VISUAL SEARCH STRATEGIES OF EXPERT ABLE ...

VISUAL SEARCH STRATEGIES OF EXPERT ABLE-BODIED AND WHEELCHAIR TENNIS PLAYERS

A Dissertation Presented to The Faculty of the Curry School of Education University of Virginia

In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

By Melissa Hunfalvay Bachelor of Social Science - Psychology Masters of Counseling

May, 2004

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UMI Number: 3118413

Copyright 2004 by Hunfalvay, Melissa

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Abstract The purpose of this study was to examine visual selective attention strategies of elite wheelchair (WC) tennis players. As an extension of research, elite able-bodied (AB) tennis players were examined and as a second research extension, a new piece of equipment, not previously used in sport, the Eye-gaze Response Interface Computer Aid (ERICA) was utilized. A total of 32 WC and 31 AB tennis players participated in this study. All participants had current world rankings. Participants watched a video of a professional tennis player serving and imagined competing in a match against the player as eye movements were recorded by the ERICA system. The ERICA system was noninvasive and found to be highly reliable (r = 0.92). During the ritual phase of the serve, results revealed WC players either predicted the tossing area ahead of time (the WC Predictive group) or focused on the non-dominant tossing arm (the WC Real Time group). During the preparatory/execution phase the WC Predictive group focused on the non-dominant arm, while the WC Real Time group focused on the arm, racquet and shoulder area. After ball/racquet contact WC players focused on the ball. AB players focused on the non-dominant arm during the ritual and preparatory phases then the arm, racquet and shoulder region during the execution phase and after ball/racquet contact focused on the ball for the longest period of time. These results are a step in the process o f developing knowledge to educate and create intervention strategies for tennis players of all skill levels, AB and disabled, to improve performance when playing tennis.


Kinesiology Department Curry School of Education University of Virginia Charlottesville, Virginia

APPROVAL OF THE DISSERTATION This dissertation, Visual Search Strategies of Expert Able-Bodied and Wheelchair Tennis Players, has been approved by the Graduate Faculty of the Curry School of Education in partial fulfillment of the requirements for the degree of Doctor of Philosophy.

Major Advisor, Professor Linda K. Bunker

Committee Member, Professor Martin E. Block

—

Committee Member, Professor B. Ann Boyce

Committee Member, Professor Luke E. Kelly

Committee Member, Professor Tonya R. Moon


DEDICATION

This dissertation is dedicated to my parents for the value of knowledge through education they have instilled in me and the support they have given me in order to attain it.

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ACKNOWLEDGMENTS

To my brother Adrian, and sister Natalie, for their unwavering love, friendship and support. To Carma Lee for her constant encouragement, love and ‘you can’ attitude. To my advisor Linda K. Bunker for her guidance, professionalism, patience, encouragement, support, friendship and careful revisions during this dissertation and throughout my program of study. Without you none of this would have been possible sincerest and heart felt thanks. Thank you to my Doctoral Committee, Martin E. Block, B. Ann Boyce, Luke E. Kelly and Tonya R. Moon, for their reading, comments and suggestions during this process. Thank you to Mrs. Deborah Larkin for her financial support through the Larkin Foundation and the grant that enabled this project to be undertaken. Thank you to Bob Bamaby and Todd Loomis for their assistance in videotaping. To all of the participants who graciously volunteered their time for the purpose of applied research and education - thank you. To the friends in my life who understand and embrace diversity, equality and human rights in its many facets. Each of you fulfill my life, and others, by enabling our global community, with its many races and cultures, to appreciate and embrace differences while also understanding similarities we all strive for and deserve as fellow human beings with universal rights. You know who you are - thank you.

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TABLE OF CONTENTS

Abstract.....................................................................................................................................iii Dedication..................................................................................................................................v Acknowledgements..................................................................................................................vi Table o f Contents....................................................................................................................vii List o f Tables......................................................................................................................... xiii List of Figures........................................................................................................................xvi Chapters I.

Introduction................................................................................................................. 17

II.

Review of Literature..................................................................................................24 Visual focus of attention......................................................24 Theories of Attention and Visual Search.....................................................26 Feature Integration Theory (Treisman & Gelade, 1980)............... 26 Similarity Theory (Duncan & Humphrey, 1989)............................29 Movement Filter Theory (McLeod et al. 1991)..............................30 Movement Filter Theory as it Relates to Other Theories of Attention...32 Theories of Attention and their Relationship to Sport................... 33 The Visual Search Process............................................................................35 Stage 1: The Collection of Visual Information............................... 36 Stage 2: Focal Attentive Cue Utilization of the Visual Search Process......................................................... 36 Methods of Measuring Visual Selective Attention..................................... 38 Eye Movement Recording Systems Used Outside the Sporting Arena..40

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Direct Viewing......................................................................40 Photoelectric Viewing.......................................................... 40 Reflection from Attachments to the Eye.............................41 Eye Movement Recording Devices Used for Sport Assessment...................................... 41 Recalling Focus of Attention...............................................41 Eye Movement Recording Devices Using Reflection

42

NAC, Eye Movement Recorder, Model V ..........................42 Applied Sciences Laboratories, 4000SU Eye Movement Headband System............................44 Applied Sciences Laboratories, 5000SU Eyetracker System..............................................44 Eye Movement Recording Devices Using Reflection in Tennis: A Summation.................... 45 Eye Response Technologies, Eye-gaze Response Interface Computer Aid................. 45 Terminology of Data Output from Eye Movement Recording Devices Using Reflection - Specifically the Eye-gaze Response Interface Computer Aid..................... 46 Fixations and Fixation L ocations................................46

Fixation Durations...................................................47 Saccades................................................................... 47 Pursuit Tracking....................................................... 48

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Limitations of Eye Movement Recording Research

48

Sport and Expertise Characteristics of Visual Selective Attention...................................................... 50 The Autonomous Stage of Learning According to Fitts and Posner (1967)............................... 52 Visual Selective Attention Strategies of Expert Tennis Players.......................................................... 53 Limitations to Current Research - Able-Bodied Participants...................................................58 Limitations to Current Research - the Term “Expert”

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Purpose of the Current Research......................................... 60 Descriptive Statements of Research.................................... 62 III.

Methodology...............................................................................................................64 Wheelchair Participant Disability Criteria...................................... 64 Visual Acuity.................................................................................... 66 Procedure....................................................................................................... 66 Apparatus: The Eye-gaze Response Interface Computer Aid (ERICA)...67 Reasoning for Selecting ERICA...................................................... 68 Designated System Parameters........................................................ 68 Reliability Testing of the ERICA System....................................... 70 Test Stimuli.................................................................................................... 70 Test Film: Tennis Servers.................................................................70 Participants..................................................................................................... 73

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Movement Violators.........................................................................74 Predictors.............................................................................. 75 Gaze Trail Distractors.......................................................... 76 Modifications in Sample of Tennis Players.................................... 76 Data Analyses................................................................................................78 Average Number of Fixations..............................................79 Average Fixation Durations.................................................79 Temporal Analysis............................................................... 80 Fixation Locations............................................................... 82 Pursuit Tracking....................................................................85 Concluding Questionnaire...................................................85 Tests for Learning............................................................................. 86 Movement Violators Retested.......................................................... 86 Descriptive Statistical Analyses....................................................... 88 IV.

Results......................................................................................................................... 91 Total Fixations and Durations (Descriptive Statement 1, 2, 3, 4)..92 Fixation Locations (via Lookzones; Descriptive Statements 5,6)......... 93 Differences in Temporal Phases of the Serve (Descriptive Statements 7, 8)........................................... 94 Fixation Locations (via Lookzones) and Temporal Phases (Descriptive Statements 9, 10)................... 96 Pursuit Tracking (Descriptive Statements 11, 12)..........................99 Concluding Questionnaire..............................................................100

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Analyses of Results via Descriptive Statements........................... 101 V.

Discussion................................................................................................................. 105 Visual Search Strategies for Able-Bodied (AB) Tennis Players............. 105 Fixations, Durations and Locations............................................... 105 Fixation Locations during Temporal Phases..................................108 Summary of Able-Bodied Results................................................. 112 Visual Search Strategies for Wheelchair (WC) Tennis Players................113 Fixations, Durations and Locations............................................... 113 Fixation Locations during Temporal Phases.................................114 Follow-up Questionnaire.................................................. 117 Summary of Wheelchair Results.................................................. 122 Observations about Wheelchair (WC) and Able-Bodied (AB) Results. 123 Fixations, Durations and Locations.............................................. 123 Fixation Locations during Temporal Phases............................... 126 Wheelchair (WC) Real Time, WC Predictive Group and Able-Bodied (AB) Observations................................................ 128 Technology - The Eye-gaze Response Interface Computer Aid

130

Travel Recommendations and Suggestions When Using the ERICA System...................................................... 131 Difficulties Using the ERICA System after Data Acquisition... 134 Movement Violators.......................................................... 134 Lookzones........................................................................... 136 Data Analysis...................................................................... 137

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Limitations of this Research

138

The Videotape Test Film................................................... 138 The Testing Environment.................................................. 142 Implications for Future Research.................................................. 147 Conclusion.......................................................................................153 References.............................................................................................................................156 Tables....................................................................................................................................163 Figures...................................................................................................................................189 Appendices........................................................................................................................... 202 A

Protocol for a Project Submitted for Review to the Institutional Review Board for the Behavioral Sciences at the University of Virginia........................................................................................................ 203

B

Consent Form Signed by the Participants of the Study............................. 212

C

General Information Questionnaire for Demographic Information on Participants.............................................................................................215

D

Statement of Instructions Read to Participants before Testing................. 218

E

Concluding Questionnaire Completed by Participants at the End of Testing......................................................................................................... 220

F

Summary Sheet of each Participants Gaze Trail Data............................... 222

G

Follow -up Questionnaire for WC Predictive and WC Real Tim e

Group Participants...................................................................................... 224 H

The Eye-gaze Response Interface Computer Aid (ERICA)..................... 227

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List o f Tables

Table 1.

Terminology................................................................................................ 164

Table 2.

Tables as they Relate to Corresponding Descriptive Variable Statements.................................................................................................. 165

Table 3.

Summary of Eye Movement Recording Devices Using Reflection in Tennis.....................................................................................166

Table 4.

Male Wheelchair Tennis Participants, Included in the Final Analyses, Wheelchair (WC) Predictive or WC Real Time Group, Ages, International Tennis Federation Rankings, Years of Playing Experience and Disability Category Between August 31st, 2002 - October 1st,

Table 5.

2003...........................................................168

Female Wheelchair Tennis Participants, Included in the Final Analyses, Wheelchair (WC) Predictive or WC Real Time Group, Ages, International Tennis Federation Rankings, Years of Tennis Playing Experience and Disability Category Between August 31st, 2002 - October 1st,

Table 6.

2003...........................................................169

Female Able-Bodied Participants, Included in the Final Analyses, Ages, Women’s Tennis Association Rankings and Years of Tennis Playing Experience Between August 3 1 st, 2 0 0 2 - O ctober 1st,

Table 7.

2 0 0 3 ....................................................................... 170

Male Able-Bodied Participants Included in Final Analyses, Ages, Professional Tennis Association Rankings and Years of Tennis Playing Experience Between August 31st,

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2002-O ctober 1st, 2003...................................................................171 Table 8.

Participants who are Able-Bodied or Wheelchair Players, Excluded from Final Analyses, Ages, Rankings and Years of Tennis Playing Experience Between August 31st, 2002 October 1st, 2003........................................................................................172

Table 9.

Average Number of Fixations and Fixation Durations in Total and Within Lookzones for Able-Bodied and Wheelchair Groups........................................................................................................ 173

Table 10.

Average Number o f Fixations and Fixation Durations in Total and Within Lookzones for Wheelchair (WC) Predictive and WC Real Time Groups.......................................................................174

Table 11.

Average Number of Fixations and Fixation Durations for each Lookzone for Able-Bodied and Wheelchair Tennis Groups...........................................................................................................175

Table 12.

Average Number of Fixations and Fixation Durations for each Lookzone for Wheelchair (WC) Predictive and WC Real Time Groups........................................................................................176

Table 13.

Average Number of Fixations and Fixation Durations for Temporal Phases of Able-Bodied and Wheelchair Tennis Groups..........................................................................................................................177

Table 14.

Average Number of Fixations and Fixation Durations for Temporal Phases of WC Predictive and WC Real Time

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Groups...........................................................................................................178 Table 15.

Average Number of Fixations and Fixation Durations for Temporal Phases and Lookzones of the Able-Bodied Group............................................................................................................179

Table 16.

Average Number of Fixations and Fixation Durations for Temporal Phases and Lookzones of the Wheelchair Group............................................................................................................180

Table 17.

Average Number of Fixations and Fixation Durations for Temporal Phases and Lookzones of the WC Predictive Group............................................................................................................181

Table 18.

Average Number of Fixations and Fixation Durations for Temporal Phases and Lookzones of the WC Real Time Group.................................................................................................. 182

Table 19.

Average Pursuit Tracking Time for Able-Bodied and Wheelchair Tennis Groups within Lookzone B (the Ball)...................................................................................................... 183

Table 20.

Average Pursuit Tracking Time for Wheelchair (WC) Predictive and WC Real Time Group Participants within Lookzone B (the Ball................................................................................................................184

Table 21.

Concluding Questionnaire Responses by Group of Tennis Players.............................................................................................. 185

Table 22.

Follow-up Questionnaire for Wheelchair (WC) Predictive and WC Real Time Groups......................................................................... 187

xv Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

List o f Figures

Figure 1

The Visual Search Process Adapted from Abernathy (1988, p. 107).............................................................................190

Figure 2

The Eye-gaze Response Interface Computer Aid....................................... 191

Figure 3

The Eye-gaze Response Interface Computer Aid - camera.......................192

Figure 4

Anatomy of the Eye...................................................................................... 193

Figure 5

Fixation on Irrelevant Location - a Distraction.......................................... 194

Figure 6

Lookzones Created by the ERICA System................................................. 195

Figure 7

Out of Alignment Gaze Trail - Top Third of the Screen........................... 197

Figure 8

Out of Alignment Gaze Trail - Left............................................................199

Figure 9

Out of Alignment Gaze Trail - Right......................................................... 201

XV|


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CHAPTER I Introduction

What makes a professional athlete an elite performer is of great interest to coaches, teachers, scholars, athletes, and most sport enthusiasts. Professional (expert) athletes are admired for their physical qualities such as strength, speed, and coordination and can also be admired for qualities that are less evident, such as being in the right place at the right time (anticipation) and for an ability to “out smart” an opponent strategically. These more subtle qualities are indicative of a proficiency in cognitive understanding (Abernathy, 1988). Research has found professional (or expert) athletes from a variety of sports have the most efficient and effective cognitive processing compared to less skilled athletes (Abernathy, 1990; Abernathy & Russell, 1987; Goulet, Bard & Fleury, 1989; Shank & Haywood, 1987; Ward, Williams & Bennett, 2002). One indication of cognition is evidenced by where one looks in order to detect and utilize the most important information in a sporting environment (Abernathy, 1988). Eye movements reflect where a person is looking and searching in the environment, referred to as visual search (Abernathy, 1988). Visual search patterns are not random but instead are learned responses to environmental stimuli (Fuchs, 1962). Research has found optimal visual search and selection patterns develop through experience and are different for experts and novices (Fuchs). For exam ple, soccer experts look at a kicker's hip to

accurately determine and quickly react to the direction where the ball is going. Novices tend to focus on the ball causing a longer time to make a decision and to initiate a movement (Williams, Davids, Burwitz & Williams, 1993).


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Typically, experts show systematic visual search patterns from one viewing to the next (Williams, Davids & Williams, 1999) and repeatedly look at the same locations to detect information (Tenenbaum, Levy-Kolker, Sade, Liebermann & Lidor, 1996). Research has found that experts selectively attend to the most salient aspects consistently when watching the sport in which they are proficient (Starkes, 1987). The visual search patterns of expert tennis players enable them to produce significantly higher numbers of correct responses regarding the type of spin and direction of the ball from the serve than do athletes of less experience. Experts also report significantly higher levels of confidence in their responses regarding spin and direction than novices (Tenenbaum et al., 1996). The visual search patterns of experts in tennis also enable them to initiate a movement faster than novices. For example, Singer, Cauraugh, Chen, Steinberg, and Frehlich (1996) found significantly faster motor response times of experts compared to novices when anticipating the direction of an oncoming ball from an opponent's serve. The quality o f motor responses has also been found to differ when less effective visual search patterns are used between players of similar skill level. Singer et al. (1998) tested the quality (depth and accuracy) of service return between college level tennis players while measuring their visual search patterns on the tennis court. Results revealed that the players with less salient visual search behaviors were judged lowest in quality of service return. H ence, the cognitive understanding experts have when watching a skill, is

evidenced via different visual search strategies that seem to provide them with an ability to anticipate direction and spin on a ball more accurately than those with less experience


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and in turn less effective visual search. This capacity has also been shown to relate to faster motor responses of a higher quality in tennis. Although some research has provided evidence of where experts look compared to novices when returning tennis serves, several limitations still exist. When conducting research, it is important to acquire accurate samples of the data of interest. When measuring visual search patterns of experts it is important that the sample is indeed “expert” as future education and interventions will be based on information gleaned from professional (expert) models visual search patterns. Research to date has used the term “expert” in tennis for a variety of different skill levels. For instance, Tenenbaum et al. (1996) determined experts to be professional players with an official Association of Tennis Professionals (ATP) ranking (not limited in range) where as Ward et al. (2002) defined experts as club level players with an average of 11.9 yrs of experience. It is important for the expert to currently hold the status of “expert” as the game of tennis is constantly changing due to the advancement of technology and changing techniques and biomechanics (Roetert & Groppel, 2003). Therefore, as an extension of current research, this study utilized a sample of tennis professionals who were internationally ranked within the last 12 months by the governing body of their sport. For able-bodied participants, rankings ranged from 4 4 -1 1 0 0 on either the Women’s Tennis Association (WTA) or Association of Tennis Professionals (ATP) (for men) ranking systems. For wheelchair tennis players, rankings ranged from 1 - 250 on the International Tennis

Federation (ITF) tour. All research conducted thus far has been conducted on people without disabilities. People with disabilities who engage in sport are an ever-growing population (British


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Wheelchair Sports, 2003). The year 2000 Para-Olympics in Sydney produced the largest crowd of spectators of the event to date (ITF, 2003). One of the most watched events in the Sydney Para-Olympics was wheelchair tennis (Paralympic Tennis Association, 2003). Sport for the disabled is growing in popularity and therefore there is a great need for research on people with disabilities in order to benefit their future performance in sport. The United States Tennis Association (USTA) has encouraged an inclusive environment for able bodied and wheelchair tennis players. For instance, now all tennis rules and regulations are presented together in an inclusive handbook titled the United States Tennis Association - Tennis Rules and Regulations (USTA, 2003). This rightful inclusion of people with disabilities into the elite sporting arena leads to an important question. Do expert wheelchair tennis players have the same visual search strategies as able-bodied expert tennis players when watching an opponent serve? The primary aim of this research was to provide some answers to this question. Another extension of current research provided by this study includes the exploration of the use of a new technology to determine visual search strategies: the Eyegaze Response Interface Computer Aid (ERICA) system. For visual search research where eyes are being tracked over video screens, effective, accurate, precise and timely technology lends credence to the results in an important and direct manner. The ERICA system is the most accurate eye movement recording system available, with a 0.5 degree of visual angle and the highest rate of tracking (60 fps) conducted in any sport (Lankford, 1999). It is extremely precise and calibrates the eyes over more areas of the display than any other system (Lankford). The ERICA system is noninvasive and requires no attachments to the head leading to an increase in validity of results as there is no external


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distractions. The software used in conjunction with ERICA’s hardware enables data to be manipulated (calculated) so that researchers are not searching for information on a frame by frame basis that can lead to human error. This technology adds to the reliability of data used in visual search research. These factors are extremely important especially when tracking eye movements of high-level experts who are watching movements and balls that are extremely fast. The ERICA system has not been used in any sport environment, hence this study uses ERICA for the first time to explore its application in a new setting. Thus, the primary purpose of this study was to examine a population of tennis players not previously examined (wheelchair tennis players) with regards to visual search variables, specifically the temporal and fixation locations when returning a tennis serve. Two extensions to the current body of knowledge in visual search were also investigated, including the utilization of a truly “elite” group of athletes who were current high-level professional tennis players (both able bodied and wheelchair players) and the utilization a new piece o f eye tracking technology (ERICA) in the sporting arena. The potential value of visual search research is broad. Recording eye movements to understand what people are attending to (or at least focusing on) when performing a skill is the first step towards a larger picture in terms of understanding visual search strategies. When research helps to fully understand what is being attended to, the next step will be to teach athletes to become more proficient in their visual search strategies. In future research it will be important to design and test potential interventions that might enhance the visual search strategies of athletes with varying skill levels. One procedure for doing this is to educate and train people so they have a greater understanding of what


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is looked at when a skill is being performed. Other strategies may be to add verbal cues to help direct attention to the relevant aspects of the skill, to provide selective visual information during the learning process, and/or to systematically reinforce the location and duration of visual search strategies. In the sporting world, the potential of visual search research can help tennis athletes of all levels decrease response times to oncoming balls, predict directions and locations of balls earlier, leading to more time for making effective strategical decisions. This is particularly important for wheelchair tennis players who have less time to respond due to mechanical aspects of movements that are constrained by their wheelchairs. Understanding the effective use of visual search can assist people with and without disabilities who play tennis and who are at differing levels of skill proficiency to improve and enjoy their sport and in turn to continue an activity that provides health and social benefits. The visual selective attention strategies specifically examined include the number of fixations, fixation durations, pursuit tracking and fixation locations during temporal phases of the tennis (see Table 1 for definitions of these concepts). As wheelchair tennis players have not yet been examined no predictions regarding their visual selective attention strategies are made. Limited past research of able-bodied tennis players (Goulet et al., 1989; Singer et al., 1996, Singer et al., 1998; Tenenbaum et al., 1996) with differing levels of experts examined, also makes it difficult to provide predictions regarding the results for this group o f participants, however, this limited research provides certain expectations. During the ritual phase it was expected that able-bodied players would focus on the general body position (Goulet et al., 1989), and during the preparatory phase players


would fixate on the non-dominant arm and ball toss (Goulet et al.) During the execution phase the arm, racquet and shoulder area would be fixated on based on research by Goulet et al. During the finishing phase it was expected that able-bodied players would focus on the ball, according to past research by Singer et al. (1998).


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CHAPTER II Review of Literature

Professional athletes have many qualities that make them exceptionally successful. These attributes are of great interest to coaches, teachers, scholars and sporting enthusiasts. Athletes at the pinnacle of their sport are admired for many of their physical qualities such as strength, speed and coordination. Michael Jordan, a professional basketball player, was admired for such qualities along with his quickness and agility. Professional athletes can also be admired for qualities that are less evident, such as being in the right place at the right time (anticipation) and for the ability to “out smart” their opponent strategically. Martina Hinges, a former professional tennis player, was admired not as much for her physical talents but for her ability to win matches by anticipating and strategically outsmarting more physically formidable opponents. Clearly, a combination of physical and psychological traits are necessary for a professional athlete to be successful. Visual focus o f attention The more subtle qualities of excellence are likely the result of proficiency in perception, attention and focus that assist in expert performance of a sport. Actions require adaptation from expert performers who continually interact and adapt to environmental changes. “In order to be successful in perceptual motor tasks, the performer must not only be capable of detecting the complexities of the surrounding visual display, but also be capable of selecting out, from this visual display, only the most pertinent information for detailed processing” (Abernathy, 1988, p. 210). For instance, a


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professional soccer player looks at an opponent’s hip and thigh (and does not focus on the ball) as the opponent passes the ball (Williams & Davids, 1998). This visual search assists in determining the direction of a kick. “The professional athlete directs attention to specific features of the environment and to action preparation activities” (Magill, 2001, p. 133). The directing of attention is known as attentional focus and visual selective attention is specifically concerned with the role vision plays in motor skill performance and in directing visual attention to environmental information. A performer’s focus of attention is part of the total visual process that includes not just focus but all input acquired by the performer through visual search. “The term visual search is used to describe the process of directing visual attention to locate relevant environmental cues” (Magill, 2001, p. 129). Effective visual searching can enable a person to gain essential information in order to determine how to prepare and ultimately perform a skill in a specific situation because it enables earlier predictions and accurate movement responses. For instance, an experienced baseball batter can determine the direction and spin of a pitch significantly earlier and more accurately than an inexperienced batter (Shank & Haywood, 1987). He is able to do this by visually searching the pitcher’s motion and attending to important aspects of that motion that give clues to the type of pitch and spin with which it will be delivered. What makes the professional tennis athlete able to visually search with such efficiency? What directs the athlete’s attention in order to pick salient cues? Both questions are important issues in visual search research and need to first be investigated through an understanding of the theories of attention and visual search.


