick situation and the formation of the wall in a free-kick situation. A greater amount of specific declarative knowledge, however, does not always guarantee better performance. That experts also differ in the efficiency with which they can link environmental information to declarative knowledge is demonstrated by their shorter response times and the greater degree of response correctness. The key to expert ability is that they extract a greater amount of relevant information from the context in one single fixation, probably by the chunking and structuring of meaningful information components. That processing time is reduced by the processing of this information in working-memory can be deduced from the shorter response times. For the novice, the interpretation of information from declarative knowledge has heavy costs both in time and the amount of working-memory required. Interpretation requires retrieval of declarative information from long-term memory, and the individual production steps be small in order to achieve flexibility and generality of the system. Proceduralizationreduces the load on working memory, since long-term information no longer needs to be held there. The concept of proceduralization as "tuning" is a useful one because, even after a skill is compiled into a task-specific procedure, learning continues and performance improves (Anderson, 1982). One learns, for example, very soon in soccer how to shoot a ball or what common specific procedure to use in front of the goal. With further proceduralization, one becomes more judicious about when to shoot the ball and which cues in the environment indicate that a pass or dribble would be preferable. This tuning of search has been characterized

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by three processes: "generalization", the process whereby production rules become broader in their applicability, "discrimination", the process by which rules are narrowed, and "strengthening", the process that allows better rules to be reinforced and poorer rules to be weakened. Clearly these tuning processes help determine the difference in performance between novice and expert players (Anderson, 1982).

This can also explain the efficient scan paths of experts in the verbal evaluation of tactical game situations from soccer presented on slides (Helsen et al., 1986b). Non-experts must often bring the same information back to working memory by repeated fixations. This may be analygous to the verbal repetition of material when one is in an early cognitive phase. Finally, this emerges more clearly as the decisions become more complex and more structured. Perhaps the visual exploration and evaluation is more directional among experts, more "schema driven" instead of "search driven" (Gilhooly & Green, 1988). Tasks in which a dynamic display and time-constraint decision making is used, such as in this experiment, encourage subjects to use more predictable orders of search. Thus they rely more on the interpretative function of the visual system, than when either the display is static or the search task is not time-constrained. However, that priorities are given to a rather limited number of cues does not fully support the original hypothesis that experts "see" totally different informational elements. This interpretation also emerges in the publications of others (Abernethy & Russell, 3987b; Bard et al.. submitted for publication). On the basis of our results, it can be stated that an expert sees what he knows, and the process is more efficient, more selective, and more rapid the more expert he is. From the cortical standpoint, highly skilled players can thus, in a limited sense, be considered skilled scanners. In this regard, we support the statement of Fodor and Pylyshyn (198 1, pp. 189) "What you see when you see a thing depends upon what the thing you see is. But what you see the thing as depends upon what you know about what you are seeing." Conclusion In summary, the theoretical assumption can be accepted that experts in a particular discipline differ from non-experts in the amount and type of knowledge they possess and in the way in which they process available information. Furthermore, the results of these sport-specific tasks provide insight into the knowledge structure of players with differing experiential background and how changes occur as a result of increasing expertise. Both the construction of declarative knowledge and the ability to "compile" and "tuning" can be considered as "software" attributes. They can be studied by means of the indirect and direct paradigms and research techniques described. Sports. in general, and team sports, like hockey and soccer, in particular, are, therefore, very appropriate for study. Each team has eleven players on a large field of play, and each has very specific defensive and offensive duties. In each, players also need to structure relevant game information. The consensus among researchers who apply these techniques is more evident than among those who investigated "hardware" components. In other words, as suggested by Starkes and Deakin (1984). the interaction found between skill level and the processing of structured match information seems to form a stable basis for further research.

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It would be valuable, as has been suggested by Bard et al. (1987). to derive a comprehensive index of perceptual efficiency that would completely chart the various "software" aspects of visual-information processing. According to the various research techniques and results (see Figure 7.1). the following factors need to be taken into account: retrieval capacity, rapidity of detection, quality of advance cue usage, simple and complex decision speed, accuracy and adequacy of response, and the organization of visual search patterns. Bard et al. (submitted for publication) recently suggested also taking into consideration the ability to use information acquirrd through the peripheral visual system and the economy of operations, (i.e. the attentional cost of the operations leading to a decision). Such a multitask approach provides a more realistic picture of domain-specific skill because of the inter- and inm-individual performance variability in individual tasks. The value of this approach has been c o n f i i e d in the stepwise multiple regression analyses of hardware and software variables (Starkes & Deakin, 1984; Starkes, 1987. 1990). For expert field hockey players, the determining factors appear to be how well they are able to encode and use information about the game structure and how well they can predict placement of a shot following a view of ball impact. According to Starkes (1987), these findings are encouraging for two reasons. First, these tasks are closer to actual game performance requirements than many of the other tasks employed, so their importance is intuitively appealing. Second, none of the "hardware" factors figured significantly in the prediction of skilled performers. Such a multitask approach, however, has only been used for fieldhockey and volleyball (see Figure 7.1). Therefore, in a further extension of our study, we investigated the relative importance of both "hardware" and "software" aspects of the visual system in the determination of expertise. The "hardware"-attributes assessed were simple reaction time, peripheral reaction time, static visual acuity, dynamic visual acuity, depth perception, periheral visual range, and visual correction time. The "software"-attributes were complex decision speed and adequacy, number of fixations and fixation duration in solving tactical game problems, presented statically by means of slides and dynamically by means of 16-mm film. A stepwise discriminant analysis was used to determine the most discriminating variables. The results indicated an average squared canonical correlation of .87 with the significant step variables all being "software"-variables. The primary variable was the response adequacy (F=20.91, ~ 0 . 4 5p