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Theories o f Attention and Visual Search It is important to understand how athletes visually search within their environment and select certain features as this leads to specific attention that is allocated to relevant areas of a display (Abernathy, 1988). The visual search process includes two stages (see Figure 1, adapted from Abernathy, 1988, p. 207). When information is received from the environment it is not initially filtered or attended. The visual information is simply detected in an initial capacity-free, preattentive process (Stage 1). This visual information is probably held there, very briefly in a literal form, as a visual sensory icon (Abernathy, 1988). Selected items from this sensory icon are then subjected to more detailed analysis (Stage 2). The questions of particular interest to this research are what items remain for further analysis (verses those that are filtered out) and why are certain items attended to further? To answer these questions it is first important to understand the process of how items “grab” the attention of athletes. To form an understanding of attention from a theoretical perspective Treisman and Gelade’s (1980) Feature Integration Theory will be discussed. To provide an alternative view, Similarity Theory (Duncan & Humphreys, 1989) will also be introduced. Movement Filter Theory is an extension of both the Feature Integration and Similarity Theories specifically accounting for movement in a visual display. Due to the nature of the current research this theory will also be examined and related to Feature Integration and Similarity theories and then interpreted to the domain of sport. Feature Integration Theory (Treisman & Gelade, 1980) Feature Integration Theory was originally proposed by Treisman and Gelade (1980) and updated in 1988 by Treisman. This theory postulated that when people


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visually search their environments they group together objects and features that have unique properties, such as by color or shape. For example, a screen that has the letters A and B presented will be visually integrated into a group of A’s and a group of B’s. Features that are of a common shape or size are “grouped” together. After this grouping of features, “maps” are formed that relate to the various features. For example, the map may identify that the A ’s are around the periphery of the visual display and the B ’s are concentrated in the center. Dependent upon the task demands, Feature Integration Theory suggests that the “maps” form the basis for further search processing and cue identification. For instance, the goal of the A/B task may be to identify the one B that is smaller than the others. After identifying where the B’s are located within the display a more intensive search within the center of the display would reveal the B that is slightly smaller than the other B’s. “When attention is focused on a particular location in the master map it allows automatic retrieval of whatever features are currently active in that location” (Treisman, 1988, p. 203). The selection of interesting areas within a visual display occurs when a person focuses his “attentional spotlight” on those features. The spotlight can cover a wide or narrow area and can even be divided to cover different pertinent areas of a display. For instance, in the previous A/B task, where the goal was to find the smallest B, the attentional spotlight would be directed to the group of B’s in the center of the visual display. When a person predetermines and specifies (either consciously or unconsciously) a goal when searching within an environment that has a distinct feature, then the visual


goal may “pop out” due to its distinctiveness among other features. For example, when searching for a person wearing a red coat within a picture, this person is found more quickly than if the cue of a red coat had not been provided, as this feature becomes the goal of the “attentional spotlight.” The red coat will further “pop out” if all the other people in the picture are wearing a blue coat, as the “redness” of the coat becomes even more unique due to the contrast in colors. “Thus, the more distinctive the feature is that identifies the target of the visual search, the more quickly a person can identify and locate the target” (Magill, 2001, p. 132). Treisman’s original research (Treisman & Gelade, 1980) focused on visual search for stationary objects that in everyday situations assist in our understanding of successful prehension (reach and grasp) motions, such as picking up a cup from a table, as well as for reading or looking at a static picture. Such research helped the understanding of preparation and initiation of movements toward, and searches of, stationary objects and provided a starting point for understanding attention. Feature Integration Theory also helped explain how individuals search their environment more effectively based on predetermined areas of interest, for instance, when looking for a friend among a group of people. Individuals focus on the features they know the friend possesses and ignore other strangers who are not the goal of their search. Feature Integration Theory also provided an understanding of how humans can process the large volume of information that comes in through their visual system s. B y discarding irrelevant pieces of visual information and

focusing on features that are important, the human information processing system reduces the cognitive overload that would occur should we have to attend to all features presented. Treisman also provided an important extension of the original theory


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(Treisman & Gelade, 1980) to moving objects in a dynamic environment (Treisman, 1988) which will be discussed later in this chapter. Similarity Theory (Duncan & Humphreys, 1989) Duncan and Humphreys (1989) offered an alternative theoretical framework for understanding visual search of static items - the Similarity Theory. Their theory proposed two factors that influenced the way in which information from the environment can be rejected and effectively discarded or ignored in order for more salient features to be accepted. These involve the similarity of the target (based on salient features) to the nontarget (or irrelevant feature). “If individual nontargets are dissimilar to the target, they have a lower weight in the competition, and so performance improves” (Duncan & Humphreys, 1989, p. 438). For example, in a picture of the beach with yellow sand, a blue sky and nothing but a large red ball in the water, if the target of the display is the ball, then it will be found with ease because the yellow sand and the blue sky are in contrast with the red ball and therefore provide less competition when searching for the ball. When nontargets are similar to one another performance can also improve. “If nontargets are similar to each other, their weights are linked so that when one is rejected, the weights of linked nontargets are lowered, and performance improves” (Driver & McLeod, 1992, p. 24). For example, when looking at a picture of a flower with a brown center and yellow petals and the target is to find the core of the flower, this would be easy as the core is a different color from the petals and the petals are similar in color so they can be grouped and discarded more easily.


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The Similarity Theory (Duncan & Humphreys, 1989) is useful in explaining how individuals search for stationary objects and displays, much like the Feature Integration Theory. However, in elite sporting environments athletes need to be able to attend to rapid movement situations, and such an explanation was provided by McLeod, Driver, Dienes and Crisp (1991) in their Movement Filter Theory. Movement Filter Theory (McLeod et al., 1991) Feature Integration Theory (Treisman & Gelade, 1980) and Similarity Theory (Duncan & Humphreys, 1989) offered general accounts of visual search that accommodated a wide range of static data. In contrast, the Movement Filter Theory was developed specifically to account for visual search in moving displays. McLeod, et al. (1991) proposed the movement filter as a subsystem in visual search that would allow visual attention to be directed only at the movement items within a visual display while effectively ignoring static (irrelevant) features. Evidence for the movement filter was tested in a series of experiments by McLeod, Driver and Crisp (1988), McLeod et al. (1991) and Driver and McLeod (1992). Overall, their results revealed that it was easy for participants to direct attention to “just the moving objects in an array of moving and stationary stimuli, even if the moving stimuli move in different directions and thus do not form a single group defined by a common fate” (Driver & McLeod). For instance, participants were required to search for an X moving in different directions among intermingling O’s moving in different directions and stationary X ’s. The visual system was able to separate all the items that were moving from those not moving and the moving X from the moving O. However, attention cannot easily be directed to both moving and stationary stimuli simultaneously.


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For instance, when having to pick out one target that remains stationary and another that is moving as in the moving X ’s and stationary X’s example. Altogether, the Movement Filter Theory suggested that some part of the visual system acts as a movement filter that allows attention to be directed towards parts of the visual scene with a particular movement characteristic. “It can separate movement items from a stationary background or it can separate stimuli moving in a particular direction from items moving in a different direction” (McLeod et al., 1991, p. 63). The ability to filter perceptual information is essential for human survival and for excellence in sport competitions (McLeod et al., 1988). One of the most important responsibilities of the visual system is to ensure that focal attention is directed to salient aspects of the environment. The success of the human race may well depend on an individual’s ability to attend to objects that are moving because they may represent food or danger. “Activity in the movement filter would represent the presence of movement within the environment even if attention was currently directed elsewhere” (McLeod et al., 1991, p. 63). Although not the focus of this current review, there is neurophysiological evidence for a movement filter (for more information see Albright, 1984; Allman, Miezin, & McGuinness, 1985) and the concept of filtering information is also well known in the field of cognitive psychology (e.g., Broadbent (1958) Information Processing Model). The movement filter is also related to Treisman’s Feature Integration Theory (Treisman & Gelade, 1980) in that perceptual grouping occurs based on either shape or form and movement becomes yet another subsystem of the “grouping” phenomenon. McLeod et al. (1991) proposed that grouping and a movement filter are not mutually


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exclusive, but instead they operate on different levels. “Grouping offers a description of phenomenology; the movement filter is a mechanism that underlies the phenomenology o f search in moving displays” (McLeod et al., p. 55). In 1988 Treisman extended and updated the original theory (Treisman & Gelade, 1980) accounting in part for movements presented within a visual display. The key modification to Triesman’s original work is that if a feature is particularly important, individual feature “maps” can inhibit nontarget locations on the master “map” so that they will not be attended to during search. For instance, static X’s in a display where a moving feature is the target will be ignored or inhibited. Movement Filter Theory as it Relates to Other Theories o f Attention Theories of attention in movement (the Feature Integration Theory and Similarity Theory) agree with the following sequence of events, even if the proposed mechanisms are different. For example, when movement is a subsystem (or feature) of the entire visual field (including the master map), static A’s would be excluded from a search for a moving A among moving B ’s and static A’s. As attention is called to the moving items among the static items, the task becomes one of searching for a salient feature (the moving A) among a subset of stimuli that are moving (the moving B’s). All o f these theories agree that the search for a moving A among moving B’s and stationary A ’s is easy and this parallel search arises because it can be directed to just the moving items (Driver & McLeod, 1992). So the task becomes one of searching for a particular feature among the subset o f stimuli that are moving. Theories also agree that visual search is more difficult when the target is stationary rather than moving because directing attention solely to the moving items within a stationary environment is easier


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than directing attention solely to unique stationary items. For instance, the target of a stationary X among stationary Os and moving Xs leads to a slower search which becomes even slower as the visual display becomes larger. As mentioned previously, the proposed mechanisms for such searches are different in different theories. The movement filter theory suggested that the moving items can be represented in a visual pathway that is insensitive to static stimuli (Driver & McLeod, 1992). Treisman (1988) suggested that the stationary items are repressed (or the movement is excited) on the master map. The Similarity Theory suggested that “the stationary items have a relatively low selection weight for entry into visual short term memory” (Duncan & Humphreys, 1989, p. 438). Theories o f Attention and Their Relationship to Sport Understanding how individuals direct their attention to features and movement within a visual display is important for the sporting arena. As previously mentioned, in the sporting environment, athletes need to be able to visually search and attend to salient features during rapid movement situations in a timely fashion, in order to accurately predict and then respond to an opponents action. Abernathy (1993) described research evidence to demonstrate that in sports involving fast ball and racquet action, such as tennis, skilled players visually search the environment for the “minimal essential information that is necessary to determine an action to perform” (p. 213). In effect, the attentional spotlight is focused on what is salient to determine where a ball m ay be directed. The minimal essential information an individual needs to be effective in sport has been shown to be from movement rather than static features of the environment (e.g., the


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motion of an opponents arm or racquet; Goulet et al., 1989). “Additionally, minimal essential information focuses the attentional spotlight on the invariant perceptual features o f a situation” (Magill, 2001, p. 132). An example would be the extending of a tennis server’s arm towards the ball/racquet contact. Minimal essential information is also “detected through coordination kinematics of an opponents action, which involves the grouping of displacement characteristics of joints involved in a coordinated movement pattern” (Magill, 2001, p. 132). An example is the relationship between the arm, racquet and shoulder during the performance of the tennis serve. The more skilled a person becomes, his or her visual attention (or attentional spotlight) becomes increasingly attuned and focused to detecting the important kinematic features, which provides the skilled player an advantage over the less skilled player in anticipation of the opponent’s action. Skilled tennis players focus on the arm, racquet and shoulder region of an opponent when watching the serve as they (the receiver) are preparing to strike the ball; however novice tennis players tend to focus on an opponent’s head (Goulet, et al., 1989). In effect, the minimal essential information “pops out “ for the skilled player and directs the player’s visual attention as he prepares to respond to his opponent’s action. In sporting environments, the most meaningful cues “pop out” and become evident to the expert performer (Magill, 2001). As a person experiences performing in certain environments, critical cues (goals) for successful performance are invariant, that is, they do not change and these pertinent features occur each time a skill is presented (Abernathy, 1993). Paying specific attention to the invariant features increases the meaningfulness and pertinence often without a person’s conscious awareness (attentional


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spotlight) (Abernathy, 1993). Instruction plays a crucial role in the way certain features become more important than others (Magill). Coaches and teachers can facilitate and direct attention to pertinent areas of a display in order to assist in learning and attending to anticipatory cues. The Visual Search Process As mentioned previously, the questions of particular interest to this research were what “grabs” an athlete’s attention (i.e. where does s/he fixate), what items remain within his/her attentional spotlight (i.e. how long are the fixation durations), in contrast to those that were filtered out, and why were certain features attended to further? To answer these questions, it was first important to understand how items are selected for attention, through the prominent theories just discussed. However, selective attention was considered as just one of the components of the visual search process. A second element is visual search, specifically concerned with the continuous selection and guiding of information from preattentive (visual detection) to focal attention and cue utilization (see Figure 1, Abernathy, 1988, p. 107). It is important to understand the entire visual search process in order to understand how visual attention facilitates cue usage. Also important is how these cues are used to assist in predicting outcomes of an opponent’s action. There are several theoretical models of the visual search process (Neisser, 1967; Schneider, 1976; Shiffrin & Schneider, 1977; Swensson, 1980; Williams, 1967) that share many commonalities even though terminology may differ. Theories of visual search generally conceive the process as involving two stages; the collection of visual information, and focal attention cue utilization.


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Stage 1: The Collection o f Visual Information The first visual search stage of processing is referred to in a variety of terms. For instance, the pre-attentive process according to Neisser (1967), the acquisition process according to Williams (1967) and the automatic search process according to Schneider (1976) to name just a few. The terminology is somewhat inconsequential as the prominent theories are in agreement with many of the characteristics of this first stage, that is, the first stage is generally characterized as receiving a large volume of information because it allows for parallel processing. The first stage is nonattention demanding and is stored in literal form. This visual sensory information is available to the performer for only a very short period of time. It decays rapidly in the absence of attention. During this stage very crude feature analysis is undertaken, for instance, the general representation of a solid object verses an outlined feature. The results of the initial analysis of the environment lead to the environmental input that will be worthy of further selection (visual selective attention), a more sustained form o f attention found in stage two of the information processing model (see Figure 1, Abernathy, 1988, p. 107). This first preattentive stage, where visual information is collected is an essential stage in skilled performance. However, what is most pertinent involves a concern with determining what information performers regard as sufficiently pertinent to commit to more detailed focal attention. It is the examination of cues reaching the second focal attentive stage that provides insight into the perceptual strategies that characterize elite performers. Stage 2: Focal Attentive Cue Utilization in the Visual Search Process


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In contrast to the first stage of the visual search process, the second stage requires a more detailed analysis (Williams, 1967). The crude feature analysis that initially occurs leads to decisions about what is next selectively attended to and focused on. For instance, the solid object seen in the first stage is recognized to be a box and the outlined object is the letter “B.” The particular feature that captures a person’s focus is seen to serve as a cue or predictor of action. For example, in tennis, when a person focuses on the arm, racquet and shoulder area of an opponent, it is thought that this selection within the visual display is important to help predict the direction in which a ball will be hit. Hence these areas are selected and attended to for a longer period of time and essentially used as predictive cues (Abernathy, 1993). Serial processing is expected to take place during this stage and can be seen through devices which record eye movements (eye movement recording devices are discussed in detail later in this chapter). An experienced participant will move his eyes and focus foveal vision in only one or two areas he deems to be important at any one time. For instance an experienced tennis player when watching an opponent serve will focus on the ball during the toss, then the arm, then the racquet and back to the ball after contact. Only one cue is attended to at a particular point in time. Some theorists refer to this second stage of the visual search process as a “controlled search” (Schneider, 1976; Shriffin & Schneider, 1977) as the performer is now controlling specific areas on which to focus. Other names for this second stage include the “focal attentive process” (Neisser, 1967), or the “identification process” (Williams, 1967). As previously mentioned, the terminology for the process is really inconsequential. What is important is the extent to which researchers agree with the


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specific events that characterize the stages. The general conception of the second stage of the visual search process is a representation of the aspects within a visual display that a performer has elected to pay specific attention to. These focal attentive aspects of visual search lend themselves to more direct examination. Researchers in a variety of domains including ergonomics, education, and sport have tried to further examine the visual selective attention aspects of visual search, in order to gain a better understanding of where people are focusing. To capture and examine this information a variety of eye movement recordings devices have been utilized. Methods o f Measuring Visual Selective Attention Measuring where the eye is looking presents a challenge in technology. Carpenter (1977) described the ideal piece of technology for measuring movements of the eye. An ideal system should be able to measure rotations of the globe about all three axes, yet be completely insensitive to translational movements; linear over a range of more than 90 degrees, yet sensitive enough to record micromovements of a few seconds of arc; and have a bandwidth extending from zero to a few hundred Hz. The device must not interfere with vision - indeed ideally should not even be visible to the subject - and be unaffected by movements of the head, or light enough that the subject can wear it rigidly fixed relative to the skull (p. 309).


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Unfortunately, to date, no system has been able to perform all of Carpenter’s recommendations with accuracy and reliability over a broad range of activities. However, technological advancements continue to be made at a rapid rate. Eye movement recording devices track where the eyes move and stop over a visual display. The term “eye movement” is somewhat paradoxical, as what is most important to researchers are the stopping points (fixations) rather than the eye movements (saccades). Eye movement recording devices provide a method from which implications can be drawn regarding a participant’s focal attention. The second stage of the visual search process (previously described) occurs after visual selective attention reduces the volume of irrelevant information and now only more “interesting” aspects of the visual field are examined requiring focused attention. The highest acuity areas of the human eye the retina or fovea only extend for about two and one-half degrees of vision around the central focal point (Lankford, 1999; Rayner, 1978). Hence the eye must be moved in order to extract detailed information from different areas of a visual display. Where the eye stops and focuses in order to extract this information is interpreted by researchers as “areas of greatest current interest” (Gaarder, 1975, p. 57). The assumption made when using eye movement recording devices is that the locations of fixations reflect visual selective attention that leads to cue utilization (Abernathy, 1988). This section reviews methods of measuring eye movements. To begin, a brief review of devices used outside of the sporting arena is examined in order to gain an understanding of the history and technological progress made in eye movement technology and an understanding of the steps still needed for an “ideal” system. Devices


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have included direct viewing, photoelectric viewing, and reflection from attachments to the eye. Following this introduction, the focus is on methods of measuring eye movements that have been used in the sporting arena, specifically devices using reflection from the cornea and recalling information from stimulus presentations. Eye Movement Recording Systems Used Outside the Sporting Arena Devices used outside the sporting arena have included direct viewing, photoelectric viewing, and reflections from attachments to the eye. These methods of assessments are reviewed first. Direct viewing. Observing a participant’s eyes without the use of any optical aid has been used for preliminary examination in clinical settings, for instance, in detecting nystagmus, gaze paresis and other obvious signs (Carpenter, 1977). One advantage of this method of assessment is its noninvasiveness. Direct viewing is only adequate for preliminary clinical examinations because it provides only a crude assessment. Photoelectric viewing. Photocells look at the high contrast between the white sclera and the darker iris at the limbus. In its simplest form a spot of light can be projected on the limbus, with a nearby photoresistor that picks up the scattered light and tracks the eye movement (Carpenter, 1977). Photoelectric viewing has been used when measuring eye movements of animals and people in psychological experiments particularly throughout the 1960’s and 1970’s (Carpenter). Photoelectric viewing is more accurate than direct viewing. However, there are several disadvantages of this method including its limitation for measuring horizontal eye movements only, since the vertical direction of the limbus is covered by the eyelids. It is also restrictive, as the participant is


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required to wear headgear in order to prevent the slightest movement of the head that could lead to a misrepresentation of eye movements. Reflection from attachments to the eye. Fitting the eye with a contact lens that has a small plane mirror mounted on it allowed for the first photoelectric records of eye movements (Carpenter, 1977). The disadvantage was that it was an invasive procedure that caused discomfort for participants. On the other hand, compared to photoelectric viewing, movement and slippage of eye equipment was rarely a problem. Direct viewing, photoelectric viewing and reflections from attachments to the eye all pose considerable limitations, ranging from discomfort to inaccurate recordings. Such limitations limit the reliability of the data being collected. Technological advancements especially during the 1980’s allowed for another type of eye movement recording, that of reflections from the cornea of the eye. This method of assessment has provided a much needed tool for sport researchers to collect more accurate data while being relatively noninvasive. Eye Movement Recording Devices Usedfo r Sport Assessment Recalling focus o f attention. Tenebaum, et al. (1996) used a technique of recording focus of attention by having participants recall where they focused their eyes. Participants viewed a film at 25 fps of different tennis strokes and were asked to “mark the cues they observed prior to the film occlusion on an illustration of a tennis player holding a racquet” (Tenebaum et al., 1996, p. 297). This was the only study in sport that could be located that used a recall technique to collect visual search data. There are obvious problems with this method of assessment in terms of an increase in the cognitive


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load required for participants along with other methodological flaws (see limitations of past research later in this chapter). Eye movement recording devices using reflection. Eye movement recording devices using reflection have recently provided a more reliable and valid technique of recording where participants are actually focusing. This technique utilizes the fact that the front surface of the cornea of the eye is shiny and convex and forms an image of an external point source (referred to as the first Purkinje image). This image lies 3.5 mm or more behind the corneal surface of the eye (depending on the distance of the visual image source). “As the center of rotation of the eye is not identical with the center of curvature of the cornea, when the eye moves the apparent position of this image will move as well” (Carpenter, 1977, p. 314). Reflection from the cornea and the eye movement is recorded by computer systems as coordinates of positions on the screen. These recordings are used for further statistical processing and analysis. There have been several different eye movement recording devices using reflection in the tennis literature: the NAC, Eye Movement Recorder, Model V, used by Goulet et al. (1989), the Applied Sciences Laboratories (ASL) Eye-Trac Model 210, used by Singer et al. (1996), the ASL 4000SU headband system, used by Singer et al. (1998) and the ASL 5000SU system used by Ward et al. (2002) (see Table 3 for a summary of these systems). The Eye Gaze Response Interface Computer Aid (ERICA) was used for the current study (see Table 3 for a summary of this system). These systems will be reviewed next. NAC, Eye Movement Recorder, Model V. Goulet et al. (1989) used the NAC Eye Movement Recorder, Model V, with an accuracy of one degree within horizontal and


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vertical ranges of +/- 25/20 degrees, for capturing fixation points of participants as they were watching a video of a person serve in tennis. Accuracy referred to the system’s ability to measure where the eye was actually pointed. The film viewed by participants was dynamic. Participant’s gaze (fixation points) were analyzed in a frame by frame fashion using a magnetoscope Panasonic AG-6100 that was connected to the NAC. Eye movements were analyzed semi-automatically, by sending from the videotape a 30-Hz signal (with a video sampling rate of 30 fps) to an Apple HE computer. A luminous cross was superimposed on the screen that specified an area within the screen where each fixation was located. For more specific locations of fixations, a frame by frame analysis was used whereby two raters (inter-rater reliability was r = .94 and intra-rater reliability was r = .96) coded the fixation cue within the zone based on predetermined codes. For example, A/B: referred to a fixation on the server’s arm holding the ball. Applied Sciences Laboratories, Eye-Trac Model 210. Singer et al. (1996) used the ASL Eye-Trac Model 210 for recording visual gaze information while seated participants watched videotapes of expert models perform either serves or groundstrokes. Both horizontal and vertical eye position data were collected by using phototransistors operating in conjunction with infrared light sources that were attached to a pair of glasses with no lenses. Data collected from the Eye-Trac were fed to a Metrabyte DAS-16 data acquisition board located in a Gateway 386 microcomputer. This computer was also fitted with a BCD-1000 video control system that continually read and coded the videotapes. Calibration of the eye to the system was conducted using a 9-point grid format presented to the participant in a clockwise direction.


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Applied Sciences Laboratories, 4000SU eye movement - headband system. Singer et al. (1998) used the ASL 4000SU headband system to collect visual search data. The primary purpose of their study was to test the equipment in an outdoor, live tennis playing situation where the participants were required to return a tennis serve as effectively as possible. “Using reflection from a near infrared light source from the cornea of the eye this video-based monocular system measured line of gaze with respect to a scene camera by computing relative positions of the pupil” (Singer et al., p.291). These positions were then transferred via a 30 m cable and processed by an external Gateway 2000 486 SX/16 microcomputer. Eye gaze was recorded from a cursor on the screen and saved for later analysis. The data sampling rate was 50 fps with an accuracy of +/- one degree of visual angle and a precision of better than lA degree in both vertical and horizontal directions. Precision refers to how consistently the system and the eye “agree” with where someone is looking. The 4000SU system was calibrated with a 9point reference grid. Calibration was retested every 3-5 serves to ensure the data were recorded accurately. Applied Sciences Laboratories, 5000SU Eyetracker System. Ward et al. (2002) used the Applied Sciences Laboratories 5000SU eye tracker and Ascension Technologies Flock of Birds magnetic head tracker (model: 6DFOB, Ascension Technologies Corporation, Burlington, VT). Movement within 1.22 m in any direction was recorded. The eye line of gaze was recorded by displacement between the pupil and the corneal reflection, position o f the eye in the head, and position and orientation of the head in space. In the Ward et al. experiment, participants viewed test films on a ‘life size’ screen (3 m x 3.5 m) at a distance of 5 m. According to the researchers, this paralleled “the


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visual angle experienced in a live situation to be reproduced (approximately 8.5 degrees)” (Ward et al., p. 108). Participants stood in a return of serve position and moved in the direction where they thought the serve was being hit. Calibration was conducted using a 9-point calibration grid overlaid onto the screen, and the system was accurate to one degree of visual angle. Eye movement recording devices using reflection in tennis: A summation. There are several advantages of eye movement recording devices. The first is that the “brightness of the corneal reflection and its contrast can be made quite distinct” (Carpenter, 1977, p. 315). This assists in tracking the eye especially over devices that transmit light such as computer and television screens. The second advantage of eye movement recording devices that utilize corneal reflection is that “motion in both horizontal and vertical plains can be recorded with ease” (Carpenter, 1977, p. 315). Hence, this provides a tool for determining the way in which the eye moves over natural visual scenes. Another advantage of this method for recording eye movements is that none of the equipment directly inserts in the eye, again allowing for more natural eye movements (Singer et al., 1998). The eye is also tracked (with proper calibration) to specific points within a display allowing for accuracy of a participant’s visual search. With current technological advancements, systems allow for tracking to occur in relative time which is obviously essential for the validity of results. Eye Response Technologies, Eye-gaze Response Interface Computer Aid (ERICA). The ERICA (2003, model 000-0-103) system is another eye movement recording system, that has not yet been used in sport but is a highly effective tool for measuring eye movements (Lankford, 1999). It contains hardware, a camera, which


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utilizes Light Emitting Diodes (LED’s) that project low powered infrared light onto the cornea of the eye. The software (Gazetracker) can identify where the person is looking by monitoring features o f the eye and tracking eye movement. These points are then translated into coordinates on the computer screen providing the raw data. The ERICA system has been used by people with disabilities to assist them in using the computer, for example, through eye movements the user can type and run programs (Olmeda, 2002). ERICA has also been used in studies conducted in education settings with children while assisting in reading and diagnosing Attention Deficit Hyperactivity Disorder (ADHD; R. Brigham, personal communication, October, 28, 2003). This system was employed for the current research because it’s noninvasive and user friendly (Lankford, 1999). Terminology o f data output from eye movement recording devices using reflection - Specifically the Eye-gaze Response Interface Computer Aid (ERICA). Visual search strategies, as interpreted through eye movement devices, include a variety of dynamic and static eye recordings. The terminology that these stationary points and eye movements represent are discussed next, in order to form a greater understanding of how eye movement recording systems represent visual selective attention. Fixations andfixation locations. When an eye stops within a visual display and remains stationary for a period of time (milliseconds) it is referred to as a fixation, usually ranging from 100 to 134 ms in sport research. Fixations reflect foveal vision and the underlying perceptual strategies engaged in by the participant. “Fixation location characteristics are assumed to reflect information about specific cues performers


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selectively attend to in order to extract task-relevant information” (Abernathy, 1988, p. 210 ).

Eye movement recording devices such as ERICA can capture fixations and where they are located. This provides a tool for researchers to aid in understanding the participant’s focal attention and assumed pertinence of a fixation point within the display. When does an eye movement become a stopping point (fixation)? Researchers in sport have determined fixation points to begin at anything from 100 ms (Williams et al., 1999) to 133 ms (Goulet et al., 1989) and points in-between (120 ms, Singer et al., 1998). In terms of videotape viewing, 100 ms are captured in 3 fps (on a 60 fps setting) and 133 ms represent 4 fps. Identification of fixations is “a critical aspect of eye movement data analysis that can have significant effects on later analysis” (Salvucci & Goldberg, 2000, p. 71). Fixation durations. Fixation durations simply refer to how long a person’s eye remains stationary at a particular point (fixation) within a visual display. Fixation durations are assumed “to reflect detail about the information processing loads faced by the performers” (Abernathy, 1988, p. 210). Generally speaking, the longer period of time a person fixates at a location within a display the more pertinent that location is assumed to be. Eye movement recording devices enable researchers to calculate how long a person fixates within a particular area of interest in order to assist in understanding the underlying perceptual strategy of the participant (Carpenter, 1977). Saccades. Saccades are defined by a “rapid shift of the gaze from one fixation point to another” (Shakhovich, 1977, p. 210). A saccade is the “most rapid movement of which the ocular system is capable” (Fuchs, 1971, p. 343). The purpose of a saccade is to


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redirect the eyes from one target location (fixation point) in the visual field to another in the quickest period of time. “Seeing” is not important during a saccade because visual functions are suppressed. That is, the movement of the eye during a saccade is of primary importance, not the obtaining of information from the visual field. Saccades are elicited by a number of stimuli that “grab” the attention o f the performer. For instance, objects of interest require voluntary saccades to position the fovea of the eye on the object deemed as pertinent. Hence, in most cases, it is not the saccade itself that is o f primary interest to researchers, it is the fixation where saccadic eye movements begin and end. Pursuit tracking. Pursuit tracking refers to the movement of the eye following an object within a dynamic visual display. A classic sport example of pursuit tracking is when a participant watches and follows a ball throughout a dynamic film. “These eye movements (pursuit tracking) assist in keeping the image within the zone of optimal vision” (Shakhovich, 1977, p. 314). The eye carries out a continuous assessment of the velocity and direction of a moving object in order to allow for smooth eye movements when tracking the object. Pursuit eye movements enable the eye to follow a moving area of interest such as the ball by keeping the ball relatively stationary on the fovea. While the focal point is on the ball, the background is moving at a velocity equal to the eye velocity. Pursuit tracking is important in sport research as it enables researchers to determine the pertinence of specific objects moving in space during temporal elements of the presenting skill. Limitations o f eye movement recording research. There are some limitations in eye movement recordings that need to be explicated. The first is that visual search and


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fixation points do not necessarily mean that actual perception or information extraction occurred at that point. This is a distinction between “looking” and “seeing.” For example, when another person that one is familiar with walks by in the shopping center we may see a person (object) moving by us, we may even “look” at them, but we may not recognize the person as being familiar if our thoughts and cognitions are elsewhere. “Looking” at the person and not recognizing them is different from “seeing” the person for who they are, as a friend for instance. Instead, it is the assumption of researchers through theoretical underpinnings that provide the link between eye movement recording devices (specifically fixations) and interpretation of cue utilization. These assumptions have been further validated through research that applies cue usage from visual search data. For instance, adding movement or verbal responses to stimuli, such as anticipating the direction o f an oncoming object (Singer et al., 1996). A second limitation, is that eye movement recording devices provide exclusively a measurement of focal vision. Peripheral vision cannot be measured directly. This is a limitation of technology to date as it is apparent that there is a powerful role being played from the periphery in the detection and control of movements within a visual display (see Paillard, 1980; 1982). There is also another technical limitation of eye movement recording devices. According to Abernathy (1988), “calibration of the recording devices is made difficult by nonlinearity in response to the subject’s eye mark for saccades made across the total range o f the visual field and by baseline drifts that accompany any extended recording of the subject’s visual search activity” (p. 212). Long term calibration of eye movement recording devices can be problematic. This problem can be overcome in a variety of


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ways. First, ensure accurate calibration at the beginning of testing by having a participant understand the importance of remaining still for eye movement recording devices such as ERICA (discussed specifically in chapter 3 and Appendix H). Second, keep assessment procedures such as videos short. Third, recalibrate a participant periodically, such as between video presentations. The limitations of eye movement recording devices need to be fully recognized and acknowledged. This is important in order for readers to be fully cognizant regarding conclusions that can be drawn about selective attention strategies “seen” through eye movement recording devices. However, given these technical limitations there is still much that can be learned from eye movements and the underlying visual search strategies o f participants. Sport and Expertise Characteristics o f Visual Selective Attention Research in sport has shown specific and consistent differences between expert and novice performers (e.g. experts in soccer look at the hip of the kicker to determine where the ball will be kicked, novices look at the head of the kicker, according to Williams and Davids, 1998) in terms of visual strategies. Differences in eye movement patterns, as measured by eye movement recording devices, occur between participants of varying skill levels. Understanding visual search patterns of expert performers is of primary interest as they provide a model for effective search of a sport environment that can, in future research, provide important educational and intervention strategies for less effective performers. Visual search patterns are not random but instead they are learned responses to environmental stimuli. Research has found optimal visual search and selection


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strategies/patterns develop through experience and are different for experts compared to novices (Fuchs, 1962). Visual search patterns have been examined by researchers in many sports, including soccer (Williams & Davids, 1998; Williams, Davids, Durwitz, & Williams, 1993), basketball (Allard & Burnett, 1985; Allard, Graham, & Paarsalu, 1980, Vickers, 1996), golf (Vickers, 1992), baseball (Chiesi, Spilich & Voss, 1979; Shank & Haywood, 1987), badminton (Abernathy & Russell, 1987), cricket (Abernathy & Russell, 1984), squash (Abernathy, 1990), volleyball (Borgeaud & Abernathy, 1987), field hockey (Starkes, 1987) and tennis (Goulet et al., 1989; Isaacs & Finch, 1983; Jones & Miles, 1978; Singer et al., 1998; Singer, et al., 1996; Tenenbaum et al., 1996). Experts have been shown to extrapolate certain stimuli specific within a visual display on a consistent inter- and intra-trial basis. For instance, experienced badminton players use relevent cues such as the playing side arm and the racquet early in visual display across a combination of badminton strokes (Abernathy and Russell, 1987). Novices tend not to make use of the same cues. For example, in badminton novices were not able to make use of cues from the racquet arm and therefore had a larger radial error when predicting the landing position of the strokes (Abernathy and Russell). Typically experts show systematic visual search patterns from one viewing to the next. Shank and Haywood (1987) examined the visual search strategies of expert baseball batters. Results revealed that expert batters fixated on the release point of the pitch’s throwing motion even after the ball had been released, and were able to detect different types of spins more accurately compared to less experienced batters. The expert batter fixated on the hand of the pitcher systematically.


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Before further investigation of the characteristics of expert tennis players’ visual selective attention, it is important to understand the criteria, described by motor learning researchers, of the motoric, cognitive, and perceptual elements of an expert athlete. Fitts and Posner (1967) described various stages of learning progressing from the novice to expert athlete. The following review of literature will focus on their description of the expert athlete who is in the autonomous (final) stage of learning and motor production. The autonomous stage o f learning according to Fitts and Posner (1967) After much practice and experience a skill can become automatic or habitual. Cognitions during this stage tend to focus on aspects of performance that are not directly related to the motor components of an action, but instead to strategies of the game such as the preferred direction of a soccer kick (Williams and Davids, 1998). At this stage, knowing what foot to step with and how to kick the side of the ball are automatic and do not need conscious thought to be produced. In many cases an expert performer may not even be able to describe how they do something, instead they just “know it” or can show another person how to do it. This is the difference between an expert player and an experienced coach or teacher because the coach can show and explain (or teach) the action and its small intricacies whereas the expert player may only be able to show it (implicit verses explicit knowledge). Such cognitive automaticity relates to Treisman’s (1988) Feature Integration Theory because the attentional “spotlight” is directed to certain areas within a visual display. The attraction and hence movement of the attentional spotlight is developed through experience. The information extracted from within the spotlight’s focus is without conscious thought, that is, the flow of information becomes autonomatic and


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does not require a great degree of attention, according to information processing theories (Neisser, 1967). Hence, features within the display are extrapolated quickly and the expert is able to make sense of the information and utilize it most effectively to produce a motor response, in a timely and efficient manner. This occurs without too much cognitive thought - which is likely to slow down the entire visual search process (Neisser). Motorically, athletes who are able to reach this stage of performance have little variability in their action and its outcome. They are able to detect and correct errors quickly and accurately. Teachers can facilitate performers in getting to this stage of learning by providing teaching environments conducive to such actions. This can be done through effective progressions and contextual factors. For instance, teachers can create an environment where the performer must think quickly and reproduce actions similar to the requirements of a game situation. It is necessary to understand the motor and cognitive characteristics of expert performers as they relate to visual search and selective attention patterns. The visual selective attention patterns of expert tennis players will now be reviewed. As this dissertation focuses on expert tennis players, the review of literature will also focus on experts and not novices. Visual selective attention strategies o f expert tennis players One of the first studies to examine visual selective attention in tennis was conducted by Goulet et al., (1989) who investigated expert and novice tennis players viewing a tennis serve using dynamic film on a television monitor. The expert players in this study were defined as those who were ranked in the top 40 in Quebec or who were


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ex-ranked players serving as coaches in the Quebec area. Goulet et al. measured visual search patterns of experts during three different phases of the tennis serve. The ritual phase preceded the initiation of the serve and consisted of the ball bounces and body position. The preparatory phase began at the elevation of the arm holding the ball and ended at the apex of the ball trajectory. The execution phase, the third and final stage of the serve, began at the server’s knee extension and ended at the ball/racquet contact. Scanpaths were used to measure visual search patterns in this study. A scanpath identifies an area where information is selected and indicates the sequential order underlying the participant’s organization of visual search. “The favored exchange of successive fixation on two different cues represents dynamic priority of the participant during the task” (Goulet et al., p. 385). Results of scanpaths used during the ritual phase revealed that experts organized their search around the head and shoulder/trunk complex, the general body position of the server. During the preparatory phase of the serve, the most frequent exchanges were around the ball and the expected position of the ball. During the execution phase, experts looked at the racquet and arm area and then, at the moment of contact with the ball, experts stopped focusing on the racquet and arm and focused on the ball/racquet contact point (Goulet et al., 1989). In addition, the number of fixations that occurred during the ritual phase were found to be significantly higher for experts than novices. However, no significant differences were found between experts and novices in terms of the number of fixations during the preparation and execution phases. In Goulet et al.’s (1989) second experiment, using the same participants as the first experiment, they examined differences between expert and novice tennis players in


terms o f the relative contribution each phase the serve played in recognizing the type of serve (flat, slice, topspin) delivered. The serve was divided into five segments (or situations) where parts of the film were occluded. Situation 1 was the preparatory phase (875 ms). Situation 2 was the preparatory phase and the first part of the execution phase (1125 ms), until the elbow reached its maximum height. Situation 3 consisted of the preparatory and execution phases until the ball/racquet contact (1208 ms). Situation 4 was the serve from the beginning of the ritual phase until ball/racquet contact (4710 ms). Situation 5 included the entire service motion without occlusion (5048 ms). Participants were required to identify the type of serve, via a verbal response as quickly as possible upon the server’s contact with the ball. Results revealed that experts had a significantly higher number of overall correct responses than novices. For experts results showed no significant differences between situations 2 and 5. However, situation 1 differed from all others, suggesting that valuable information is selected during the preparatory phase and the first part of the execution phase, that is from the ball placement and beginning of the upper body rotation and arm/racquet complex motion. Tenenbaum et al., (1996) also used a video film on a television monitor to stimulate tennis strokes including groundstrokes, volleys, and serves. The methodology involved determining what cues were essential through the use of recalling focus of attention when observing different stroke sequences. Results revealed that expert tennis players were significantly more accurate in anticipating the final ball locations of each of the strokes compared with novice participants. This finding was particularly evident under conditions where occlusion occurred after ball contact; hence, expert participants


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could view information early in the motor sequence. Such results are consistent with Goulet et al. (1989) where experts also took advantage of cues in the preparatory and early execution phases. In both Goulet et al. and Tenenbaum et al. results revealed that experts produced significantly better final performance scores (number o f correct responses) than novice participants and this was particularly evident in conditions where the preparatory phase of the tennis serve was seen and not occluded. The investigation of visual search strategies by Tenenbaum et al. (1996) found fixation locations o f experts to focus on the shoulder, arm and racquet prior to ball contact. These results were consistent with Goulet et al. (1989). Experts attended to several locations (arm, racquet, shoulder) with high consistency, moving back and forth among these locations (similar to Goulet et al.’s scanpaths). Experts focused on a single location, the ball, after racquet/ball contact. Singer et al. (1996) conducted a study of tennis players investigating visual search strategies. Their study replicated and extended Goulet et al.’s (1989) and Tenenbaum et al.'s (1996) research by requiring participants to produce a motor response (a simulated split step) in the anticipated direction of the oncoming service ball. Video was displayed on a television monitor to measure expert tennis players. Highly skilled players visually tracked the ball during the ball toss phase and then focused on the racquet-arm and racquet region. These results were consistent with Goulet et al. and Tenenbaum et al. Experts were consistent with their visual search patterns and were fast and accurate in anticipation and decision making. In a further extension of Singer et al.’s (1996) study, Singer et al. (1998) investigated visual search strategies in a live tennis situation. One of the purposes of


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their research was to explore the possibility of being able to use the ASL 4000 instrumentation in an outdoor environment. Of particular interest were differences in visual search patterns among expert tennis players, all of whom were highly ranked university varsity players, as they prepared for and executed the return o f serve. Participant’s visual search behaviors were analyzed based on the entire service motion and phases of the serve as determined by Goulet et al. (1989). Results revealed differences among experts in that the best male and female player focused on the arm, racquet and shoulder region during the ritual phase. During the preparatory phase the highest ranked players demonstrated 100% pursuit tracking (smooth eye movements) of the ball to the highest point of the ball toss. In contrast lower ranked experts used saccades, fast, jerky eye movements, to the apex of the toss and did not fixate on the ball but tended to fixate to the expected ball location during the toss. The most significant difference between experts visual search behavior occurred after ball contact by the server. Lower ranked experts did not effectively pursuit track the ball and instead made saccadic eye movements from the ball to the expected bouncing location, whereas the highest ranked experts used smooth pursuit tracking eye movements maintaining fixations on the ball during the toss and after contact from the opponents racquet. Singer et al. (1998) reported that intraexpert differences were primarily observed with regards to type of visual search behavior. In other words, the experts did not focus on different locations, but rather employed different strategies when looking at the same locations (saccades or pursuit tracking). In summary, results from Goulet et al. (1989), Singer et al. (1996), Singer et al. (1998) and Tenenbaum et al. (1996) all revealed that experts are systematic in their visual


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search patterns from one viewing to the next and tend to focus on similar consistent locations. These results have been found across studies in the various temporal phases of the service motion. However, differences between experts were found in terms of the type of eye movements at particular locations (Singer et al., 1998). The visual search patterns of experts enable them to initiate a movement faster than novices. For example, Singer et al. (1996) found significantly faster motor response times of experts compared to novices when anticipating the direction of an oncoming ball from an opponent's serve. The quality of motor responses has also been found to differ when less effective visual search patterns are used between players of similar skill level. Singer et al. (1998) tested the quality (depth and accuracy) of service return between college level tennis players while measuring their visual search patterns on the tennis court. Results revealed that players with less salient visual search behaviors were judged lowest in quality of service return. Hence, the cognitive understanding experts have when watching a skill, is evidenced via different visual search strategies that seem to provide them with an ability to anticipate direction and spin on a ball more accurately than those with less experience. This capacity has also been shown to relate to faster motor responses o f a higher quality. Limitations to current research - able-bodied participants Although research thus far has provided information about where expert ablebodied tennis players look compared to novices when returning tennis serves, several limitations still exist. For instance, all research conducted thus far has been conducted on people without disabilities even though people with disabilities who engage in sport are an ever-growing population (British Wheelchair Sports, 2003). The 2000 Para-Olympics


in Sydney produced the largest crowd of spectators to date (International Tennis Federation, 2003). One o f the most watched events in the Sydney Para-Olympics was wheelchair tennis (Paralympic Tennis Association, 2003). The growing popularity of sport for the disabled, coupled with the USTA’s encouragement of an inclusive environment for able-bodied and wheelchair tennis players, leads to an important question: What are the visual search strategies of wheelchair tennis players, and do they have similar visual search strategies as able-bodied expert tennis players when watching an opponent serve? Wheelchairs can only move forward and backward thus restricting lateral movements in wheelchair tennis (Wheelchair Tennis Coaches Manual, 2000). In order to compensate for the restrictions in movement, expert wheelchair tennis players may need to anticipate where to move more quickly when returning an opponent’s serve compared to able-bodied tennis players. Does this change their visual selective attention? Research to date provides no answers to these important questions. Limitations to current research - the term “expert” Research to date has used the term “expert” in tennis for a variety of different skill levels. For instance, Tenenbaum et al. (1996) determined experts to be professional players with an official Association of Tennis Professionals (ATP) ranking (not limited in range). Singer et al. (1998) defined experts as ranked college level players. Goulet et al. (1989) specified experts as those ranked in the top 40 in Quebec or who were currently or previously ranked players in the Quebec area. Ward et al. (2002) defined experts as club level players with 11.9 years of experience. Clearly these different levels of expertise make it difficult to compare the results of these studies. For instance, Singer et al. (1998) found differences not in locations of search but of the visual search behaviors of college


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tennis players. It is important to provide a very high standard of “expert” to accurately portray the most appropriate visual search strategies. It is also important for the expert to currently hold that status as the game of tennis is constantly changing due to the advancement of technology and changing techniques and biomechanics (Roetert & Groppel, 2003). For instance, a high ranking male professional’s serve in tennis today may reach speeds of 140 mph compared to similarly ranked tennis professionals of the past who averaged around 100-1 lOmph. With a ball reaching such high speeds, visual search patterns are critical to effective return of serve. It is important to recognize these research limitations in trying to understand findings. More research is needed before results can be generalized to an ever growing population of tennis players who play from wheelchairs. Also, for able-bodied participants, understanding visual search strategies of the “most expert” tennis players is necessary, as a first step for the development of an understanding of the most effective visual search strategies when returning a tennis serve. Once these strategies are understood, it will be possible to create effective interventions to assist players of all levels. Purpose o f the Current Research The primary aim of this study was to investigate a population of tennis players who have not previously been studied in terms of their visual search strategies, that is wheelchair tennis players. All research conducted thus far has been done on people without disabilities. People with disabilities who engage in sport are an ever-growing population (British Wheelchair Sports, 2003). It is hopeful that this research will provide some answers regarding where an expert wheelchair tennis player looks to when


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returning a serve. The goal of this research was to provide a first step in understanding expert wheelchair tennis players visual search strategies and for future research to develop interventions and educational strategies for wheelchair tennis players with less experience. As previously mentioned, research to date has classified participants as “expert” in tennis for a variety of different skill levels. As an extension of current research, this study utilized participants, able-bodied and disabled, who were tennis professionals ranked within the last twelve months by the governing body of their sport. For ablebodied participants, ranking ranged from 44 - 1100 on either the Women’s Tennis Association (WTA) or Association of Tennis Professionals (ATP) (for men) ranking systems. For wheelchair tennis players rankings ranged from 1 - 250 on the International Tennis Federation (ITF) tour. These players were all “elite” level professionals, and because of their “expert” status, provided a good sample to accurately portray the most appropriate visual search strategies. It was also important for the expert to currently hold that status as the game of tennis is constantly changing due to the advancement of technology and changing techniques and biomechanics (Roetert & Groppel, 2003). Another extension of past research that this study investigated was the use of new technology for detecting visual search not previously used in the sporting arena. The Eye-gaze Response Interface Computer Aid (ERICA) (Eye Response Technologies, Inc.) is the most accurate eye movement recording system available, with 0.5 degrees of detectable visual angle (Lankford, 1999). Eye movements are tracked and recorded at 60 fjps, the highest rate of tracking available (Lankford). Precalibration is calculated with respect to a 16-point grid, double that of other eye movement systems ensuring accurate


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calibration throughout viewing the visual display across the viewing field. These qualities enable ERICA to be a very accurate and reliable eye tracking system, adding to the reliability of data used in visual search research. These factors are extremely important especially when tracking eye movements of high-level experts who are watching movements and balls that are extremely fast. Thus, the primary purpose of this study was to examine a population of tennis players not previously examined (elite wheelchair players) with regards to visual search variables, specifically the number of fixations and fixation locations when returning a tennis serve. Two extensions to the current body of knowledge in visual search were also investigated, including an analysis of current high-level professional tennis players (able bodied and wheelchair players) and the utilization of a new piece of eye tracking technology (ERICA) in the sporting arena. Descriptive statements o f research The following 12 statements were analyzed in order to compare them to past literature on able bodied tennis players and to extend their application to wheelchair tennis professionals. For an explanation of each variable (e.g., temporal phase, lookzone and visual search variable) see the Chapter 3 and Table 1. 1.

Average number of fixations for the entire length of the tennis serve.

2.

Average fixation durations for the entire length of the tennis serve.

3.

Average number of fixations for each area of interest (lookzone).

4.

Average fixation durations for each lookzone.

5.

Average number of fixations for each individual lookzone.

6.

Average fixation durations for each individual lookzone.


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7.

Average number of fixations within each temporal phase of the serve.

8.

Average fixation durations within each temporal phase of the serve.

9.

Average number of fixations in each temporal phase for each lookzone.

10. Average fixation durations in each temporal phase for each lookzone 11. Average pursuit tracking within lookzone 4 (the ball) for the total service time. 12. Average pursuit tracking within lookzone 4 (the ball) for each temporal phase.


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CHAPTER III Method

The purpose of this research was to describe the visual search patterns of two classifications of expert tennis players: able-bodied (AB) and those who play from a wheelchair (WC). Specifically, the number of fixations, fixation durations, pursuit tracking as well as locations and temporal aspects of the player’s visual search patterns were examined. Due to the complexity of technology in this study and modifications in the participant sample, this chapter will begin with WC participants disability criteria, then the procedure undertaken during the study. Technology is then described in the apparatus section, followed by the test stimuli and then the participant sample. The chapter concludes with data analyses, including tests for learning and descriptive statistical analysis. Wheelchair participant disability criteria. All WC tennis participants were classified as eligible for the paraplegic division (injuries at or below the thoracic level of the spinal cord) by the International Tennis Federation (ITF, 2003). According to the International Standards for Neurological and Functional Classification of Spinal Cord Injury (ISNFCSCI, 2003) a spinal cord injury (SCI) is “an insult to the spinal cord resulting in a change, either temporary or permanent, in its normal motor, sensory, or autonomic function” (p. 12). The participants in this study had permanent changes in function. The ISNFCSCI is a widely accepted system describing the level and extent of injury based on a systematic motor and sensory examination of neurologic function.


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Classification of SCI Paraplegia includes any “injury in the spinal cord in the thoracic, lumbar, or sacral segments, including the cauda equina and conus medullaris” (ISNFCSCI, 2003, p. 10). Not all participants competing in the ITF paraplegic WC tennis division are paraplegics, because other injuries enable participants to enter this division. According to the ITF Rules and Regulations (2003) the competitive paraplegic division WC tennis player is eligible to compete in WC tennis if they have a “medically diagnosed permanent mobility related physical disability” (p. 17). This permanent disability must result in a “substantial loss of function in one or both lower extremities” (p. 18). All players met at least one of the following minimum eligibility criteria according to the ITF (2003, p. 17): 1. A neurological deficit at the sacral 1 level or rostral, associated with loss of motor function; 2. Ankylosis and/or severe arthrosis and/or joint replacement of the hip, knee or upper ankle joints; 3. Amputation of any lower extremity joint rostral to the metatarsophalangeal joint; 4. A player with function disabilities in one or both lower extremities equivalent to any of the previous conditions; 5. Other permanent impairment related to but not specified in categories 1-4. The WC tennis players in this study had a variety of disabilities (see Tables 4 - 5 , 8), and all competed in the same ITF paraplegic division. None of the disabilities effected range of motion in the cervical region of the spinal cord that might have inhibited head or eye movements based on the preceding criteria.


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Visual acuity. Visual acuity for all participants was reported by participants as 20:20 natural or corrected vision (if they wore corrective lenses) when viewing the film. This was acceptable providing ERICA calibration was successful. If contact lenses or glasses prevented successful calibration, participants were excluded from the study. Only one participant was excluded due to calibration difficulties, which were related to recent eye surgery. Procedure Participants were tested at various locations and times throughout professional tennis tournaments between April and September, 2003. This study was approved by the Institutional Review Board (see Appendix A) and upon arrival for testing, participants completed a consent form (see Appendix B) and a general information questionnaire (see Appendix C) collecting descriptive data such as age and ranking information. Each player was tested individually at a location within the tournament site. AB participants sat in a comfortable chair (without wheels). Participants who played WC tennis sat in their “everyday” chairs, because those chairs were fitted with brakes and therefore limited movement compared to tennis wheelchairs, that do not have brakes. WC participants were instructed to secure the brakes to prevent any change in eye positioning due to their WC position. Each participant was asked about her/his dominant side (racket arm) which was the same side eye used for measuring eye movements. The system was then calibrated to each participant’s dominant side eye. When the system had been successfully calibrated, instructions were read. The statement began with a description of the video to assist with familiarization of the process. Each participant was asked to watch the serve and


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“imagine you are on the tennis court playing this person in a competitive match situation, such as at this tournament... you are about to return serve during the match ... think about and imagine trying to return the serve as effectively as possible, making it difficult for the server to return” (for the full statement of instructions see Appendix D). If the participant had no questions, three serves (one flat, one slice and one topspin, varying in presentation from the deuce and add sides of the court) were presented in random order to become familiar with the video. After the presentation of the three serves, the calibration was again checked afterwards the participant watched the testing video in its entirety. None of the participants asked questions after seeing the three pre-test video clips. At the completion of watching the testing video of 18 serves, participants filled out a final questionnaire (see Appendix E) as a form of verification and clarification of the participants’ thoughts while watching the video. The concluding questionnaire also served to assess the realism they thought the video portrayed. Apparatus: The Eye-gaze Response Interface Computer Aid (ERICA) Recent technology allows research in visual search to be conducted with noninvasive precision. In tennis research there have been several systems used from the Applied Sciences Laboratories Company (Waltham, MA), including most recently the 5000SU Eye Tracker (Ward et al., 2002) or the 4000SU eye movement head mounted system (Singer et al., 1998), and the Eye-Trac model 210 (Singer et al., 1996). More recently, the Eye Gaze Response Interface Computer Aid (ERICA, 2003; Eye Response Technologies, Charlottesville, VA) has become available and was utilized in this research.


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The Eye-gaze Response Interface Computer Aid (ERICA, 2003; Model 000-00103) system was used to collect visual selective attention data (Figure 2-3). The ERICA module collects information about the user’s eye and conveys it to a computer (in this case, Dell Dimension 4500). Inside the module is a Light Emitting Diode (LED), which projects low-powered infrared light. This light is reflected from a camera (ERICA Model LCL-902K) onto the eye (see Figure 3). Infrared light is harmless and invisible to the human eye, so the light does not interfere with the user’s vision. Reasoning fo r selecting ERICA. Technological limitations of past research have necessitated various adaptations such as metal bite bars with dental impression compound (Suppes, Cohen, Laddaga, Anliker & Floyd, 1982), chin rests (Singer et al., 1994), face masks (Gale & Findlay, 1983; Ross, Hommer, Breiger, Varley, & Radant, 1994), and head mounted cameras (Buizza & Avanzini, 1983). Such problems have caused experimenters to eliminate participants and call into question the validity of their results (Kaufmann & Coles, 1983). Recent technological advancements such as the ERICA system allow for precise and accurate recording of data in a less restrictive, more comfortable and less intrusive fashion. ERICA does not require the use of obtrusive devices (White, Hutchinson & Carley, 1993) and has a user friendly interface system (Lankford, personnel communication, 21 November, 2003) with automatic calibration (Lankford, 1999). For these reasons this system was seen as most appropriate for the current study. Designated System Parameters. The ERICA system records pupil area and eye gaze position data. These recordings occur at incremental frequencies of approximately 60 hz, or 60 points or fps.


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Fixation rate was set at 100 ms based on past research studies in tennis (Ward et al., 2002; Williams & Davids, 1998). The fixation rate was also based on eye tracking protocol recommendations (Salvucci & Goldberg, 2000). Past research has found that foveal (or focal vision) extends approximately 2.5 degrees from the center of gaze in all directions (Kundel, Nodine & Krupinski, 1989; White et al., 1997). This corresponds to a 0.8 cm radius at a viewing distance of 18 inches (Lankford, 2002). The ERICA system is capable of determining a point of regard with accuracy and precision within +/- 0.6 degrees of visual angle, which corresponds to a 0.5 cm radius around the true point of regard on the computer screen (Lankford, 2002, see Appendix H for details). ERICA accuracy refers to the systems ability to measure where the eye is actually pointed, and involves errors in screen calibration. Precision refers to how consistently the system and the eye “agree” with where someone is looking. Accuracy and precision of any eye tracking system, including ERICA, is obtained only when head motion is limited (with the exception of a head-mounted piece of equipment used in the ASL 4000SU eye movement head mounted system). The ERICA system has a movement tolerance of approximately 3 cm during gaze tracking without a change in calibration that may misrepresent data (Lankford, 1999). Therefore to avoid compromising internal validity, participants were seated throughout this study. AB participants were seated in a chair with no wheels and WC participants where seated in their personal everyday chairs (as WC tennis chairs do not usually have brakes) and asked to secure the brakes to reduce excessive head movements. Participants were also asked to keep still from the beginning of calibration until the end of the test


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video, as recommended by Spetz, Hutchinson and Corman (1991). The researcher also recorded (via observational notes) any excessive head movements should they occur while the participant was seated and after instructions had been read. The system was calibrated with a one-point eye calibration and then a 16-point screen calibration, presented in a grid format, beginning in the upper left hand comer of the screen and finishing in the lower right hand comer (for details see Appendix H). ERICA measured only one eye, which was selected as the eye on the same side of the head as the participant’s dominant (racket) hand. If a participant was right handed their right eye was used for calibration and testing or if left-handed the left eye was used. Reliability testing o f the ERICA system. To test for reliability of the data produced by the ERICA system, an Interclass r correlation was calculated on the three participants who were retested due to moving violations. The resulting high correlation, Interclass r = 0.92 between testing session 1 and 2 suggested the reliability of the ERICA system. Test Stimuli To examine visual selective attention strategies this research used videotape with professional tennis players serving. Participants watched the videotape and imagined they were in the return o f serve position. Test Film: Tennis Servers. Two professionally ranked male tennis players were used as models for the tennis serve videotapes. One player was AB and the other was a WC player. WC tennis players watched the WC tennis model and AB tennis players watched the AB model. The reason for having two different videotape models was based on past research findings that suggested if parts of a display were ambiguous or novel those areas of a


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display attracted fixations (Kundel & Wright, 1969; Mackworth & Morandi, 1967). Because professional WC tennis players do not compete against professional AB players, it was determined that having a WC player view an AB server would be “ambiguous and novel” enough to disrupt visual selective attention patterns and potentially contaminate the results. The same might be true of AB players who seldom play against WC players at professional “expert” levels. Male models were chosen for viewing for both genders based on modeling research suggesting that males relate, learn, and assess informational cues more effectively from models of the same gender (McCullagh, 1993; 2001). Research on females and modeling suggest that females can view models of either the same or different genders and are able to relate, learn and assess informational cues as effectively with either gender (McCullagh, 1993). The AB model had a current (April, 2003) ATP ranking of 32 (aged 23 years, 19 years of tennis playing experience) and the WC tennis model was ranked number one (September, 2002) according to the ITF (aged 30 years, 10 years of WC tennis playing experience). Both models were filmed from a “front on” perspective using a digital video camera (Sony, DCR-TRV19). For the AB model, the video camera was positioned 91.44 cm from where the singles sideline and baseline intersect and towards the hash mark (center of baseline) on the baseline at a height of 171.45 cm, based on the average height of a male player (Halls, 2003). For the tennis players who played from a WC the video camera was positioned 91.44 cm behind the baseline from where the singles sideline and baseline intersect and 91.44 cm from the hash mark (or center baseline). The video camera for WC tennis


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players was filmed at a height of 120.65 cm based on the models' height while seated in a WC tennis chair. AB tennis players position themselves around the baseline when receiving serve according to current Davis Cup Coach P. McEnroe (personal communication, September 15, 2003) whereas WC tennis players sit further back in the court according to Olympic WC tennis coach D. G. James (personal communication, September 9, 2003). Hence the two videos were positioned in different locations at the receiving end of the court. The models performed 18 serves, nine on each side of the court. Three types of serves (flat, slice, and topspin) were executed and each type of serve was hit in one of three directions (wide, at the body, or down the center) within the service boxes. The videotape was then edited using the Pinnacle Studio Version 7 (Pinnacle Systems, Inc., 2002) editing system. The natural occurring sounds associated with each server were included in the videotape. Each serve was recorded from when the server moved up to the baseline to begin the service motion and terminated when the ball crossed the net. The serves were shown in real time with the total duration of the serves from the AB model equal to 108.51 s, including a two-second grey screen presented between each of the 18 serves. The total duration of the video for the WC server was 44.47 s, including a two-second grey screen presentation between each of the 18 serves. Tapes were different in length because WC tennis players traditionally have a very short ritual phase and the preparatory and execution phases often occur simultaneously. The AB player had a traditionally longer ritual phase and produced the service motion sequentially, moving from the ritual phase to the preparatory phase then to the execution and finishing phase with no time overlap between the beginning and end of each phase.


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Video editing enabled the 18 clips to be presented in random order for each participant. The grey screen was presented for two seconds between each clip to neutralize the pupil. The videotape was sized to fit a 17-inch (43.18 cm) flat panel Dell Dimension 4500 (Dell Computer Corporation) computer system. Participants A total of 87 adults participated in this study, including 43 WC tennis players (aged 18-56 years, M = 34.95, SD = 8.94; 22 males, 21 females) and 44 AB tennis players (aged 17-34 years, M = 22.62, SD = 3.85; 20 males, 24 females). Participants were tested on a volunteer basis, having been recruited from tennis tournaments. This accounted for differences in group participation. WC tennis players were ranked between 1 - 250 on the International Tennis Federation (ITF) tour, and had played tennis from a wheelchair for an average of 11.42 years (SD = 5.83; see Tables 4, 5 and 8). AB players were ranked on the Women’s Tennis Association (WTA) tour (see Tables 6 and 8) or the Association for Tennis Professionals (ATP) tour (see Tables 7 - 8 ) between 44 - 1100, and had played tennis for an average of 15.29 years (SD = 4.42). Rankings for both male and female AB and WC tennis professionals were effective as of August 3 1st, 2002 to October 1st 2003. An initial analysis of the average number of fixations for the entire tennis serve (descriptive statements 1; see Table 2) and across all lookzones (lookzone 1 + 2 + 3 + 4; descriptive statement 3; see Table 2) revealed some perplexing results that had implications for the methods used in this study. Results for the analysis of fixations for the entire serve revealed that the AB participants had an average of 6.58 fixations (SD = 0.70) anywhere on the computer screen and with 4.72 fixations (SD = 0.64) or 71.7%


within any of the lookzones. For WC tennis participants the total number of fixations while watching the 18 video clips averaged 4.81 (SD = 0.47) with 1.71 fixations (SD = 0.78) or 35.6% within the lookzones. This seemed perplexing as the lookzones covered the entire model during all aspects of the service motion, hence a fixation recorded outside a lookzone would not be associated with the service performance. In other words, the participants may have been viewing the net or the back screen of the tennis court, or some other non-relevant area of the total image. Due to this initial finding, each participant’s results were transmitted and outputted separately to a notepad file. The researcher created a summary sheet (see Appendix F) in order to perform an individual analysis of each participant’s fixations, fixation locations, and fixation durations. Results from this process revealed that some participants’ did not view the model during some of the video clips. Therefore, it was deemed necessary to review each participant’s videotape individually with the gaze trail superimposed (in real time) to see what was causing these perplexing findings. When reviewing the videotapes the researcher was blind to both the participants’ identity and status as AB or WC. Three observations emerged from these individual analyses. Some participants seemed to move during data acquisition (labeled: movement violators), and seemed to visually search ahead of the actual events (e.g. anticipating the ball movement; labeled: predictors), others showed obvious signs of distraction (labeled: gaze trail distractors). Movement Violators. One observation indicated that some participants had moved while watching the test videotape. For some participants this was shown by having the gaze trail on every trial consistently in the top third of the screen (see Figure 7). For


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others the gaze trail was consistently two inches to the left or right of the model (see Figures 8-9). These participants were classified as “movement violators” and excluded from the overall final analysis. Data from these participants were later used when discussing different viewpoints of researchers using the ERICA technology (see Movement Violators Retested for further explanation). Predictors. Another observation resulting from viewing the gaze trails of each participant was particularly evident for approximately 40% of the WC participants who seemed to fixate in the anticipated tossing area prior to the ball actually being tossed. It appeared that they were predicting where the ball was going during the ritual phase. WC participants, who engaged in this strategy did so only during the beginning of the serve, prior to the ball being tossed (i.e. the ritual and beginning of the preparatory/execution phase). As this occurred in 17 of the 43 WC participants it was viewed as a possible visual selective attention strategy of WC tennis players, not accounted for in past literature with AB tennis players. Hence, a fifth lookzone (the predictive lookzone) was added for both the AB and WC tennis players. A decision was made for the WC tennis participants to be analyzed as a whole as well as in two groups for all phases of the serve: (a) the “WC Predictive Group” that anticipated the ball toss ahead of time, and (b) the “WC Real Time Group” that did not predict the ball toss ahead of time and instead viewed the model as he performed the action in “real time.” The WC participants were divided into these two groups in order to reveal the number o f fixations in this new

“predictive lookzone” (lookzone 5). If participants had any fixations in the predictive lookzone they were classified in the “WC Predictive Group.” If participants had no fixations in the predictive lookzone they were classified in the “WC Real Time Group.”


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The predictive lookzone was present during the ritual phase of the serve and until the tossing (non-dominant) arm was lifted waist high (see Fixation Locations for details). Gaze Trail Distractors. Upon reviewing each participants gaze trail a third observational pattern was noted. It appeared some participants were distracted from the server and seemed to focus on a water puddle on the court or a tree in the background that occasionally moved with the breeze (see Figure 5). Modifications in Sample o f Tennis Players After viewing the videotapes, and taking into consideration these observations, it was deemed necessary to reevaluate participants with the researcher blind to the participants, using the following three criteria for exclusion: 1. Movement violations or distractions. By viewing each participant’s videotape with the gaze trail superimposed and noting its quality, it was possible to check for movement violations or obvious signs of distraction. Participants were excluded if it seemed they had moved or were distracted. A total of 7 WC and 2 AB participants were excluded due to movement violations or distractions. 2. Inattention. Participant responses to the post-assessment (concluding) questionnaire (see Appendix E) were examined. If participants answered “no” to “Were you thinking about competing in a real match against this person?” (Question 1) or “Were you thinking about trying to return the serve as effectively as possible?” (Question 2) they were excluded from the analysis. A total of 3 WC and 10 AB participants were excluded due to inattention as determined from the post-assessment questionnaire.


3. Unusual occurrences during testing based on observational notes. Descriptive observational notes taken by the researcher at the time of testing were considered. These included: information on the environment, distractions, and the participants’ manner and attentiveness during testing. Participants were excluded from the analyses if observational notes revealed that either the environment or personal dilemma caused the participant to be obviously distracted. A total of 1 WC and 1 AB participants were excluded due to observational notes explicating unusual occurrences. After each participant was reevaluated for inclusion based on the preceding criteria, a total of 63 participants were included in this study including 31 AB tennis players (aged 18-34 years, M = 23.63, SD = 3.59, 15 males and 16 females) ranked between 44-440 on the Women’s Tennis Association (WTA) tour (see Table 6) or the Association for Tennis Professionals (ATP) tour (see Table 7). The participants had played tennis for an average of 16.74 years (SD = 4.52). Thirty-two WC players were left for inclusion in the final analysis (aged 18-54 years, M = 33.38, SD = 8.77, 17 males, 15 females) ranked from 1-82 on the International Tennis Federation (ITF) tour. WC participants had played tennis from a WC for an average of 11.41 years (SD = 7.42; see Table 4 - 5 ) . A total of 17 WC players were in the Predictive group (aged 19-54, M = 32.20, SD = 8.57, 9 males, 8 females) and 15 WC players were in the WC Real Time group (aged 18-44, M = 34.41, SD = 9.08, 8 males, 7 females). WC Predictive group participants were ranked on the ITF tour 1-60, and WC Real Time group participants were ranked 1-82. Years of WC tennis playing experience for WC Predictive group participants was an average of 9.76 years (SD —6.61) and for WC Real Time group


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participants was 14.07 years (SD = 7.32). Due to the reevaluation of participants all results reported from this point forth involves the reclassified participants (N = 63, AB n = 31, WC n = 32) included in the final analysis, unless otherwise specified. Data Analyses Data for all descriptive measures were calculated using the Statistical Package for the Social Sciences (SPSS, 1998). The ERICA system can manipulate the assessment video post testing to enable the analysis of visual selective attention patterns. This was done in a variety of ways for each of the variables being measured. This study was limited to descriptive measures for two reasons. First, there were large differences in the lengths of the two video presentations. For WC players, the video was 44.47 s, whereas for the AB players the video was 108.51 s. Differences in the length of the videos is significant to a visual search study because the number of fixations and length of fixation durations may be different between the groups simply due to one group having more time to view the display than the other group. A second reason for a descriptive analysis was the difference in the temporal phases of the serve between the WC model and the AB model. The AB model had four temporal phases, including the ritual, preparatory, execution and finishing phases (see Table 1 or Temporal Analysis Chapter 3, for definitions of phases). Each phase was distinct and occurred in a sequential order for AB participants. This was similar to past research using AB models (Goulet et al., 1989) and past biomechanic analyses of the tennis serve (Groppel, 1980). An AB player is able to push off the ground using the lower body by bending the knees and extending the body using ground force and rotation in order to generate speed and extend into the serve (Groppel, 1980). The WC tennis


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service motion was similar to the AB model’s service motion in that the ritual and finishing phases were distinct. However, as WC tennis players have no use of the lower body, the timing and sequential motion of the serve found in AB participants occurs differently from WC tennis players. The preparatory and execution phases occurred simultaneously for the WC tennis model, therefore making the total service duration shorter and distinctly different from the AB server. Given these differences, it was not appropriate to contrast the two groups through inferential statistics. Rather the decision was made to merely explore the two types of players separately through descriptive statistics. The variables for this study are defined (see Table 1) and calculation procedures for each are described next. Visual search variables included the number of fixations and fixation durations. These variables were calculated first as total scores for each serve, and then averaged across the 18 serves. They were also calculated within each temporal phase and each lookzone (area of interest within the visual display). Average number o f fixations. A fixation refers to a stopping point when the participant’s eye remains stationary over the video screen when watching the server within a 40 pixel diameter of movement tolerance and longer than 100 ms. The ERICA system kept a tally of the fixations for each service presentation. At the end of the video, data output included a total number of fixation points for every serve presented. This was averaged across all serves presented and reported as the participants’ average number o f fixations. Average fixation durations. Each player viewed the server, and stopped her/his gaze at various points. The length of the “stop” is referred to as the duration of a fixation.


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The ERICA system was programmed to recognize a fixation point as any stopping point of the eye within a 40 pixel diameter on the screen that lasted longer than 100 ms. This is equivalent to three video frames based on a sampling rate of 60 fps. At the end of the video, the total time spent fixating for every serve was calculated. This was averaged across all serves presented and reported as the participants’ average fixation durations. Temporal analysis. Each serve was edited to include four phases of the tennis serve for AB players and three phases for WC players. The first phase, for both AB and WC players, was the ritual phase, defined as preceding the initiation of the serve and consisting of ball bounces, and chair or foot positioning (Goulet et al., 1989). For the AB model, this phase averaged 4320 ms (SD = 0.93) and for the WC tennis model the ritual phase was 1130 ms (SD = 0.14). The second phase was the preparatory phase, beginning with the elevation of the arm holding the ball and ending at the apex of the ball trajectory (Goulet et al., 1989). For the AB model this phase averaged 900 ms (SD = 0.20). The execution phase starts at the server’s racquet arm extension up towards ball contact and finishes at ball/racquet contact (Goulet et al., 1989). For the AB model this phase was averaged to be 570 ms (SD = 0.37). For the WC tennis model, the preparatory and execution phases were produced simultaneously (not sequentially like the AB model) and were therefore combined. The total average of both phases (preparatory and execution) combined were 930 ms (SD = 0.18) for the WC model. The fourth phase, for both AB and WC tennis players, was combined by the researcher and labeled the “finishing phase” that starts immediately after ball contact with


the racquet and ended as the ball crossed the net, at which time the video was terminated. For the AB model this phase was 280 ms (SD = 0.11) and for the WC tennis model the finishing phase was 410 ms (SD = 0.13). A total score, including all phases, was averaged for each of the servers. For the AB model, the total average time per serve was 6070 ms (SD = 0.79). For the WC tennis server, the total average time per serve was 2470 ms (SD = 0.39). Within each of the phases, the number of fixations longer than 100 ms were calculated. For instance, for the ritual phase a number was calculated representing how many times the participant stopped for longer than 100 ms for every serve presented. This was averaged across all serves for this temporal phase and reported as the participants ritual phase average number o f fixations. The same process was utilized for each of the three subsequent temporal phases of the service motion for each participant. Finally, a mean score for each phase was calculated by adding the total individual scores and dividing by the number of participants. Also for each of the tennis serve phases, fixation durations were calculated. For instance, for the ritual phase the ERICA system captured how long the participant stopped to look during each fixation. This was averaged across all serves for the temporal phase and reported as the participant’s ritual phase average fixation duration. The same process was utilized for each of the subsequent phases of the service motion for each participant (i.e. preparatory, execution and finishing phase for AB participants and preparatory/execution and finishing phase for WC participants). Finally, an average group score for each phase was calculated by adding the total individual scores and dividing by the number of participants.


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Fixation locations. To determine where a participant looked within the visual display, “lookzones” were created. For reliability of creating lookzones the following criteria were implemented. Based on 2.5 degrees of foveal vision, which corresponds to 0.8 cm (8 mm) on the computer screen, ERICA can determine a point of regard with a near accuracy and precision within +/- 0.6 degrees of visual angle, which corresponds to a 0.5 cm (5 mm) radius around the true point of regard on the computer screen (Lankford, 1999). Hence all lookzones were created by determining the “area of interest” for example the arm, then drawing around the arm with a 5 mm distance between the line drawn and the edge of the arm (see Figure 6). A lookzone was applied to record the amount of time the user spent gazing in a particular region of an image. Lookzones were areas defined by the researcher and represented the section of the display seen as important for the participant. Lookzones were not apparent to the participant, rather they were positioned within the visual display and moved with the area o f interest over the screen only during data analysis. Five lookzones were created for each of the service presentations. These areas were determined to be important based on past research findings for AB tennis experts (Goulet et al., 1989; Singer et al., 1996; Singer et al., 1998; Tenenbaum et al., 1996; Ward et al., 2002). All lookzones were presented for the entire service motion except for the predictive lookzone (see lookzone 5 for details). Lookzone 1 was set around the ‘general body position’ (GBP) that included the torso, legs, and head. For the WC tennis model this lookzone included the wheelchair. The general body position was deemed important during the ritual stage, according to research conducted by Goulet et al. For AB participants the shape of the general body position lookzone was similar to an


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elongated circle, the total surface area of this lookzone ranged from 1930 mm squared to 1850 mm squared. For WC participants the general body position lookzone was also an elongated circle, however it was larger around the chair, the total surface area of this lookzones for WC participants ranged from 1800 mm squared to 1780 mm squared. Lookzone 2 was created around the server’s non-dominant tossing arm (NDA). This area has been seen as pertinent during the preparatory phase, in past research by Goulet et al. (1989). For both AB and WC participants the NDA lookzone was the shape of an elongated circle. For AB participants the total surface area ranged from 535 mm squared to 445 mm squared, while for WC participants this lookzone ranged from 530 mm squared to 535 mm squared. Lookzone 3 was created around the arm, racquet and shoulder (ARS) region of the server’s dominant arm. This region has been seen as important by AB experts in various studies most specifically during the execution phase (Goulet et al., 1989; Singer et al., 1996; Singer et al., 1998; Tenenbaum et al., 1996). The shape of the ARS changed throughout the service motion, especially from the ritual phase (where the arm was straight) to the execution phase (where during the back scratch position the arm was bent). The total average surface area for the ARS for AB participants, ranged from 1255 mm squared to 905 mm squared. For WC participants it ranged from 1200 mm squared to 890 mm squared. Lookzone 4 was created around the ball (B), because of its importance at every stage within the service motion. Goulet et al., (1989) found that the ball was important during the ritual and the preparatory phase of the service motion. The lookzone surrounding the ball was in the shape of a circle, the total surface area of the ball ranged


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from 100 mm squared to 135 mm squared for AB participants and 100 mm squared to 130 mm squared for WC participants. Lookzone 5, the predictive (P) lookzone, was present for the WC players, during the ritual phase averaging 1130 ms (SD = 0.14) and for AB players averaging 4320 ms (SD = 0.93). The predictive lookzone for each serve was also present for a short period of time during the preparatory phase, an average of 480 ms (SD = 0.20) for the AB players and 490 ms (SD = 0.39) for the WC tennis players. In other words, this lookzone was present from when the model first appeared on the screen and until the non-dominant arm was raised to waist height during the preparatory phase. The predictive lookzone was set at 36 mm long and 14 mm wide (a total surface area of 504 mm squared) in the shape of a rectangle for both the AB and WC tennis players. Within each of these lookzones, three visual selective attention measures were of interest and recorded by the ERICA system: fixations, fixation durations, and pursuit tracking (discussed in the next section). The number o f fixations fo r lookzone 1 (GBP) was calculated through the ERICA system as the number of times the participant stopped to look (fixated for longer than 100 ms) for each serve presented within the lookzone. This was averaged across all serves for this lookzone. The same process was used for each o f the three subsequent lookzones of the service motion, for each participant. Finally, an average group score for each lookzone was calculated by adding the total individual scores and dividing by the number of participants. The same process was undertaken for fixation durations within each lookzone producing average fixation durations fo r lookzone 1, 2, 3, 4 and 5.


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The number o f fixations and fixation durations within each temporal phase of each lookzone were also calculated. For instance, within Lookzone GBP, the average number of fixations and fixation durations for each serve was calculated (as previously explained) and a score that represented a specific temporal phase within that average score was also calculated; for example, the average number o f fixations in lookzone GDP fo r the ritual phase. A score for fixations, fixation durations, and pursuit tracking was calculated for each of these phases. The visual search strategies of each serve by each individual participant was calculated. This score was again averaged across all trials for the individual and then averaged as a group score. Pursuit tracking. Pursuit tracking refers to the movement of the eye following an object within a dynamic visual display. A classic sport example of pursuit tracking is when a participant watches and follows a ball throughout a dynamic film. “These movements (pursuit tracking) assist in keeping the image within the zone of optimal vision” (Shakhovich, 1977, p. 243). This is particularly important at varying temporal phases of the tennis serve, in order to determine when the participant looks at the ball. A score for pursuit tracking is determined by firstly creating a lookzone around the object of interest and then by a process o f elimination, anything not found to be a fixation within the lookzone is seen as pursuit tracking. This process was created and calculated for the ball (lookzone 4) within each service display. A total score for the entire length o f the serve and a temporal score for each phase of the serve was calculated for this variable. Concluding questionnaire. At the completion of the protocol, participants completed a concluding questionnaire used as a form of verification and clarification of


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the participant’s thoughts while watching the video (see Appendix E). For questions three and four, a 4-point Likert scale was used ranging from “very realistic” to “not realistic.” Testing fo r Learning Before analysis began for each of the groups and after the revised selection criteria was implemented, it was necessary to check on the stability of the data, and the possibility that some learning may have occurred throughout the testing period from trails (serves) 1 through 18. A one-way repeated measures Analysis of Variance (ANOVA; p < 0.05) was performed on the total number of fixations within lookzones (descriptive statement 3) for both the AB and WC participants (no effect sizes are reported as trials are not seen as treatment effects). Results revealed no main effect for trails F(17, 544) = 94.17,/? > 0.75, for the AB participants or for the WC participants F(17, 527) = 182.69,p > 0.85, hence all 18 trails were included in the descriptive analyses. Movement Violators Retested As previously mentioned some participants were excluded from the descriptive analysis because it appeared that they had moved while watching the test video. For these participants, the gaze trail followed the server’s actions. For instance, as the model tossed the ball into the air, the gaze trail went up with the same relative time and the same distance as the ball being tossed on the screen, however the participants gaze trail seemed to be out o f alignment (see Figure 7-9). Each participant was calibrated while sitting at a certain angle. Yet it was discovered that if, while viewing the video, the participant relaxed and sank down into the chair, the rotation of the eye and related vector points became skewed when the movement was beyond 3 cubic cm of head motion. In such cases, eye movements initially calculated in the middle of the computer screen were


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displayed at the top (see Figure 7). If a participant shifted in his/her seat, then similar results occured. A shift to the left resulted in gaze trails skewed to the right (see Figure 9) and vice versa if a participant shifted to the right (see Figure 8). The ERICA system has been used for other studies (Bohn, 1993; Olmeda, 2002) and after consultation with researchers who had experienced using the ERICA system, it was confirmed that they too had experienced these movement occurrences (R. Brigham, personal communication, December 9, 2003). Brigham chose to physically manipulate the gaze trails and realign them (R. Brigham, personal communication, January 5, 2004). One o f the purposes of this dissertation was to use and experiment this new piece of technology (ERICA) that had not been used in sport, hence a sample of the participants who seemingly moved was retested. A total of nine participants were presumed to have moved during testing, three of these participants were retested. The purpose of retesting these three participants was to see if shifting the gaze trail from testing session one would reveal the same results as testing session two. If the results revealed no differences between the two testing sessions, then future research could be carried out with greater assurance that the shifting of a participants gaze trail would be an accurate representation of the participant’s eye movements. However, if differences were found between the two testing sessions then it would seem that shifting the gaze trail would not be an acceptable method. In this case, either participants in future studies would need to be excluded, or precautionary methods such as chin rests, would need to be used to avoid movement during testing when using the ERICA system. For the three participants who were retested the gaze trail for testing session one was moved using the “shift data” control keys for all 18 trials. This was done for the


same distance in the exact same direction for each trial. The distance and direction differed for each participant and was dependent upon the relationship of her/his movement. For the first participant’s testing session one, the gaze trail was shifted 56 mm to the right (due East) on the computer screen. For the second participant, the gaze trail was shifted 64 mm down (due South) on the computer screen. For the third participant, the gaze trail was shifted 44 mm to the north west of the screen at a 48-degree angle. All three participants were tested for a second time using exactly the same procedure as testing session one. Data from the total number of fixation in lookzones (statement 3) were used to test for differences between sessions using a two-way repeated measures Analysis of Variance (ANOVA) across all 18 trials. Due to multiple testing an adjustment of the alpha level was necessary to p < 0.125 (Tabachnick & Fidell, 1996). Results revealed no significant main effect between testing session one and two F( 1,2) = 1.00,/> > 0.136, partial eta effect size 0.95. Also no main effect was found between serves F{ 17, 34) = 35.50,p > 0.570, partial eta effect size 0.33. These results indicated that shifting data from testing session 1 to 2 for these three participants showed no significant differences and was an accurate representation of their gaze trails. It is also important to note that the small sample size (n = 3) of movement violators leads to low statistical power that may have contributed to the lack of non-significant findings. Descriptive Statistical Analyses The following measures were collected and described for both AB and WC tennis participants. For an explanation of each temporal phase, lookzone and visual search variable see Table 1. The following variables will be described for expert tennis players,


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both AB and WC players (see Table 2 for tables as they relate to corresponding descriptive variables): 1. Average number of fixations for the entire length of the tennis serve. 2. Average fixation durations for the entire length of the tennis serve. 3. Average number of fixations across all lookzones (lookzone 1 + 2 + 3 + 4 + 5). 4. Average fixation durations across all lookzones (lookzone 1 + 2 + 3 + 4 + 5). 5. Average number of fixations for each individual lookzone (lookzone 1, 2, 3,4, 5). 6. Average fixation durations for each individual lookzone (lookzone 1, 2, 3, 4, 5). 7. Average number of fixations within each temporal phase of the serve a) for AB participants (ritual, preparatory, execution, finishing) b) for WC participants (ritual, preparatory/execution, finishing) 8. Average fixation durations within each temporal phase of the serve a) for AB participants (ritual, preparatory, execution, finishing) b) for WC participants (ritual, preparatory/execution, finishing) 9. Average number of fixations in each temporal phase for each lookzone (lookzone 1, 2, 3.4, 5) a) for AB participants (ritual, preparatory, execution, finishing) b) for WC participants (ritual, preparatory/execution, finishing) 10. Average fixation durations in each temporal phase for each lookzone (lookzone 1, 2, 3.4, 5) a) for AB participants (ritual, preparatory, execution, finishing) b) for WC participants (ritual, preparatory/execution, finishing) 11. Average pursuit tracking within lookzone 4 (the ball) for the total service time.


12. Average pursuit tracking within lookzone 4 (the ball) for each temporal phase a) for AB participants (ritual, preparatory, execution, finishing) b) for WC participants (ritual, preparatory/execution, finishing)


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CHAPTER IV Results

This study investigated the visual selective attention patterns of expert ablebodied (AB) and wheelchair (WC) tennis players. Specifically, the number of fixations, fixation durations, locations and temporal phases of viewing, and pursuit tracking of the tennis serve were examined. One aspect o f this analysis involved investigating the fixations in particular lookzones which had been defined as: Lookzone 1: General Body Position (GBP) Lookzone 2: Non-Dominant Arm (NDA) Lookzone 3: Arm, Racquet and Shoulder (ARS) Lookzone 4: Ball (B) Lookzone 5: Predictive (P) This chapter begins with the broadest visual selective attention results including the average number of fixations and durations in total and within lookzones (descriptive statements 1,2, 3,4) to examine if players were on task and attentive during the video presentation. As the chapter progresses more specific analyses of the data will be presented. For instance, fixation locations (via lookzones) are reported (descriptive statements 5 and 6) followed by differences in the number of fixations and fixation durations in temporal phases of the serve (descriptive statements 7 and 8). Results of the most in-depth and complete interpretation of data are then reported for how often (number of fixations) and for how long (fixation durations), where (fixation locations) and in what temporal phases these fixations and durations occur (descriptive statements 9


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and 10). The number of fixations and fixation durations are important to researchers to assist in understanding where a person stops to look and for how long. Pursuit tracking of an object that is moving (such as the ball) is also salient to understanding a more complete picture of a participant’s visual selective attention. This chapter concludes with results of pursuit tracking within lookzone B (the Ball). Results are then interpreted via descriptive statements. Total Fixations and Durations (Descriptive Statement 1, 2, 3, 4). To test if players were on task, a broad measure of visual selective attention strategies was first examined via fixations and durations in total and within lookzones. AB participants had an average trial time of 6070 ms (SD = 0.79), and fixated an average of 8.08 times in each trial (SD = 3.40) and of these fixations most occurred within the lookzones (M = 7.57, SD = 1.15; see Table 9). WC participants with an average trial time of 2470 ms (SD = 0.39), fixated an average of 4.66 times per trial (SD = 1.57) and of these fixations most occurred, with little variation, within the lookzones (M= 4.41, SD = 0.28; see Table 9). The WC group was further subdivided into two groups: Predictive and Real Time groups. The WC Predictive group seemed to anticipate the ball toss ahead of time. The WC Real Time group did not seem to predict the ball toss ahead of time and instead viewed the model as he performed the action in “real time.” There was not much difference for wheelchair participants in the WC Predictive group compared to the WC Real Time group with regard to the total fixations and fixation durations (descriptive statements 1, 2, 3, 4; see Table 10). For instance, with regard to the average number of fixations, the WC Predictive group averaged 4.63 fixations per trail (SD = 1.53) and the


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WC Real Time group averaged 4.67 fixations per trail (SD = 1.58). For fixations within lookzones, the WC Predictive group averaged 4.54 fixations per trial (SD = 0.27) and the WC Real Time group averaged 4.40 fixations per trial (SD = 0.28). Fixation durations were identical within lookzones for the WC Predictive and WC Real Time groups averaging 430 ms (WC Predictive SD = 0.19; WC Real Time SD = 0.20). Fixation Locations (via Lookzones; Descriptive Statements 5, 6) AB participants fixated on the general body position (lookzone GBP) and predicted ball toss area (lookzone P) the least amount of time compared to other lookzones (see Table 11). The greatest number of fixations occurred in lookzone NDA (the non-dominant arm) (M= 2.42, SD = 0.87), followed by lookzone ARS, the arm, racquet shoulder area (M= 2.24, SD = 0.89). The longest fixation durations for AB were on the general body position (M= 575 ms, SD = 2.29) compared to other areas of the body. WC participants had the greatest number of fixations on both the arm, racquet and shoulder area ( M - 1.37, SD = 2.13; see Table 11) and the non-dominant arm (M= 1.21, SD = 2.02). WC participants spent the longest time (fixation duration) fixating within the predictive lookzone (M= 570, SD = 1.60). This was followed by the non-dominant arm (M= 490, SD = 1.45) and then the general body position (M= 480, SD = 2.13). It is important to note that when WC participants from both the Predictive group and the Real Time group were analyzed together, the standard deviations for the number of fixations in the predictive lookzone were very high (SD - 16.26; see Table 11). This is indicative of the broad spread of scores due to some participants predicting the ball toss location and others not using this prediction. Table 12 provides more details on the WC


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Predictive and WC Real Time group in terms of fixation locations. Compared to the WC Real Time group, WC in the Predictive group showed a greater number o f fixations in the predictive lookzone (M= 1.71) with less spread of scores within the group (SD = 1.79), indicating a group of WC tennis participants who have greater similarity in the amount and location of fixations within lookzone P (the predictive lookzone). Fixation durations for the WC Predictive group in the predictive lookzone were longer than in any other lookzone (M= 605 ms, SD = 0.53; see Table 12), indicating that for this group the predictive lookzone was a salient area within the videotape. WC Real Time group participants focused the majority of their fixations on the arm, racquet and shoulder (M = 1.72, SD = 0.81; see Table 12) followed closely by the non-dominant arm (M= 1.64, SD = 0.83). For WC participants in the Real Time group fixation durations were longest on the non-dominant arm (M= 600 ms, SD = 0.64). Differences in Temporal Phases o f the Serve (Descriptive Statements 7, 8) Each tennis serve was divided into a sequence of temporal phases beginning with the ritual phase, then the preparatory, execution and finishing phases. In taking into account the temporal phases of the tennis serve, results revealed that the ritual phase (the first phase) of the serve had the largest number of fixations and longest durations for both AB and WC tennis players (see Table 13). However, it is important to keep in mind that these temporal phases (all averaged over 18 trials) are not of equal duration for AB and WC players, or between serve phases, and looking at the data without consideration of the differences in the phase lengths can result in misleading interpretations. For AB tennis players (n = 31) the ritual phase was M = 4320 ms (SD = 0.93), the preparatory phase was 900 ms (SD = 0.20), the execution phase was 570 ms (SD = 0.37)


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and the finishing phase was 280 ms (SD = 0.11). Although there is no known relationship regarding amount of fixations per second for instance, it is important to note that simply due to the length of the ritual phase for AB participants it stands to logical reasoning that more fixations would be present during longer periods of time. A more complete way to interpret the salience of a particular phase of the serve is to look at the durations within each phase (see Table 13). Abernathy (1988) suggested that the longer a duration the more details can be extracted from the fixation location. This is important when interpreting temporal aspects of the tennis serve as the duration of the temporal phases may be misinterpreted if just examined via the number of fixations. For instance, in the preparatory phase (M= 900 ms, SD = 0.20) for the AB participants (n = 31), fixation durations were 505 ms (SD = 0.86). When considered with the number of fixations (M= 1.75, SD - 1.03) participants were fixating for almost (880 ms out of 900 ms) the entire length of this preparatory phase suggesting it provided information to the players (Abernathy, Wood & Parks, 1999; Singer et al., 1994). The same is true for the execution phase (M= 570 ms, SD = 0.37), where AB participants had an average of 1.41 fixations (SD = 1.04) and related average fixation durations were 375 ms (SD = 0.76) indicating participants fixated for almost the entire length (529 out of 570 ms) of the execution phase. WC tennis participants (n = 32; see Table 13) had the highest number of fixations during the ritual phase ( M - 2.00, SD =1.78) and fixations occurring during the ritual phase were for the longest durations (M= 550 ms, SD = 1.83). For WC participants, the average ritual phase was 1130 ms (SD = 0.14), the preparatory and execution phases (which occurred simultaneously) were 930 ms (SD = 0.18), and the finishing phase was


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410 ms long (SD - 0.13). The same note of caution applies with this group of participants when reporting temporal phase results as was explicated in the AB interpretation, as the various temporal phases differ considerably in duration. For instance, in the ritual phase (M= 1130 ms, SD = 0.14) for the WC participants, fixation durations were 550 ms (SD = 1.83). When considered with the number of fixations ( M 2.00, SD = 1.78) this suggests that the participants were fixating for almost (1100 out of 1130 ms) the entire length of this ritual phase. For the preparatory/execution phase (M= 930 ms, SD = 0.18), WC participants had an average of 1.78 fixations (SD = 0.76) and related average fixation durations were 410 ms (SD = 1.10) indicating that the participants fixated for almost eighty percent (78.50%) of the length (730 out of 930 ms) of the preparatory/execution phase, thus suggesting the ritual and preparatory/execution phases provided important information to the players (Abernathy et al., 1999; Singer et al., 1994). When the WC participants were separated into the WC Predictive (n = 17) and WC Real Time (n = 15) groups (see Table 14), the same trend occurred. The number o f fixations were most frequent and for the longest period of time for the ritual phase, then the preparation/execution phase followed by the finishing phase. Fixation Locations (via Lookzones) and Temporal Phases (Descriptive Statements 9, 10) For the most in-depth and complete interpretation of these data it is important to investigate not only at how many times fixations occur, or for how long, but also where participants fixate (lookzones) and at what temporal phases these fixations and durations occur (see descriptive statements 9 and 10; Tables 15-18). The total visual field for each serve was divided into five lookzones: the General Body Position (GPD), the Non-


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Dominant Arm (NDA), the Arm, Racquet and Shoulder (ARS), the Ball (B) and the Predictive lookzone (P). For AB participants (n - 31; see Table 15), the highest number of fixations (M= 1.19, SD = 2.32) and longest fixation durations (M= 705 ms, SD = 0.36) occurred in the ritual phase on the non-dominant arm (NDA). Most AB participants did not predict where the ball toss was going to occur ahead of time, as the average number o f fixations in the predictive lookzone (P) were below one fixation (M = 0.34, SD = 1.09). For the preparatory phase the non-dominant arm was also fixated on most frequently (M= 0.80, SD = 0.88) and for the longest duration (M = 660 ms; SD = 1.03). For the execution phase, the longest fixation duration (M= 475 ms; SD = 0.23) and highest number of fixations occurred while looking at the arm, racquet and shoulder region ( M - 0.80; SD = 0.57). During the finishing phase, the highest number of fixations were on the ball (M = 0.80; SD = 0.66), fixation durations were also longest on the ball (M= 220 ms; SD = 0.08). For WC participants (n = 32; see Table 16) the most number of fixations (M = 0.79, SD = 2.18) on the non-dominant arm occurred during the ritual phase. The non dominant arm also had the longest fixation durations (M= 670 ms, SD = 0.99) during the ritual phase for wheelchair participants. During the preparation/execution phase the highest number of fixations were on the non-dominant arm (M = 0.64, SD - 0.37) closely followed by the arm racquet shoulder (.M - 0.60, SD - 0.77). Fixation durations were also highest for these two areas during the preparation/execution phase (non-dominant arm M = 545 ms, SD = 0.72; arm, racquet and shoulder M = 510 ms, SD = 0.79). During


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the finishing phase the most fixations occurred on the ball (M = 0.39, SD = 0.23) for an average duration of 295 ms (SD = 1.08). For all WC participants (n = 32; see Table 16) the largest standard deviations occurred in the predictive lookzone during the ritual phase (SD =15.56). To gain a greater understanding of these data, the wheelchair group was divided into two groups (the Predictive and Real Time Groups): the Predictive group (n = 17; see Table 17) fixated the most number of times (M= 0.98, SD = 1.71) during the ritual phase in the predictive lookzone. The Predictive group also had the longest fixation durations in the predictive lookzone during the ritual phase (M= 730 ms, SD - 0.32). The standard deviation for the number of fixations were much lower (i.e. SD = 1.71) for the Predictive group compared to the standard deviation for all WC participants number of fixations (SD = 15.56) in the predictive lookzone. This indicates a group of participants in the WC Predictive group with more similar visual selective attention strategies than those of the entire group of WC participants. During the ritual and preparatory/execution phases, participants in the WC Predictive group (n = 17; see Table 17) had the second highest number o f fixations ( M 0.70, SD = 1.00) and longest fixation durations (M= 600 ms, SD = 0.32) on the non dominant arm compared to the predictive lookzone. During the finishing phase, WC Predictive group members had the highest number of fixations (M= 0.41, SD = 0.93) and longest fixation duration (M= 300 ms, SD = 0.98) on the ball. Participants in the WC Real Time group (n = 15; see Table 18) had the highest number of fixations (M= 1.01, SD —0.74) and longest fixation durations (M= 730 ms, SD = 0.99) during the ritual phase on the non-dominant arm. During the


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preparatory/execution phase, participants in the WC Real Time group fixated most frequently on the arm, racquet and shoulder region (M= 0.97, SD = 0.70) with fixation durations o f M = 550 ms (SD = 0.79). During the finishing phase the Real Time group fixated most often (M= 0.32, SD = 0.10) and for the longest duration (M= 290 ms, SD = 0.38) on the ball. Pursuit Tracking (Descriptive Statements 11 and 12) The number of fixations and fixation durations are important to researchers to assist in understanding where a person stops to look and for how long. Pursuit tracking (see Table 19 and 20) of an object that is moving (such as the ball) is also salient to understanding a more complete picture of a participant’s visual selective attention. AB participants (n = 31; see Table 19) pursuit tracked the ball for an average of 1115 ms (SD = 1.04; or 18.37% of the time) for each serve. The longest period of time pursuit tracking was during the ritual phase (M= 1015 ms, SD - 1.18; or 23.50% of the ritual phase) while the model bounced the ball. The second longest period of time AB participants pursuit tracked the ball was during the finishing phase, after the ball had been contacted and until it crossed the net (M = 51 ms, SD = 0.91; or 18.21% of the finishing phase). WC participants (n = 32) also spent time pursuit tracking the ball (see Table 19). The average amount of time spent pursuit tracking the ball per service motion was 460 ms (SD = 0.76; or 18.62% of the time). Wheelchair participants pursuit tracked the ball for the longest period of time during the finishing phase (M= 289 ms; SD - 0.22; or 70.49% of the finishing phase). The second longest period of time pursuit tracking the ball for WC participants was during the preparation/execution phase (M= 141 ms, SD = 1.33; or 15.16% of the phase). There was not much difference between WC tennis


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players from the WC Predictive group (n = 17; total average pursuit tracking time M 450 ms, SD = 0.37, 18.22%) and the WC Real Time group (n = 15; total average pursuit tracking time M = 460 ms, SD = 0.62, 18.62%) when pursuit tracking the ball during the serve (see Table 20). Concluding Questionnaire At the conclusion of testing, participants were asked to complete a questionnaire for verification of the participants’ thoughts while watching the video (see Appendix E). This concluding questionnaire also served to assess the realism participants thought the video portrayed. Participants were asked “When watching the video were you thinking about competing in a real match against this person? (Question 1; see Table 21).” Participants who answered “no” to this question (or question 2) were excluded from the final analyses. Results revealed that 70% of AB participants (n = 31) and 74% of WC participants (n = 32) answered “yes” to this question. Hence 30% of AB participants (n = 13) and 26% of WC participants (n = 11) were excluded from final analyses as they answered “no” to this question. Participants were also asked “when watching the video were you thinking about trying to return the serve as effectively as possible ideally making it difficult for the server to return? (Question 2; see Table 21).” Participants who answered “no” to this question were also excluded from the final analyses. Results revealed that 70% of AB (n = 31) and 74% of WC participants (n = 32) answered “yes” to this question. Hence 30% (n = 13) of AB participants, some of whom had also answered “no” to question 1, were


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excluded from the final analyses and 27% (« = 11) of WC participants were also excluded. Participants were also asked to assess the realism of the video, via the following question; “Did you feel that the image you saw was a realistic comparison to what you see when on the tennis court? (Question 3; see Table 21).” For AB participants (48% or 21 out of 44) and 51% (22 out of 43) of all WC participants felt the video was “somewhat realistic.” Another question in this post testing questionnaire asked participants “How realistic do you feel it is to assume you are competing in a real match against this player and to be thinking about trying to return the serve as effectively as possible ideally making it difficult for the server to return? (see Table 21)” Half of the AB participants (50% or 22 out of 44) and three quarters of the WC participants (75% or 32 out of 43) felt this was “somewhat realistic.” Analyses o f Results via Descriptive Statements In summary, results were interpreted for AB and WC groups as they related to the descriptive statements. Statements 1 and 3: Average number o f fixations in total and within lookzones (1 + 2 + 3 + 4 +5) fo r the entire length o f the serve. Results indicated that both AB (n =31) and WC participants (n = 32) were on task. That is, the majority of the fixations were within the designated (lookzones) pertinent areas of the screen (see Tables 9 and 10).

Statements 2 and 4: Average fixation durations in total and within lookzones (1 + 2 + 3 + 4 + 5) fo r the entire length o f the serve. As the majority of fixations occurred


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within the designated lookzones for both AB (n = 31) and WC (n = 32) participants there was little variations in the fixation durations for the entire length of the tennis serve compared to within lookzones (see Tables 9 and 10). WC and AB participants had very similar fixation durations within lookzones (AB, M = 432 ms, S D - 1.10; WC M = 430 ms, SD = 0.20). Statements 5 and 6: Average number offixations andfixation durations fo r each lookzone (lookzone 1, 2, 3, 4, 5). For AB (n = 31) and WC (n = 32) participants the highest number of fixations and longest fixation durations (irrespective of temporal phase) were on the non-dominant arm, then the arm, racquet and shoulder region (see Table 11). The WC Predictive group (n = 17; see Table 12) had the highest number of fixations and longest fixation durations on the predictive lookzone (P) followed by the non-dominant arm. The WC Real Time group (n= 15) had the highest number of fixations and longest fixation durations on the non-dominant arm and arm, racquet and shoulder (see Table 12) the same as AB participants. Statements 7 and 8: Average number o f fixations within each temporal phase o f the serve (see Table 13 and 14). A note of caution in that temporal phases are not of equal duration and when taking into consideration the length of phase, number of fixations and fixation durations the following results were found: a) AB participants (n = 31) fixated for the longest period of time within a phase during the preparatory then execution phases b) WC participants (n - 32) fixated for the longest period of time within a phase during the ritual then preparatory/execution phases respectively


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Results indicated the pertinence of these phases in the visual selective attention of expert AB and WC groups. Statements 9 and 10: Average number offixations and fixation durations in each temporal phase fo r each lookzone (lookzone 1, 2, 3, 4, 5): a) For AB participants (n = 31; see Table 15), during the ritual and preparatory phase the highest number of fixations and longest fixation durations occurred on the non-dominant arm. During the execution phase the highest number of fixations and longest fixation durations were on the arm, racquet and shoulder region, and during the finishing phase on the ball. b) For WC participants (n = 32; see Table 16), as a whole and during the ritual phase the highest amount of fixations and longest fixation durations occurred on the non-dominant arm, during the preparatory/execution phase on the non dominant arm closely followed by the arm, racquet and shoulder area and during the finishing phase on the ball. The WC Predictive group (n = 17; see Table 17) had the highest number of fixation and longest fixation durations during the ritual phase on the predictive lookzone, during the preparatory/execution phase on the predictive lookzone and the non-dominant arm, and during the finishing phase on the ball. The WC Real Time group (n = 15; see Table 18) had the highest number of fixation and longest fixation durations on the non-dominant arm during the ritual phase then the arm, racquet and shoulder during the preparatory/execution phase and then the ball during the finishing phase.


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Statements 11 and 12: Pursuit tracking within lookzone B (the Ball) fo r the total service time and all phases o f the serve. For AB participants (n = 31; see Table 19) a total of 18.37% of the total service time was spent pursuit tracking the ball. The ritual phase then the finishing phase had the highest percentage of time pursuit tracking. For WC participants (n = 32; see Tables 19 and 20) a total of 18.62% of the total service time was spent pursuit tracking the ball. The finishing phase had the highest percentage (70.49%) of any phase for either group pursuit tracking the ball.


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CHAPTERV Discussion

This research investigated the visual selective attention patterns of expert ablebodied (AB; n = 31) and wheelchair (WC; n = 32) tennis players. Results are interpreted as they relate to past research (where it exists) as well as from experts in the field and responses from participants in the study. Limitations of this study and future research implications are also discussed. This chapter discusses results from AB participants followed by results from WC participants. Similarities and differences between these groups are then explored. At the conclusion of this chapter the use of ERICA is discussed, including its advantages and limitations as well as suggestions for future researchers who may use the system for investigating visual search strategies in sport. Limitations of the test film and testing environment are also explicated, and areas for future research are discussed. This chapter concludes with a summary of the dissertation. Visual Search Strategies for Able-Bodied (AB) Tennis Players Past research on visual search strategies of AB tennis players who had been classified as experts was used for comparison and discussion (Goulet et al., 1989; Singer et al, 1996; 1998; Tenenbaum et al, 1996). While there are similar trends in results between the current research and past literature, there are also some differences. These will be discussed especially as they pertain to pertinent areas of interest such as the non dominant arm the arm, racquet and shoulder area, and the predictive area where the ball is expected to be tossed. Fixations, Durations and Locations


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No past research was found specifically related to the average number o f fixations or fixation durations per trial for the tennis serve. However, for some points of reference regarding these broadest measures of visual selective attention strategies, Williams et al. (2002) reported that the average number of fixations was 7.00 (SD = 0.50) for a 4 s presentation of a tennis groundstroke, the average number of fixations within the allocated areas of interest was 4.70 (SD = 0.4) and the average fixation durations recorded when participants watched the tennis groundstrokes was 442.30 ms (SD = 92.7). The current study produced comparable numbers (see Table 9; see descriptive statement 1 - 4) for the average number of fixations and fixation durations while watching videotapes and within lookzones. For instance, an average of 8.08 (SD = 3.40) fixations occurred for the AB participants (n = 31) and 7.57 (SD = 1.15) of these fixations were somewhere on the person (within any of the lookzones). Interestingly the average fixation durations (M= 432 ms, SD = 1.10) within lookzones were similar to those found by Williams et al. These are important broad reference points and might also be considered as preliminary checks that the ERICA system worked correctly and that participants were properly calibrated and had not moved outside the specified range for the equipment. It is important to analyze where participants looked on the model servers body, and analyze the average numbers of fixations and average fixation durations (Descriptive Statements 5 and 6; Table 11). For AB participants (n = 31) the highest number of fixations (M= 2.42, SD = 0.87) occurred on the non-dominant arm, closely followed by the arm, racquet and shoulder area (M= 2.24, SD = 0.89). Past research by Singer et al. (1998) and Tenenbaum et al. (1996) found that the greatest number of fixations occurred on the arm, racquet and shoulder area for the highest level experts. Past research (e.g.


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Goulet et al., 1989) has also found the non-dominant arm as important in certain temporal phases of the serve (i.e. the preparatory phase). Hence the non-dominant arm and the arm, racquet and shoulder area seem to be important for expert tennis players to focus. However, the overall number of fixations and fixation durations for the entire service motion on the non-dominant arm have not been found to be as high, in past research, as this study found. More research is needed to help understand this difference. It is important to look not only at the number of fixations on a particular location to determine its importance, but also fixation durations (see Table 11) and pursuit tracking (see Table 19). When taken together, the ball was “looked at” for the longest period of time over the entire duration of the serve. This result is consistent with Singer et al. (1998) who found that experts spent the most amount of time looking at the ball. It is especially important to assess where visual attention is focused at various temporal phases of the serve. In interpreting these results, it is imperative to take into account the duration of each phase, as they differ considerably, and without consideration of their length misinterpretations may be made. A different way to determine importance of a phase o f the serve is to occlude various temporal elements and ask participants to make judgements about direction, depth or spin on the ball. Results from past temporal occlusion studies help shed light on the current findings. Goulet et al. (1989) used temporal occlusion when testing participant’s number of correct responses to the direction of the tennis serve. Results from Goulet et al.’s study revealed that the preparatory phase and the first part of the execution phase provided the most valuable information to the expert preparing to return a serve. In other words, when these phases were occluded the participant had the least number of correct responses. What is


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interesting when looking at the results of Goulet et al. compared to the present study is that the preparatory phase and execution phase had the most number of fixations and longest related durations per phase (see Table 13). For instance, in the preparatory phase (M = 900 ms, SD = 0.20), AB participants (n = 31) fixated an average of 1.75 times (SD = 1.03) and these fixations were 505 ms (SD = 0.86) long, hence the fixations occurred on average for 884 ms, out of a possible 900 ms, within this phase of the serve. In the execution phase ( M - 570 ms, SD = 0.37) AB participants fixated and average of 1.41 times (SD = 1.04) and these fixations were 375 ms (SD = 0.76) long, such that the fixations occurred for 529 ms out of a possible 570 ms, within this phase of the serve. Hence given the number of fixations and the related fixation durations participants used almost the entire length of the preparatory and execution phases to stop and look for long periods of time. Abernathy (1988) specified that the longer a fixation the more information can be extracted and hence the more important the fixation is in determining the outcome of a motion. Therefore the preparatory and execution phases, when examined in terms of the length of the phase, the number of fixations and their related durations, supports the findings of Goulet et al. about the importance of the preparatory and execution phases for anticipating service outcome. AB participants also fixated for approximately 82% of the finishing phase (229 ms out of a possible 280 ms) but less than half the ritual phase (2096 ms out of a possible 4320). Fixation Locations during Temporal Phases For the most complete picture of visual selective attention strategies of expert AB tennis players it is important to examine the number of fixations and durations of fixations within each temporal phase and location of the serve (see Tables 15). During


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the ritual phase of the serve, results revealed that AB participants had the most frequent fixations, of the longest duration, on the non-dominant arm. The second most frequent fixations with the longest duration was on the general body position, suggesting that both the non-dominant arm and general body position are pertinent when watching a server during the ritual phase. Goulet et al. (1989) found that experts also organized their search around the general body position during the ritual phase, though they did not focus on the non-dominant arm until the next phase (the preparatory phase). Perhaps the difference in the finding regarding fixations on the non-dominant arm from the current study to Goulet et al.’s research during the ritual phase is an indication of the difference in the definition of “participant expertise” in the two studies. Participants in Goulet et al. were ranked in the top 40 in Quebec or were currently or previously ranked players in the Quebec area; however none of these players had world rankings, compared to this research in which AB participants ( n - 31) were ranked 44 - 440 on the ATP or WTA tours. Another possible explanation for the difference in the focus on the non-dominant arm during the ritual phase may be the difference in the game of tennis from 1989 (when Goulet et al.’s research was conducted) to 2003 (when the current research was undertaken). For example, one difference has been the introduction of Titanium racquets that are lighter and more powerful. Technological advancements on the tennis racquet have allowed servers on the ATP tour to serve the ball up to and beyond 130 mph at relatively regular intervals. Focusing on the head and shoulder/trunk complex, that Goulet et al. specifically refers to as the general body position, would not seem to give the serve returner any useful or pertinent information on the direction or spin of an oncoming ball. Perhaps the highest level expert eliminates the head and shoulder/trunk complex from


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his/her visual search. Instead the player may focus on an area that provides more pertinent information, even if not during the ritual phase then they may look ahead to pertinent areas during the preparatory phase. A limited amount of support for the prediction of looking ahead may be found in a small number of AB expert tennis players who spent 630 ms (SD = 0.23) looking in the predictive lookzone (Table 15) during the ritual phase, indicating an anticipation of the movement to come. Experts in today’s game o f tennis may be looking ahead, specifically during the ritual phase to pertinent areas of the preparatory phase because the service motion and oncoming ball is coming faster than ever before. During the preparatory phase, AB tennis players (n = 31) fixated most frequently and for the longest period of time on the non-dominant arm (see Table 15). Participants also pursuit tracked the ball (see Table 19). Singer et al. (1996) and Goulet et al. (1989) also found that player’s pursuit tracked the ball during the preparatory phase. Goulet et al. found experts used a combination of fixations and pursuit tracking during the preparatory phase. Consistency between these research studies indicated that following the ball during the tossing portion of the serve is a consistently important visual search strategy undertaken by expert AB tennis players. Results revealed that experts AB players fixated on the non-dominant arm as well as pursuit track the ball during the preparatory phase. Focusing on the non-dominant arm when tossing the ball is important because the non-dominant arm moves in the form of an arc and the ball is released during the arc. Looking at the non-dominant arm as it is elevated is an important indicator of the relative timing and position of where the ball is going to be tossed. For the expert performer, fixating on the non-dominant arm would


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therefore seem appropriate. Goulet et al. found that experts used a combination of fixations and pursuit tracking of the ball on the non-dominant arm and the predicted ball toss area. These results are consistent with the current research and provide information to the viewer for important areas of interest about where experts focus their attention during the preparatory phase of the tennis serve. During the execution phase, the longest fixation durations and most frequent fixations occurred while looking at the arm, racquet and shoulder region (see Table 15) for expert AB players (n = 31). This is consistent with past research by Goulet et al. (1989), Tenenbaum et al. (1996), Singer et al. (1996) and Singer et al. (1998). Consistency between these studies provides increasingly strong evidence for the pertinence of the arm, racquet and shoulder region of the server during the execution phase. During the finishing phase of the serve the largest number of fixations were on the ball (see Table 15). AB participant’s (n = 31) pursuit tracked the ball for 51 ms (out of a possible 280 ms) during this phase of the serve. Results taken together of the number of fixations (M = 0.80, SD = 0.66), fixation durations (M= 220 ms, SD = 0.08) and pursuit tracking (M= 51 ms, SD = 0.91) indicate that the ball was a pertinent location during the finishing phase of the serve. Past research (Goulet et al. 1989) has generally stopped analyzing visual selective attention patterns at the ball/racquet contact area (the end o f the execution phase). Singer et al. (1998) looked beyond the ball/racquet contact area and recorded visual search after the ball was hit. In a test of equipment in a real life setting, Singer et al. found that after the ball had been contacted, college level experts pursuit tracked the ball 100% of the time. No fixations were recorded. Results from Singer et al.


and this research show that the ball is important during the finishing phase, however it seems that visual search behaviors (i.e. fixations, verses pursuit tracking verses saccades) are different. Clearly more research needs to be conducted on this phase of the tennis serve. There is also another possible explanation where further testing would help rectify the differences between Singer et al. (1996) and the current results. Results from this study revealed that AB participants fixated the second highest number o f times and for the second longest period of time on the arm, racquet and shoulder region of the server during the finishing phase (see Table 15). Although a different skill, it is intriguing to note that professional level baseball hitters focused on the arm and hand of the pitcher after the ball had been released (Shank & Haywood, 1987). This enabled the hitter to better determine the spin on the ball. It is possible that the expert tennis players in this study were doing the same thing. In other words, these players continued to focus on the arm, racquet and shoulder area to see the completion of the service motion. This may have helped them determine direction and spin on the ball and then later they caught up to the ball and began pursuit tracking it as it came closer to the net. Summary o f Able-Bodied Results Results gathered from AB participants (n = 31) revealed a systematic and consistent pattern of visual selective attention. During the ritual phase, the greatest number of fixations and the longest durations occurred on the general body position and the non-dominant arm, then during the preparatory phase m oved almost exclu sively to

the non-dominant arm. During the execution phase they occurred on the arm, racquet and shoulder and then finished watching the ball and service motion. These results are mostly consistent with past research from Goulet et al. (1989), Tenenbaum et al. (1996),


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Singer et al. (1996) and Singer et al. (1998). Some differences such as visual search behaviors were also present and possible explanations exist due to the different levels of “expert” performers used on each of the studies. More research is needed to clarify these predictions. Visual Search Strategies fo r Wheelchair (WC) Tennis Players This research examined a group of expert WC tennis players (n = 32) who had not yet been studied with regards to their visual selective attention strategies. Results found are intriguing however, as this is the first time WC tennis players have been tested, there was no past literature for comparison. An understanding of the game of WC tennis by certified coaches and professional players helps shed insight into some of these results. Additionally, a follow-up questionnaire (discussed in depth in this section) also helps to further examine results from this group of participants. Fixations, Durations and Locations Wheelchair tennis participants (n = 32) fixated an average of 4.66 (SD = 1.57) times per service presentation (see Table 9). Almost all of these fixations were somewhere on the server, as they were within the lookzones, suggesting that the participants were paying attention to the model and the service action while watching the videos (see Table 9-10). WC participants had the most number of fixations on the arm, racquet and shoulder area and the non-dominant arm (see Table 11-12). Suggesting these locations are important areas to look at during the service motion. When separating the WC participants into two groups (the WC Predictive group (n= 17) and the WC Real Time (n = 15) group) these two locations continued to be important for both groups. In addition, the WC Predictive group had the most number of fixations and longest fixation


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durations in the predictive lookzone. As the predictive lookzone occurred only for the first portion of the serve (during the ritual phase and beginning of the preparatory phase) this result indicated that the WC Predictive group looked at the predictive lookzone indicating its importance at the beginning of the serve. When taking into consideration the temporal aspects of the serve, WC participants (n - 32) focused the most number of times and for the longest duration of time in the ritual phase. However, for WC participants, the ritual phase was almost 1/3 longer than the preparatory phase of the serve, and almost three times longer than the finishing phase of the serve. It is imperative when analyzing temporal aspects of the serve to recognize that the phases are of different lengths. For instance, in the ritual phase (M= 1130 ms, SD = 0.14) WC participants fixated an average of 2.00 times (SD = 1.78) and these fixations were 550 ms (SD = 1.83) long, hence these fixations occurred for an average of 1100 ms, out of a possible 1130 ms, within this phase of the serve. For the preparatory/execution phase, fixations occurred for an average of 730 ms out of a possible 930 ms, and for the finishing phase participants were fixating for an average of 117 ms out of 410 ms. As specified earlier (Abernathy, 1988) the longer a fixation the more information extracted from the fixation location. Therefore for the most complete picture it is important to examine the number of fixations, fixation durations and related phases and locations (Descriptive Statements 9 and 10; Tables, 16-18) and also pursuit tracking behaviors (Descriptive Statements 11 and 12, Tables 19 - 20) discussed in detail in the next sections. Fixation Locations during Temporal Phases WC participants, when grouped together (n = 32), had the greatest number of fixations and longest fixation durations on the non-dominant arm during the ritual phase


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of the serve (see Table 16). During the preparatory/execution phase the highest number of fixations and longest fixation durations were on the non-dominant arm, followed closely by the arm, racquet and shoulder area. During the finishing phase the most frequent fixations occurred on the ball. WC participants pursuit tracked the ball for more than half the length of the finishing phase (see Table 19-20) indicating the ball was an important source of information during the finishing phase of the serve. WC participants (n = 32) were within one standard deviation of most locations of fixations indicating a fairly low spread of scores for the group and relatively consistent visual selective attention patterns throughout the temporal phases of the serve (see Table 16). One exception to this was in the predictive lookzone, where standard deviations were very high (Table 16, SD = 15.56). Hence the WC group was divided to incorporate participants who used the predictive lookzone (the WC Predictive group; n = 17) and those who did not (the WC Real Time group; n = 15). When separated, the WC Predictive group (n = 17) fixated the most number of times and for the longest period of time in the predictive lookzone during the ritual phase of the serve (see Table 17). The non-dominant arm was also important during the ritual phase for the WC Predictive group as the number of fixations and fixation durations closely trailed the predictive lookzone. During the preparatory/execution phase, the predictive lookzone had the highest number of fixations closely followed by the non dominant arm. It is important to remember that the predictive lookzone disappeared when the non-dominant arm was waist high, at an average of 490 ms (SD = 0.39) into the preparatory/execution phase. Fixation durations for the WC Predictive group were longest on the non-dominant arm during the preparatory/execution phase compared to the


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predictive lookzone or any other lookzone. During the finishing phase the greatest number of fixations and the longest fixation durations occurred on the ball. Participants in the WC Predictive group pursuit tracked the ball for over half the duration of the finishing phase (291 ms; SD = 0.22; see Table 20) indicating the importance of the ball during this phase of the tennis serve. Results from the WC Real Time group (n= 15) revealed differences in the temporal phases and locations compared to the WC Predictive group (n - 17). For the ritual phase, the WC Real Time group had the most frequent fixations and longest durations on the non-dominant arm, followed by the arm, racquet and shoulder area (see Tables 18). During the preparatory/execution phase, WC Real Time participants had the greatest number of fixations with the longest durations on the arm, racquet and shoulder area. For the finishing phase WC Real Time group participants focused most often and for the longest period of time on the ball. WC Real Time group participants pursuit tracked the ball during the finishing phase for more than half the duration of the phase, 291 ms (SD = 0.22; see Table 20). Results of the WC Predictive group (n = 17) compared to the WC Real Time group (n= 15) were intriguing. It seems that the WC Predictive group was visually tracking ahead of the ball for the ritual and first portion of the preparatory phase. Perhaps these participants felt that this particular strategy would enable them to determine the direction and spin on the ball faster than if they waited to actually see the performance of the model. By predicting ahead of time, perhaps these WC players felt they would be able to initiate a movement more quickly where as if they waited to see what the model actually did they may not have time to move due to limited lateral mobility.


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Several questions surfaced when examining differences between the WC Predictive in - 17) and WC Real Time (n = 15) groups. For example, why did only half the WC group use this predictive strategy and the other half use a real time strategy? Was the predictive strategy one that some WC players had used because of their long time or experience in playing WC tennis? Was a predictive strategy used by WC players to adapt to movement constraints? Did differences in these groups occur because prior to getting hurt some players (particularly in the WC Real Time group) had played AB tennis and were using visual selective attention strategies similar to when they played AB tennis? Did some WC tennis players only ever play tennis from a WC leading to different visual selective attention than if they initially had played AB tennis? Or perhaps it was a difference in cues used by WC player’s coaches? Were these WC coaches AB or WC bound? Did they teach from a WC or standing up? Although all these questions are important and would help to further understand differences between the WC Predictive and the WC Real Time group, they cannot all be answered in this dissertation. Further research needs to be conducted to answer these questions. However, to try and shed some preliminary understanding on these questions a follow-up questionnaire was constructed asking for feedback from the WC participants (see Appendix G). A total of 20 participants responded to this follow-up questionnaire, 9 from the WC Real Time group and 11 from the WC Predictive group. WC follow-up questionnaire. The first question on the follow-up questionnaire asked participants how long they had been playing wheelchair tennis. The WC Real Time group (n = 9) had been playing wheelchair tennis almost five years less than WC Predictive group (n = 11; see Table 22). Perhaps the difference between these groups had


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to do with the number of years actually playing WC tennis. Perhaps the strategy undertaken by the WC Real Time group was simply a progression in learning and they had not actually mastered the most effective visual search strategy for WC tennis as they had not played the game for as long as the WC Predictive group. Future research would best be able to test for differences based on years of experience. For instance, using an occlusion method, participants from the WC Real Time group could be compared to participants in the WC Predictive group and tested in relation to the number of correct responses to direction and spin of a serve. When asked if participants had played AB tennis before participating in WC tennis (Question 2; see Table 22), approximately 1/3 (3 out of 11) of the respondents from the WC Predictive group answered ‘yes’ compared to 2/3 (6 out of 9) of the respondents from the WC Real Time group. As a follow up question to number 2, if WC participants answered “yes” they were asked to specify how long they had played AB tennis. Participants from the WC Predictive group who had played AB tennis, before their injury, did so for approximately half the time (M= 2.3, SD = 2.08) compared to participants in the WC Real Time group (M= 4.8, SD - 1.79). These are important differences and can provide an initial insight to understanding where visual cues and visual selective attention strategies were first developed. When a person learns a skill they develop visual selective attention strategies to assist them in responding to an opponent’s action. W hen this person continues to practice and play the skill using these

cues it is very difficult to change. The game of WC tennis is different from that of AB tennis, this is especially evident when watching the tennis serve. Given this fact, it is quite possible that tennis players who were once AB continue to use AB visual selective


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attention strategies after becoming disabled. This would become more likely the longer they played AB tennis. Using this reasoning a possible explanation of the differences between the WC Predictive and WC Real Time group can be at the very least presented. As the WC Real Time group had more time playing AB tennis it is possible that they developed different visual selective attention strategies compared to the WC Predictive group who had not had the same longevity playing AB tennis. Question 3 asked participants if they had a tennis coach. Most WC participants who responded to the follow-up questionnaire from both the WC Predictive (n = 11) and WC Real Time in = 9) groups had tennis coaches (see Table 22). Over 85% of the coaches for both groups were AB people (WC Predictive group 10 out o f 11; WC Real Time group 8 out of 9). Given the fact that most people who teach/coach WC tennis are AB, it is likely that they have an AB perspective on the game of tennis and in turn use AB cues, strategies and terminology even when working with their WC players. To gain a more specific knowledge of these coaches, participants were asked if their coach teaches from a WC? Both groups responded overwhelmingly “no” (see Table 22). Only 12% (1 out of 9) of the coaches from the WC Real Time group taught from a WC, while 18% (2 out o f 11) of the coaches teaching participants from the WC Predictive group did so from a WC. As previously mentioned teaching from an AB view point may produce a completely different perspective of the tennis court than when seated, not to mention a lack of understanding of the mobility constraints experienced by WC tennis players. This may influence a coach’s teaching method, cues and visual selective attention as they are teaching WC tennis players from a perspective of an AB player.


On a similar vein as the previous question, WC participants were asked if their coach practiced playing with them from a WC. Although no more than 45% of the coaches played tennis with the participants from a WC the percentages of coaches playing tennis against participants from a WC was higher than coaching from a WC. Fourty-five percent (5 out of 11) of coaches who taught players in the WC Predictive group actually practiced against the players from a WC while only 33% (3 out of 9) of the coaches from the WC Real Time group practiced playing with the participant from a WC. This is important because when practicing from a WC it gives the WC participant a visual image and perspective of playing another WC athlete. It likely enables them to use fixation points and related cues associated with WC tennis. The difference between the WC Predictive group and the WC Real Time group may be due in part to coaches who are providing themselves as WC tennis models. Modeling literature suggests that models who are most similar to a learner (or participant) provide the most effective learning experience (McGullaugh, 2001). Coaches playing tennis from a WC with participants may not just increase motor learning components but also components of learning that are less evident to the naked eye such as cognitions. Part of cognitive learning is visual selective attention, which may be influenced positively to increase learning of WC participants. Most of the coaches (63%; 7 out of 11) who taught participants in the WC Predictive group were certified specifically in WC tennis by an accredited WC tennis certifying association while only 33% (3 out of 9) of the participants in the WC Real Time group had coaches who were certified by a WC tennis certification association. This is important for several reasons. A coach who is certified from an accredited


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association is likely to have more knowledge of WC tennis that will assist the WC player in the nuances of the game of WC tennis in particular. WC tennis certification associations are also likely to give the coach some time in actually playing tennis from a WC. This gives the coach some reference points, cues and knowledge of the similarities and differences between AB and WC tennis. Through this understanding, the coach can tailor teaching environments for WC players to WC (and not AB) tennis players. In doing so, this can influence cues, reference points and visual selective attention strategies of WC tennis players. In the follow-up questionnaire WC participants were asked if they visualized or imagined themselves playing tennis as an AB or WC tennis player? They were also asked if when they dream about tennis do they do so from an AB or WC tennis perspective? The reason these two questions were asked is because visualization and imagery enhances performance (Blair, Hall & Leyshon, 1993). Visualization can lead to cue development (Cogan & Petrie, 1995; Daw & Burton, 1994). In conversation with several athletes including Randy Snow (personnel communication, December 2, 2003) and Carma Lee Lewellen (personnel communication, December 18, 2003) and Linda Bunker (personnel communication, December 19, 2003) all spoke of visualizing and imagining themselves as AB. This was intriguing and perhaps may also lead to a partial explanation of the differences between the WC Predictive and WC Real Time groups, should they visualize themselves differently. For the WC Predictive group, 63% (7 out of 11) of the participants surveyed visualized themselves as playing tennis from a WC and 46% (5 out of 11) of these participants dreamed about themselves playing tennis from a WC. For the WC Real


Time group 33% (3 out of 9) of participants visualized themselves as playing tennis from a WC and 21% (2 out o f 9) dreamed of themselves as playing tennis from a WC. Perhaps differences between the WC Predictive and WC Real Time group are due to differences in how players view themselves on the tennis court. As these results suggest, the WC Predictive group generally visualizes and dreams of themselves as WC tennis players, hence when they watch and practice tennis from their “minds eye” they are doing so from the perspective of playing tennis in a WC. The majority of the WC Real Time group however, has an incongruence between the visual image of themselves playing tennis as an AB person (79%; 7 out of 9; hence reinforcing tennis playing from a standing position) compared to real life tennis playing as a WC player. This may lead to the use of visual selective attention strategies that seem more like those of AB athletes than WC tennis players. Further evidence and validation of this line of thinking may also be sought from the difference between the WC groups in terms of years playing AB tennis, as the WC Real Time group had played AB tennis twice as long as the WC Predictive group (Question 2, Table 22). It is interesting that the majority of WC players, in both groups, dreamed of themselves as AB players. More research needs to be conducted in order to see if this finding can be generalized. In light if these results however, future intervention strategies would be wise to incorporate imagery and visualization in order to establish relevant cues specific to WC tennis. Summary o f Wheelchair Results WC participants were divided into two groups: WC Predictive ( n - 17) and WC Real Time (n = 15). The WC Predictive group predicted the ball toss ahead of time


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during the ritual and beginning portion of the preparatory/execution phase. During the preparatory/execution phases these participants focused on the non-dominant arm and then during the finishing phase focused on the ball. In contrast, WC Real Time participants began focusing on the non-dominant arm (during the ritual phase, then shifted focus to the arm, racquet and shoulder and then ball. Differences between these groups were discussed in terms of the years of the WC tennis playing experiences. More research is needed for a greater understanding of the potential link between experience in WC tennis and visual selective attention strategies. Observations about Wheelchair (WC) and Able-Bodied (AB) Results This dissertation is descriptive in nature and therefore does not provide inferential statistical analyses of a comparison between AB participants and WC participants. As previously mentioned, it was felt that inferential analyses were not appropriate due to the two distinctly different videotapes watched by the WC and AB groups (see Chapter 3, Methods section, Data Analyses for details). However, this discussion would be incomplete without speaking of the similarities and differences between the groups, for example in fixation locations as they impact future coaching strategies in helping players learn visual selective attention strategies. Fixations, Durations and Locations Both AB (n = 31) and WC (n = 32) participants had the majority of their fixations on the model during the service motion (see Tables 9-10) indicating that both groups looked in the relevant areas of the video clip. Fixation durations for both groups, within lookzones were almost identical (see Table 9). AB participants fixated an average of 432 ms (SD = 1.10) when looking within any lookzones and WC participants fixated an


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average of 430 ms (SD = 0.20). WC participants had a more narrow spread of scores (smaller SD) compared to AB participants, which indicated that participants in the WC group had greater similarity in their fixation durations within lookzones compared to AB participants. AB (n = 31) and WC (n = 32) participants had the greatest number of fixations on the non-dominant arm and the arm, racquet and shoulder area (see Table 11-12). This suggested that these areas were important to serve returners to fixate on during the AB and WC tennis service motion. When examined by temporal phases some interesting results were revealed especially in terms of differences in search rate (number of fixations within a time period or phase) and visual search behavior (i.e. fixations verses pursuit tracking) as well as fixation locations. Both groups AB (n = 31) and WC (n = 32) had the highest number of fixations during the ritual phase (see Table 13-14). This was not surprising as the ritual phase was longer than any other phase of the serve for both groups. What was interesting was that when taken together, the number of fixations and fixation durations during the preparatory and execution phases revealed that both groups spent almost the entire duration of the phases fixating. Fixating for the majority of time over a temporal phase indicates the phase’s importance in determining the outcome of a movement. According to Abernathy (1988) the longer a fixation the more specific information extracted from the location and in turn the more important the fixation becomes in determining the result of the action. Past research literature lends support to the importance of these fixations in different phases. For example, Goulet et al. (1989) found that when occluding the preparatory phase and the beginning portion of the execution phase, AB participants were


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less accurate in determining the direction of an oncoming serve. Where participants are looking within these phases in order to determine outcome of the serve is the most important question for researchers, teachers, coaches and aspiring athletes, and will be discussed in the next section. One other interesting way to interpret the results of AB (n = 31) and WC (n = 32) participants was to examine the search rate (i.e. how many times one group stopped and changed where they were looking compared to the other group). When examining temporal phase results, AB tennis players fixated during the ritual phase (M = 4320 ms, SD = 0.93) an average of 3.30 times (SD = 2.19), resulting in one fixation every 1309 ms. When examining the search rate of AB tennis players in the other phases of the serve the following results were revealed: one fixation every 514 ms during the preparatory phase, and one fixation every 404 ms during the execution phase, and finally one fixation every 256 ms in the finishing phase. WC participants fixated during the ritual phase (M = 1130 ms, SD = 0.14) an average of 2.00 times (SD = 1.78), resulting in one fixation every 565 ms. During the preparatory/execution phase, one fixation was detected every 522 ms, and in the finishing phase, one fixation every 80 ms. When interpreting the results in this manner it appeared that WC tennis players had a much faster search rate than AB tennis players during the ritual phase and finishing phases. However, reporting results in this fashion are done with caution because each group had different video presentations and there is no known relationship between fixations and fixation durations hence these results cannot be conclusive. To illustrate this point further, although AB persons fixate only once every 1309 ms during the ritual phase perhaps this is not due to a slower search rate but simply due to having more time than the WC tennis players in this phase of the


serve. It is not necessarily the case that for the first 1309 ms they fixated once, then the second 1309 ms they fixated once and so on so forth. Perhaps they fixated three times in the first 1309 ms and then no other times the rest of the phase, as they had already gathered all the information they needed during the first part of the phase, and the rest of the phase was simply extra (unnecessary, non pertinent) time. Hence, presenting the data in this way needs to be reported and examined with caution especially as it applies to possible search rate differences between WC and AB participants as the time of each phase is considerably different. A future research study in this area would be very interesting. For example, examining fixations by time as they occur (irrespective of phase), for instance, when (and for how long) did the first fixation occur for AB verses WC participants, and then the second fixation and so on. This would be the most detailed form of investigating the search rate question, and probably the most accurate. It is also important to remember that the temporal phases used in this study were taken from Groppel’s (1980) work with advanced AB players, using an analysis of these players biomechanics. Results from the current study showed differences in the temporal phases for WC tennis compared to AB participants. Hence examining fixations as they occur (one at a time) would be the most detailed and accurate representation of search rate. Fixation Locations during Temporal Phases For a more specific and in-depth look at the similarities and differences between the visual search strategies of AB (n = 31) and WC (n = 32) tennis participants, an examination of where participants were looking during temporal phases of the serve was necessary. Both AB and WC tennis participants fixated the most number of times and for the longest duration of time on the non-dominant arm during the ritual and preparatory


phase o f the tennis serve (see Tables 15-18), indicating that the non-dominant arm is an important area to focus on during the beginning of the serve. This makes logical sense as the non-dominant arm forms an arc when tossing the ball, hence to focus on this area provides the participant with real time and predictive information about where the ball will be tossed. During the execution phase AB participants had the most fixations for the longest duration on the arm, racquet and shoulder region of the dominant arm (see Table 15). WC participant’s preparatory and execution phases occurred simultaneously and although participants fixated on the non-dominant arm most frequently and for the longest period of time, the second most frequent fixations occurred on the arm, racquet and shoulder region (see Table 16). These fixations were almost as frequent as those on the non-dominant arm. Fixation durations on the arm, racquet and shoulder region occurred for the second longest time, behind the non-dominant arm for this phase of the serve. Hence, these results indicate that it is important for both groups to fixate for an extended period of time on the non-dominant arm and arm, racquet and shoulder area during the preparatory and execution phases of the serve. During the finishing phases of the serve, both AB and WC groups fixated on the ball the greatest number of times and for the longest period of time. However, WC tennis participant’s (n = 32) pursuit tracked the ball for approximately 70% (289 ms out of 410 ms) of time during the finishing phase, whereas AB participants (n = 31) did so for only 18% (51 ms out of 280 ms) of the time (see Table 19). The finishing phase was considerably longer for WC participants averaging 410 ms (SD = 0.13) compared to 280 ms (SD = 0.11) for AB participants. It is likely that the ball traveled much faster from the AB model compared to the WC model indicated by the length of the finishing phases.


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Results indicate that AB participants used different combinations of visual search behaviors during the finishing phase of the serve, they fixated on the ball, as well as the arm, racquet and shoulder region and pursuit tracking the ball. Perhaps these behaviors occurred in AB participants because of the speed of the ball. Different behaviors were engaged in by WC participants as the ball was travelling at a slower speed. Further research would help answer such questions. Wheelchair (WC) Real Time, WC Predictive Group and Able-Bodied (AB) Observations The WC Predictive (n = 17) and WC Real Time (n = 15) groups of WC participants were different especially during the beginning of the serve. However, when examining the WC Real Time group compared to AB participants (n = 31) interesting similarities were evident. Participants in the WC Real Time group had the highest number of fixations for the longest durations in the same locations as AB participants. However, WC participants in the WC Predictive group differed in their durations and number of fixations for various locations and temporal phases of the serve compared to the AB and WC Real Time groups. For instance, during the ritual, preparatory/execution phases of the serve the WC Predictive group had the highest number of fixations and longest fixation durations in the predictive lookzone. In contrast, WC participants in the WC Real Time group and AB participants had the highest number of fixations and longest fixation durations on the non-dominant arm or arm, racquet and shoulder region. During the execution phase for AB participants the highest number of fixations and longest fixation durations occurred on the arm, racquet and shoulder region, which is the same location that was attended to for participants in the WC Real Time group. Participants in the WC Real Time and WC Predictive group were similar to each other


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during the finishing phase of the serve, as contrasted with the AB participants. Hence, results indicated that, except for the finishing phase, participants in the WC Real Time group were similar to the AB participants in terms of locations of interest during temporal phases of the serve. The follow-up questionnaire helped to shed some light on these issues. The majority of participants in the WC Real Time group played AB tennis prior to playing WC tennis (64%, 6 out of 9 participants; see Table 22), for an average of 4.8 years (SD = 1.79). Most participants in the WC Predictive group did not play AB tennis prior to playing WC tennis (71%, 8 out of 11 participants; see Table 22). Those participants in the WC Predictive group who did play AB tennis, did so for an average of 2.3 years (SD = 2.08), less than half the period of time that the WC Real Time group participants played AB tennis. These results not only help to explain possible differences between WC Real Time and WC Predictive group participants but also similarities between WC Real Time and AB participants. Participants in the WC Real Time group learned the game of tennis from an AB perspective, it stands to logical reasoning that when they began playing WC tennis they used the same visual selective attention strategies including: cues, fixations and focus points as when they were standing. Reinforcements from AB coaches the vast majority of whom teach and play tennis while standing would reinforce these fixation points. Additionally, coaches who are not WC tennis certified are most likely teaching WC tennis players the game of tennis how they know it to be played - from and AB perspective. Results from the follow-up questionnaire revealed that WC Real Time group participants as compared with WC Predictive group participants had fewer coaches who


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taught or practiced from a WC and were certified (see Table 22). This lends credence to the argument that participants in the WC Real Time group were similar to AB participants (and dissimilar to WC Predictive group participants) simply because they were engaged in AB tennis environments. Question 5 of the follow-up questionnaire provided some interesting preliminary findings that further validate these results (see Table 22). Participants were asked if they thought there were differences between returning a serve as an AB verses a WC player? All participants (11 out of 11) from the WC Predictive group responded “yes.” However, participants in the WC Real Time group were divided, with 55% (5 out of 9) stating “yes” and 45 % (4 out of 9) stating “no.” If participants in the WC Real Time group do not feel there are differences between AB and WC tennis, then they are most likely not to change their visual selective attention strategies when they play WC tennis. They also may not be likely to change strategies even after playing WC tennis for a number of years as evidenced by their continued belief in a lack of differences between AB and WC tennis when returning serve. In turn, these WC participants are more likely to use visual selective attention strategies that represent an AB person compared to a WC tennis player who has experienced less AB tennis environments. It is important to note however, that these were initial findings based on a limited number of responding participants (n = 20) to the follow-up questionnaire. Further research in this area is needed before definitive explanations can be given with confidence. Technology - The Eye-gaze Response Interface Computer Aid This study was the first to use the ERICA system for analyzing a sport skill in a dynamic environment. Overall the experience was favorable. The use of ERICA allowed


the study to be undertaken (after the first testing session) with quick calibration procedures for all participants. The system was noninvasive, and participants were not distracted by equipment on or around the eye, as is required in other systems such as the ASL 4000SU headband system used by Singer et al. (1998) that may have caused discomfort and distractions. The consistent functioning of the ERICA system during each tournament was a large contributing factor to the number of participants who volunteered their time. Possibly even more important were the positive quick experiences, from the initial calibration to the end of testing (approximately 10 minutes), for the first participants which then allowed them to encourage others to take part in the study. Data collection using the ERICA system was easy with no frustration experienced by the researcher or participants when the 32 mW LED was used, and when the environment was desirable and reasonably self-contained (for details see testing environment later in this chapter). In addition, a small sample of participants (n = 3) were tested twice and a Interclass r correlation test was performed to check for the reliability of the ERICA system. Results revealed a high correlation (r = 0.92) giving further confidence to the results. There were however some limitations of the ERICA system. One of the obvious limitations o f an eye tracking system such as ERICA is its requirement that a participant not move, compared to a real life setting in which movement is imperative to success. As discussed in the methods section (Chapter 3), a highly accurate and precise machine (ERICA) was chosen based on the limitations of technology available in today’s market. Travel Recommendations and Suggestions When Using the ERICA System


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For future researchers who choose to use the ERICA system, there are several experiences that deserve mention and may help prevent problems. In this study, the researcher traveled to tennis tournament sites and permission had been granted by the tournament director ahead of time, however most of the time the researcher had no idea of the environment where testing would take place. The ERICA system used for this project was a desktop system, that needed to be set up at the beginning of every day and dismantled after testing had been completed. When travelling and testing in different environments it is recommended that future researchers use a lap top computer with a large supplemental monitor. Due to the uncertainty of the testing environment, the researcher traveled with several adaptations. The first, which was used at all but one testing site was an adjustable table approximately 45.72 cm x 91.44 cm that could move up or down. This was important as participants, especially those in WC, sit at different heights. When using the ERICA system, a person should be seated with their eyes leveled around the middle to the top third of the screen. The table was the most used and probably most important supplemental equipment when traveling. Other items taken to testing sites included blankets in case of ambient light sources. Black plastic sheets, a large and small umbrella and duct tape were also available for the purpose of limiting ambient light. None of these were needed, however, they are recommended even for piece of mind. A large cardboard box was also taken to store wires, extension cords and computer equipment such as the mouse, keyboard and speakers. Masking tape was used to secure wires from these devices so they did not become entangled.


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Finally, one of the most important things taken with the researcher when traveling and testing was the telephone number of the Chief Technical Officer of ERICA and the telephone number of the Advisor for the study. It was found when traveling to sites where there is no prior knowledge of the testing environment there is never too much preparation, equipment, adaptations and forethought that can be put into a successful outcome. Other considerations when testing participants at a tournament site in sport using ERICA include collecting information (either via a formal questionnaire or researchers notes) on the following: •

Standardizing when participants were tested, for instance the time of day, before or after a match or practice

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Measuring the distance from the screen, or have a marker where each participant must sit when testing

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Take dark eye equipment adaptations of the ERICA system in case this phenomenon is experienced. Dark eye is used to track eye movements in some people when the bright eye effect is not present. The cause of a lack of bright eye effect is unknown.

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Weather conditions

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Drug usage, especially of people with disabilities due to nerve and other pain

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Attention Deficit Hyperactivity Disorder

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Use of a chin rest, as it is noninvasive and will prevent participants from becoming relaxed and sinking lower in their chair or shifting and moving out of calibration

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Setting the ERICA system up at the tournament site - not the tournament hotel or elsewhere. This is important as players can then be tested at a convenient place


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during the day. Most players are tired and although well intentioned will tend to forget coming by a hotel room for testing in an experiment after a long day of competition. •

When testing at a tournament site, it is suggested that the researcher utilize some temporary room-dividing device, for instance a white wall. This will reduce distractions during testing.

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It is also strongly suggested that when testing participants at a tournament site to have two researchers present. This was very helpful, as one researcher could prepare paperwork and recruit participants, while the other researcher tested participants and kept the ERICA system under a careful watch.

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It is important to be flexible with time during the tournament. Give players the maximum opportunity to be tested, get to the site early, and have equipment set up before the first matches. Spend time at the tournament. Be helpful to the tournament directors and officials, assisting them with the many chores they have to run a tournament ensures a cooperative and facilitative relationship. And don’t forget to send a thank you note when the tournament is completed. Difficulties using the ERICA System - After Data Acquisition There were three challenges that emerged after initial data collection at the

tournament sites using the ERICA system. One involved participants who had likely m oved during data acquisition, another w as the determination o f lookzones and a third

area was related to the data analysis itself. Movement Violators. As mentioned in the Methods section (Chapter 3) it seemed likely, after observation of superimposed gaze trails, that some participants had moved


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while being tested. In past research using the ERICA system Richard Brigham (personnel communication December 5, 2004) shifted the gaze trails to where it was felt the participant was focusing. For instance, Olmeda (2002) in her dissertation about reading strategies, supervised by Brigham, shifted gaze trails that were above words to where they were superimposed on top of words. This can be done fairly easily on the ERICA system by using a “shift data” command. To examine if shifting data can be done accurately the current study retested 3 participants, who had likely moved, for a second time. Results from the first testing session were shifted to what the researcher thought was accurate (for more details see movement violators in Chapter 3) these results were then examined against the second testing session. Using a two-way repeated measures ANOVA, results revealed that there were no significant differences between the first testing session (after gaze trails had been shifted) compared to the second testing session ( F (l,2 )= 1.00, p > 0.136, partial eta squared equals 0.95) or for the different serves (Main effect for Time: F (\l, 34) = 35.50, p > 0.570, partial eta squared equals 0.33). For these three participants therefore, a shifting of their gaze trails seemed to accurately represent their actual visual behavior. It is important to be aware that this is a very small sample size and these are preliminary results that need to be viewed with caution. They are encouraging results however, and lend some credence to this strategy for dealing with “movement violators.” More research needs to be done in this area but in light of these initial results, it seems appropriate to “shift data” when necessary. If participants seem to have moved, shifting the data seems to produce an accurate representation of a gaze trail and therefore participants may not necessarily need to be excluded.


Lookzones. As mentioned in the Methods section (Chapter 3) lookzones were drawn on the screen much like football commentators draw on the screen during a game to specify where a player moved or where a hole exists in the defense. Using ERICA lookzones were drawn around areas of interest, determined initially by past research. The lookzones extended 6 mm beyond the object or area of interest, due to the Vi degree of accuracy and precision of the ERICA system (Lankford, 1999). However, lookzones were not easy to draw on the model, as the model was moving. An interpolation device is used to adapt the lookzone from one position to another, however the shape of moving body parts also changed during the serve. For an expert server, the shape of the arm, racquet and shoulder region of the body changes considerably within hundreds of a millisecond. Hence, the lookzones needed to change shape and move as the model served the ball. This was a long and tedious process, taking approximately 2 hours per video clip. A total of 142 lookzones were created for the WC model over the 18-video clips, and 144 were created for the AB model over the 18-video clips. A limitation of this study related to the creation of lookzones was that only one researcher created the lookzones even though a very specific formula was used (i.e. based on a 0.5 cm radius around the area of interest). In future studies it is recommended that a second person validate these creations. Other issues that presented themselves during the creation of lookzones occurred due to the film ing angle. Looking at the server from the return o f serve position, does not

always allow the viewer to see all components of the body (and lookzones) at all times. For instance, when the server takes his dominant arm into the back-scratch position (where the elbow is bent and the racket is tucked behind the back or head for WC


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players) the only part o f this lookzone (the arm, racquet and shoulder) that can be seen is some of the racquet. Although this is what happens in “real life” when viewing a server from a return of serve position, it becomes difficult when creating lookzones. This difficulty is compounded by creating lookzones over a 2-dimensional screen where at certain temporal elements body parts overlap. Another problem that stems from the creation of more than one lookzone for each area of interest occurs when viewing the output of the data, scores are partial and therefore more calculations are needed. There is no automatic way to do this when the sample size is large and hence calculation becomes a time consuming process. Another challenge with using lookzones was experienced when movements occurred extremely fast, such as the racquet just prior to and after contact, and the ball during the finishing phase of the serve. The film was taken and stretched to fit the 17inch (43.18 cm) screen, a procedure that ERICA automatically does upon request. However, due to the stretching of the video, trails were created when the image was very fast. For example when the ball was hit, instead of the object appearing to be round it changed shape. Therefore the lookzone also had to change shape. Although the 6 mm circumference around the ball was still applied, the ball itself was larger due to the trailing after effect. This was an adjustment that was needed on every video clip, AB and WC, and became a time consuming process. Data Analysis. ERICA has a data analysis system called “Multiple Subject Wizard” that calculates basic data for several participants or an individual. Data can be sent to Excel spread sheets or graphed in three dimensions. The Multiple Subject Wizard can calculate fixation data for lookzones or entire video clips. This is a good tool as an


initial check that individual participants were testing as expected, for instance that they are in fact looking within the lookzones. However, beyond this, for analyzing multiple subjects the data analysis process becomes much more difficult. For instance to test the lookzones in which participants are fixating at different temporal phases of the video clip (service motion), an individual output needs to be done. There was no way to calculate group scores over different temporal elements within lookzones except by the long process of doing so one individual at a time and calculating these points by hand or individually adding them into an Excel or SPSS program for instance. Fixation durations also needed to be calculated in the same fashion. This was a long process, especially for a large sample size. Overall the experience of using ERICA for testing participants in sport was a positive one. All eye movement tracking devices produce volumes of information. ERICA software is user friendly, and assists the researcher in basic data analysis. However, specific temporal and location information across many subjects needed to be calculated more specifically by efforts from the researcher. Limitations o f this Research There are some limitations of this research related to the test film and the testing environment. It is hoped that by explicating these in detail it will help future researchers, especially when at tennis tournaments. The Videotape Test Film One limitation of this study was the use of videotape compared to a real life setting. As mentioned previously, this limitation was weighed against the technology available in the market place today and a decision was made to use a less ecologically


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valid but very accurate and precise machine. Even so, there were several limitation of these videos. One limitation involves the location of filming. The two videotapes were filmed at different locations, as the researcher was limited to where the models were practicing and competing. The researcher also had little control over the time of day when filming took place, as this would depend on the models tournament schedule and match length. Due to these circumstances, the AB model was filmed during day light hours, while the WC model was filmed in the early evening, after the sun had set, using the artificial light that surrounded the tennis court. The background for both participants was therefore different. For the AB model, a tree was behind the court and on the day of filming, the breeze occasionally blew the tree creating some movement. According to Treisman (1988) an moving object will direct attention toward it and this was found to be the case for some of the participants that were excluded. Also, on the tennis court used with the AB model, there were several puddles of water, that may also have drawn the attention of some participants, so much so that some commented on the water during testing. This was noted in observational notes and viewed when the gaze trails were superimposed over the screen at post-testing (see Figure 5). Additionally, the AB model was practicing on an end court of a row of tennis courts. When filming the researcher used a “shot gun” microphone to eliminate as much background sound as possible. This made a difference to the quality and relevance of the sound being heard, however, noise from adjacent courts could still be heard while the model was serving.


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For the WC tennis model, the background had fewer distractions because the filming was conducted after sundown (under artificial light) and the only real distraction was an occasional car pulling up around the tennis courts and headlights in the distance. There was no water or other distractions on the court, and there were no people playing on adjacent courts around the WC tennis model during filming. The realism of each video is also important. As specified in the Method section (see Test Film for a detailed explanation) the videotape cameras were placed in positions and at heights that mimicked those of an average return of server in AB and WC tennis. For instance, when filming the WC tennis model, the videotape was 120.65 cm high (the height of the model performing the serve) whereas the AB camera was set at a height of 177.45 cm, the average height of males (Halls, 2003). Also, when filming the WC tennis model the camera was 91.44 cm behind the baseline, as this is the starting position for expert WC tennis players when returning the serve. In comparison the AB model was filmed with the camera on the baseline, consistent with the starting return of serve position for most AB experts. These are important differences that occur in real competition, but provide a completely different perspective of the tennis court for each group o f participants. WC participants expressed the differences between a WC and AB perspective when returning serve, justifying the differences accounted for in the test films. Question 5 o f the follow-up questionnaire asked participants if there were any differences between returning the serve as an AB person verse being a WC player (see Table 22)? Several participants responded to this question by stating “yes” and making comments such as the difference in the height of WC verses AB servers. Also, some participants commented on


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the momentum needed in WC tennis to assist in fast movements hence WC players when returning serve start further back than AB players to allow for some extra “pushes” on their chair prior to making contact with the ball. Participants were asked to rate the realism of the video in the concluding questionnaire. Some participants also made additional voluntary comments regarding the videos. Question 3 of the concluding questionnaire asked participants if “the image they saw was a realistic comparison to what you see when on the tennis court?” Participants in both groups overwhelmingly (90%, 28 out of 31 AB’s, 30 out of 32 WC’s) stated that the video was “very realistic” or “somewhat realistic” (see Table 21). The minority who rated the test film as “not very realistic” or “not at all realistic” expressed two general concerns. One was that the film was two dimensional, unlike the game of tennis. The second comment was that they felt the video stopped too soon and that they could not see the ball bounce. Question 4 of the concluding questionnaire also asked participants to rate the test film. Participants were asked “How realistic do you feel it is to assume you are competing in a real match against this player and to be thinking about trying to return the serve as effectively as possible...?” The majority of participants in both AB (52%, 16 out of 31) and WC (75%, 24 out of 32) groups felt that this was “somewhat realistic” (see Table 21). Only 7% (2 out of 31) of AB participants and 3% (1 out of 32) of WC participants felt the test film was “not very realistic.” Three percent of AB participants (1 out of 31) felt the test film was “not at all realistic.” Additional comments (see Appendix E) revealed two themes that shed some light upon the minority of participants who did not see the film as realistic. Some female participants, as they were watching male


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models on the test film, felt it was unrealistic for them because professionally they would not play a male. The other theme was that they could not move in response to the oncoming ball and therefore the test film was less realistic. These are important pieces of feedback and should be considered in future research studies. The Testing Environment All data were collected during professional tennis tournaments in which the participants were competing. This was the most economical and easy way to collect visual selective attention data from a large number of professional tennis players. All data was collected during the main tennis season in the United States, between the months of April and September 2003. For all participants the same dominant arm and same side eye was used for calibration purposes, future research should test for eye dominance and in turn use the dominant eye when calibrating. For the AB male participants (n = 31) the ERICA system was set up in a trailer near the tennis courts. The trailer housed the athletic training room at one end and at the other end was a computer for tournament administrators that was originally dedicated to checking weather reports for instance. However, during the time of testing participants, problems with the tournament computer did not allow online connections and so for the most part this computer area was empty, and the ERICA system being set up next to the tournament computer was not a problem. Although the area was small it was the only inside area (that was protected from ambient light sources) that provided electrical outlets where the ERICA system could be set up. There were some environmental distractions at this site. People (players and administrators) would come in and out of the trailer looking for the physical therapist, this sometimes occurred during testing. Also, people would


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talk amongst themselves and to the physical therapist, which provided some noise distractions during testing and on occasion was problematic when reading the statement of instructions to participants. It is believed these distractions (although unavoidable) may have contributed to participants answering ‘no’ to question 1 on the concluding questionnaire (i.e. Were you thinking about competing in a real match with this person?) and in turn being excluded from the study. A total of 3 WC and 10 AB participants were excluded due to inattention as determined from the post-assessment questionnaire. The AB data from females was collected in the player’s locker room, inside a brick building at a professional tennis tournament. This was again the best area within the tournament site that provided protection from ambient light and had electrical outlets for the computer. The ERICA system was set up in the far west comer of the room, a partition was in place that separated the front of the room, where a table of food was set up for the players, and from a lounge area, where players could rest, read, and talk. Although this was an ideal place for the ERICA system, the players’ locker room was not without distraction from noise, with players, coaches and family members talking. Some family members were also present when testing was taking place, this provided a different distraction, as some of them talked to the participant when testing. This should not have occurred, and was recorded in the observational notes taken by the researcher. These noted distractions along with results from the concluding questionnaire and obvious distractions when reviewing the video post-testing resulted in most of these participant’s exclusions. Across all tournaments 11 WC participants and 13 AB participants were excluded.


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At both of the AB tournaments the researcher had no assistant. At the WC tennis tournaments an assistant traveled with the researcher to assist with completing pre and post testing forms (IRB, informed consents, demographic and concluding questionnaires). This was very helpful and enabled the researcher to test participants in a more timely fashion. The first tournament that the researcher attended for data collection purposes was for WC tennis players, and had several technical (ERICA related) problems. The testing took place in the suite of the researcher’s hotel room. The room was darkened by closing the curtains and door, virtually no ambient light was present. The assistant had participants fill out pre test forms in the hallway of the hotel so there were no noise distractions during testing. The environment was excellent for testing. However, a lowlevel infrared light (12 mW LED) was used in the ERICA camera. This led to constant problems calibrating participants, in fact many participants could not be calibrated at all, and had to leave after not viewing the test video. It took some participants 40 minutes or more to be calibrated, which lead to frustration for the researcher and participants. Several lessons were learned from this experience that were improved upon for all subsequent tournaments. First, the Chief Technical Officer of the ERICA system replaced the camera with a stronger infrared light (32 mW LED). This solved the calibration problem in all subsequent testing sessions. Second, an adjustable table was taken to each tournament, so that the monitor could be placed at a high that allowed the participants eyes to be in the middle to the top third of the screen, an optimal position for successful calibration. Third, the researcher noticed that during the 16-point calibration, participants would begin to anticipate where the next point of focus would be (as the


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calibration points appeared from left to right beginning at the top left of the screen and ending at the bottom right). Hence, the researcher told participants before beginning calibration that this part of the process was “not a test” and that the participant needed to “keep their eyes focused on the red box until another red box appeared, they were not to predict when or where the next box was presented.” This statement was helpful and the researcher talked the participant through the first few calibration points making comments like “look at the box, stay with it, don’t move your eye yet, okay now move your eye to the next box.” The researcher did this until the participant successfully mastered the task, which usually only took 2-4 calibration points. There are two other points worth mentioning that occurred during this first testing session and although they did not present themselves during testing at subsequent tournaments, they are important for future researchers - the first is ‘dark eye.’ There are some people that do not have a “bright eye” effect, this is not related to gender or ethnic background and the cause is unknown. In such cases another method o f eye tracking can be used that aligns the light source to create a dark pupil instead of a bright one. According to Lankford, “the dark eye effect has not been seen in any participants when the 32 mW LED has been used” (personnel communication, December 7, 2003). A second point of interest for future researchers involves the fact that some WC tennis players are taking prescription use drugs as part of a pain management program related to their injuries, for problematic conditions such as nerve pain, or to control neurological tremors. These types of drugs are usually narcotics or amphetamines and reduce the size of a user’s pupil. This can impact visual search capacities and cognitions, such as a person’s ability to fixate, which may in turn impede calibration. Problems with


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calibration did not occur after the first testing session when the higher powered infrared light was used, and this is possibly the best solution for future researchers who my experience similar problems. Due to problems calibrating and hence collecting data at the first tournament where WC tennis participants were tested, a second tournament was attended, using the lessons and new equipment adapted from the first experience. At the second tournament the ERICA system was set up in a brick building where the tournament registration table and administration table was also present. In the large room, the ERICA system was set up in the north comer, no windows were at this end of the room, a door was at the opposite side of the room. The researcher ensured the glass door and the blinds covering the door were closed before each testing session began. This was the best location at the tournament for the ERICA system as it provided the electrical outlets needed and was inside reducing ambient light sources. The room did not provide an ideal environment for testing, due to noise from players and administrators, however, after the first day of the tournament (when players are required to register) the room had fewer distractions. Hence after testing two players, a decision was made not to collect any more data on the first day and instead to make people aware of the experiment and encourage their participation on subsequent, less busy administrative days of the tournament. This required spending a longer period of time at the tournament but certainly created a better testing environment. Overall, the testing environment at professional tennis tournaments was adequate for using the ERICA system. Unavoidable environmental distractions from players and administrators were present. The researcher was in a “catch-22” position; on one hand it


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was important to be able to test a valid sample size of professional level tennis players and the most cost and time efficient place to do this was at professional tournaments. However, it was out of the experimenters control as to where (or even if) an ideal testing environment within the tournament site would be present. The researcher was grateful for the adaptations and cooperation of all the tournament administrators and for the assistance they provided, all of which was voluntary. Implications fo r Future Research Visual selective attention in sport is a relatively new field o f study and research conducted in sport, specifically in tennis, is limited. Prior to this study there had been no research conducted on WC tennis players. Also, technological changes and advancements in data acquisition revealed limitations of past studies and the need for more research with considerably more efficient, accurate and precise instrumentation. Due to these many limitations there are many areas that need to be considered in future studies. Some have been previously discussed in this chapter. The following section will outline the future areas of research for further consideration. The immediate next step in visual selective attention, especially of WC tennis players is to determine which of the strategies found in this study are indeed most effective in determining direction and spin when returning a tennis serve. This is especially important before any interventions or education sessions for such athletes is undertaken. General limitations in past research that need to be considered for future research are:


• The need to report averages and standard deviations of all visual search behaviors measured. This is important as reporting means and standard deviations can be used in future research using a meta analysis. Such an analysis would make it possible to compare results over several studies, rather than individually interpreting the data. • O f the research published to date, there is no systematic reporting of visual selective attention concepts. For instance, some research has reported total number of fixations by all participants over every slide, others report percentages of time, still others report means of specific locations but not total means over slides. To make sense of the results, it is necessary to have consistency in reporting the data. This is very important not only because the field is new and research is limited, but also because the terminology can be very confusing to someone not exposed to it. Reporting results in a clear and systematic fashion is imperative. This is even more important when considering that the ultimate purpose of examining visual selective attention is to provide intervention strategies that coaches and professionals in the field of sport can use to enhance learning and performance. •

With the same line of thinking as the previous point it is important to report the broadest as well as the most specific results. For instance, the average number for fixations and fixations durations within video clips and the total number of fixations and durations in any pertinent area of interest or lookzone (Statements 1, 2, 3, 4, 5 of this study). These are important measures as they provide initial validation that the results are in the generally expected direction. Past research has not reported these important results in such a direct fashion.


•

Based on feedback from participants in this study it may be important to take into consideration possible gender differences in visual selective attention strategies and while viewing models of different genders. This is very important for professional level tennis players, the majority of whom play singles or same gender doubles (i.e. men’s or women’s doubles), especially due to WTA and ATP scheduling of tournaments in different locations. Few studies have examined differences between genders (Singer et al., 1996), and more research is needed in this area.

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The participants of the current study were world ranked experts. It was assumed therefore that they were also proficient at returning the tennis serve. However, it would be wise for future researchers to specifically ask this question of participants, that is “Is returning serve a strength of your tennis game?” Such a question would be helpful in that it could help create an initial understanding of within group differences should participants respond with a variety of answers. Researchers could also test for differences between participants who feel that returning serve is a strength of their tennis game compared to other who feel returning serve is a limitation.

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Different areas of the game of tennis also need to be examined, such as visual selective attention strategies for groundstrokes, volleys, during defensive situations, and offensive scenarios as well as others.

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An understanding of whether selective attention strategies change at difficult points in a match, (e.g. under more stressful situations) is also important. I f participant’s

believe the scores in 6 - 6 in a tie breaker of an important tournament, do their visual selective attention strategies differ compared to if the score was 1 - 1 in the first set of a small tournament? Does competing against different participants, e.g. the


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number one player in the world verses the number 200 player in the world produce different visual selective attention strategies? • One of the most important outcomes of this and other visual selective attention research is the potential for designing intervention strategies based on the knowledge gained. Ultimately the goal is to be able to teach players of all levels of skill, AB and disabled, effective visual selective attention strategies to assist in performance. Intervention strategies need to be tested for their effectiveness, for example occluding strokes at various points and asking participants to detect direction and spin, or adding verbal and visual cues to strokes. Goulet et al. (1989) and Singer et al. (1994) took the first step in this direction and more research is needed. • Coaches and teachers need to be made aware of visual selective attention strategies and their effectiveness in assisting performance. An educational program to teach coaches these strategies and interventions is needed. • A difference between people of various skill levels is evident from past research. Goulet et al. (1989) and Singer et al. (1994) examined a visual selective attention intervention strategy to assist people learning tennis so that their visual selective attention becomes more effective, like expert players. More studies using interventions are also needed in future research. • The primary purpose and uniqueness of this study was to examine expert WC tennis participant’s visual selective attention strategies. The USTA serves people of all skill levels as well as AB and WC players. The majority of people who play tennis are not elite athletes like those in this study. A push for inclusion by the USTA has led to AB and WC players competing against one another. It is imperative from a


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community based perspective to educate all those who enjoy the game of tennis on effective visual selective attention strategies. At some skill levels this will include AB persons learning how to search for clues from WC players and vice versa. One area in need of fixture research is more testing of professional level tennis players. Testing of AB players on the ATP and WTA tour will help create a greater understanding of the following questions: • Do higher level AB experts focus on the non-dominant arm during the ritual phase as well as the general body position? • Is there a difference in visual selective attention behavior, of expert participants, such as the use of saccades verses pursuit tracking in expert tennis players? This was an initial finding of Singer et al. (1998) and needs to be further explored. • Is there a difference in the visual selective attention gaze trail order of expert tennis players compared to players with less experience? • Do WC tennis players compared to AB tennis players use a different search rate (i.e. does one group visually search the environment at a faster rate than the other)? • Is there a difference in scan paths used by expert tennis players? A scanpath determines dynamic priority between parts of the body. Goulet et al. (1989) used scanpaths to measure visual selective attention strategies of experts, however no research has examined expert differences or followed up on Goulet et al.’s initial study.

• Do experts tend to look ahead and anticipate more if the serve is fast? It is important to examine if differences in visual selective attention strategies occur if the serve is produced at different paces or with different spins.


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•

Do differences in the types of serve lead to different visual selective attention strategies used by experts? Goulet et al. (1989) and Singer et al. (1996) examined differences between experts and novices on the type of serve delivered. It is important to know if experts differ and to follow up on their studies with highly ranked experts.

• During the finishing phase of the serve, expert AB players used a variety of visual selective attention locations and behaviors (fixations and pursuit tracking). These behaviors have differed between studies. More research is needed to understand these differences. More testing of WC tennis players in visual selective attention strategies in every area is needed. Several future areas of research are specified below: • Can future studies replicate the split in visual selective attention strategies as was found in this study related to the WC Real Time and WC Predictive groups? • Are WC participants who demonstrate WC Real Time verses WC Predictive visual selective attention strategies more successful in determining the direction and/or spin on a ball sooner, more accurately or with more confidence than the other? • Was the visual selective attention strategy undertaken by the WC Predictive group participants due to their fewer years of playing experience in WC tennis compared to the WC Real Time group? And, on the other side of the coin, were differences between these two groups due to the WC Real Time group having played AB tennis for twice as long as WC Predictive group participants?


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•

More information is needed on WC tennis players past history playing tennis and ability of returning serve and if these factors influenced their current visual selective attention strategy.

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More information is needed on WC tennis players coaches and if and how they influence a players visual selective attention.

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An examination of the biomechanics of the WC phases needs to be examined. This study revealed that there are differences between WC and AB service motions tennis serves. It is important to examine these differences over a wide variety of WC players to be able to generalize results. Also, a more recent examination of truly elite AB tennis player’s biomechanics is needed. Although this does not directly relate to visual selective attention strategies, it is important for effective teaching of wheelchair tennis. Should there be consistent differences in the wheelchair tennis serve then different visual search strategies may result.

•

Related to the previous point, gaze trails can be examined in a variety of different ways. In this study, gaze trails were examined based on past AB research literature regarding temporal phases of advanced AB players from work by Groppel (1980). Now that limited research exists regarding the biomechanical differences between the AB and WC tennis serves it is important to examine temporal elements and gaze trails more specifically as they relate to either the AB or WC tennis serve.

Conclusion Visual selective attention strategies are important cognitive elements of an athlete’s performance. Expert athletes, in particular tennis players, have systematic visual selective attention strategies from one trial to another as well as between trials.


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Prior to this dissertation, no research had been conducted on expert WC tennis players. It is important not to simply apply what is known about AB visual selective attention strategies to WC tennis players as it may be different. Hence, the primary purpose of this study was to examine visual selective attention strategies of expert WC tennis players. Results revealed some similarities and differences, both within the WC group of participants and as observed compared to AB participants. More research needs to be conducted with WC tennis players in order to understand the most effective visual search strategies for this group of tennis experts. Additionally, as a research extension, limited examination of tennis athletes who are truly elite experts had been conducted prior to this study. It was important to test current professional tennis players in order to examine the most efficient visual selective attention strategies in the ever changing game of tennis. All participants in this study, AB and WC players, had high world rankings. Results revealed some differences between highly ranked experts in this study and “experts” tested in other studies. This was especially evident in the finishing phase of the serve. More research is needed to understand these differences. An extension of research was also undertaken through the use of the ERICA system to measure visual selective attention for a sport skill. Testing using this piece of equipment was favorable. The ERICA system was highly reliable (r = 0.92). Software for data analysis procedures was user friendly, however, com piling results in som e areas

of visual selective attention was challenging. It is hoped that this dissertation will encourage future studies in tennis and other sports specifically for people with disabilities, as there is no research available for this


155

population of skilled athletes. The next step is to test these results in order to determine which visual selective attention strategies are most effective in determining direction and spin when returning the serve. Further exploration of visual selective attention strategies should ultimately lead to effective teaching and coaching strategies (interventions) for participants at all skill levels, with and without disabilities, to assist in learning and performance improvement in a game that is enjoyed for millions of people of all ages and in many countries around the world.


Visual Search Strategies in Tennis - 156

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TABLES


164

Table 1

Terminology Terminology

Definition

Fixation

Any stopping point o f the eye within a 40 pixel diameter on the screen that lasted longer than 100 ms.

Fixation Duration

How long the eye stops during a fixation, averaged across serves.

Pursuit Tracking

Movement o f the eye following an object within a dynamic display

Temporal Phases

Biomechanical phases o f the tennis serves according to Groppel (1984). These phases have certain movement criteria and are timed and averaged across serves.

Ritual Phase

The first temporal component o f the tennis serve. Precedes the initiation o f the serve, and consists o f ball bounces and foot (or chair) positioning.

Preparatory Phase

The second temporal phase o f the tennis serve. Begins at the elevation o f the arm holding the ball and ends at the apex o f the ball trajectory.

Execution Phase

The third temporal phase o f the tennis serve. Begins at the server’s knee extension and finishes at the ball/racquet contact.

Finishing Phase

The final fourth phase o f the tennis serve. Begins after the ball/racquet impact and finishes as the ball crosses the net.

Lookzones

A lookzone is used to record the amount o f time the users gaze spends in a particular region o f an image, an area o f the display seen as important. Lookzones move as the image moves across a dynamic film.

Lookzone 1

Located around the ‘general body position’ (GBP) that included the torso, legs and head. For the wheelchair tennis model this lookzone also included the wheelchair.

Lookzone 2

Located around the servers’ non-dominant tossing arm (NDA).

Lookzone 3

Located around the arm, racquet and shoulder (ARS) region o f the servers dominant arm.

Lookzone 4

Located around the ball (B).

Lookzone 5

Located to the left-hand side o f the model in an area where the non dominant arm is expected to toss the ball referred to as the predictive lookzone (P).

Note: The preparatory and execution phases for wheelchair participants occurred simultaneously and therefore produced a combined score.


165

Table 2 Tables as they Relate to Corresponding Descriptive Variable Statements

Table

Descriptive Variable Statements

9

Average number of fixations and fixation durations in total and within lookzones for able-bodied and wheelchair groups (statement 1,2, 3, 4)

10

Average number of fixations and fixation durations in total and within lookzones for WC Predictive and WC Real Time groups (statement 1, 2, 3,4)

11

Average number of fixations and fixation durations for each lookzone for able-bodied and wheelchair groups (statements 5, 6)

12

Average number of fixations and fixation durations for each lookzone for WC Predictive and WC Real Time groups (statements 5, 6)

13

Average number of fixations and fixation durations for temporal phases of able-bodied and wheelchair groups (statements 7, 8)

14

Average number of fixations and fixation durations for temporal phases of WC Predictive and WC Real Time groups (statements 7, 8)

15

Average number of fixations and fixation durations for temporal phases and lookzones of the able-bodied group (statements 9, 10)

16

Average number of fixations and fixation durations for temporal phases and lookzones of the wheelchair group (statements 9,10)

17

Average number of fixations and fixation durations for temporal phases and lookzones o f the WC Predictive group (statements 9, 10)

18

Average number of fixations and fixation durations for temporal phases and lookzones of the WC Real Time group (statements 9,10)

19

Pursuit tracking for able-bodied and wheelchair tennis groups within lookzone B (the Ball)

20

Pursuit tracking for WC Predictive and WC Real Time groups for lookzone B (the Ball)


166

Table 3 Summary o f Eye Movement Recording Devices using Reflection in Tennis

Name of Recording Device

Eye Movement Recorder Model V

Eye-Trac Model 210

4000SU Headband system

5000SU

Company

NAC

ASL

ASL

ASL

Study

Goulet, Bard & Fleury (1989)

Singer et al. (1996)

Singer et al. (1998)

Williams, Ward & Bennett (2002)

+/- 1

+/- 1

Accuracy (in degrees)

1

Precision (in degrees)

Eye Response Technology current

+/- Vi

+/-14

+/- v2

60

Data sampling Rate (frames Per second)

30

50

Fixation Locations

4 grids frame by frame

frame by frame

Screen

Eye Gaze Response Interface Computer Aid

lookzones

9

9

9

return of serve groundstrokes

return of serve

groundstrokes

16

Calibration

(in points) Task

return of serve


return of serve

167

Table3 Summary o f Eye Movement Recording Devices using Reflection in Tennis cont ’d

Name of Recording Device

Eye Movement Recorder Model V

Eye-Trac Model 210

4000SU Headband system

5000SU

Eye Gaze Response Interface Computer Aid

Movement Required?

No

No

Yes

Yes

No

Body Attachments

Y eshead

Yeshead

Yeshead + cord

Y eshead

No

Other

interrater reliability .94, intrarater reliability .96

difficult calibration

3mx3.5m screen for viewing


168

Table 4 Male Wheelchair Tennis Participants, Included in Final Analyses, Wheelchair (WC) Predictive or WC Real Time Group, Ages, International Tennis Federation Rankings, Years o f Tennis Playing Experience and Disability Category Between August 31st, 2002 October 1st, 2003.

Participant Number & Group (Predictive - P Real Time = RT) 2P 3 RT 4P 5 RT 6P 7 RT 8P 9P 10 RT 13 P 14 RT 17 P 18 RT 19 P 21 RT 22 P 25 RT

Age

Ranking

41 39 36 30 23 38 39 37 44 23 43 19 31 40 44 33 21

15 39 23 13 48 34 42 8 12 14 16 16 1 60 1 1 9

Years Playing

Disability Category

15 33 9 9 13 3 12 17 14 7 15 9 13 8 17 10 8

5 5 3 1 1 1 1 4 4 1 1 1 3 1 1 3 1

Note. Eligibility criteria according to the ITF (2003, p. 17): 1. A neurological deficit at the sacral 1 level or rostral, associated with loss of motor function; 2. Ankylosis and/or severe arthrosis and/or joint replacement of the hip, knee or upper ankle joints; 3. Amputation of any lower extremity joint rostral to the metatarsophalangeal joint; 4. A player with function disabilities in one or both lower extremities equivalent to any o f the previous conditions;

5. Other permanent impairment related but not specified in categories 1-4. Note: Rankings reported as the highest achieved rank between August 3 1st 2002 to October 1st 2003.


169

Table 5 Female Wheelchair Tennis Participants, Included in Final Analyses, Wheelchair (WC) Predictive or WC Real Time Group, Ages, International Tennis Federation Rankings, Years o f Tennis Playing Experience and Disability Category Between August 31st, 2002 October 1st, 2003. Participant Number & Group (Predictive = P Real Time = RT) 26 P 27 P 28 P 29 P 30 RT 33 RT 35 P 36 RT 37 P 38 RT 39 P 40 RT 41 RT 42 RT 43 P

Age

Ranking

33 43 37 54 37 24 19 22 35 18 37 30 34 28 36

21 25 18 26 1 2 32 65 3 18 10 82 15 1 4

Years Playing

Disability Category

7 30 4 9 17 10 6 10 2 7 4 19 23 13 3

5 5 5 1 1 5 4 4 5 1 1 1 1 1 3

Note. Eligibility criteria according to the ITF (2003, p. 17): 1. A neurological deficit at the sacral 1 level or rostral, associated with loss of motor function; 2. Ankylosis and/or severe arthrosis and/or joint replacement of the hip, knee or upper ankle joints; 3. Amputation of any lower extremity joint rostral to the metatarsophalangeal joint; 4. A player with function disabilities in one or both lower extremities equivalent to any of the previous conditions; 5. Other permanent impairment related but not specified in categories 1-4. Note: Rankings reported as the highest achieved rank between August 3 1st 2002 to October 1st 2003.


170

Table 6 Female Able-Bodied Participants, Included in the Final Analyses, Ages, Women’s Tennis Association Rankings and Years o f Tennis Playing Experience Between August 31st, 2002 - October 1st, 2003.

Age

Ranking

44

27

252

24

46

21

380

12

47

24

114

19

48

22

436

16

49

25

340

15

50

26

62

17

54

18

383

10

57

19

440

5

58

21

402

13

59

24

300

20

60

23

255

18

61

22

102

16

62

21

416

10

63

22

299

18

67

18

183

11

68

21

280

15

Participant Number

Years Playing

Note: Rankings reported as the highest achieved rank between August 3 1st 2002 to October 1st 2003.


171

Table 7 Male Able-Bodied Participants, Included in Final Analyses, Ages, Association fo r Tennis Professionals Tennis Association Rankings and Years o f Tennis Playing Experience Between August 31st, 2002 - October 1st, 2003.

Age

Ranking

69

29

143

24

70

28

290

16

71

34

44

25

73

25

146

19

76

25

109

20

77

22

236

15

78

25

345

21

79

22

178

13

80

25

180

19

81

27

127

19

82

23

238

15

83

25

154

17

84

22

269

12

86

30

52

25

88

21

230

15

Participant Number

Years Playing

Note: Rankings reported as the highest achieved rank between August 31st 2002 to October 1st 2003.


172

Table 8 Participants who are Able-Bodied or Wheelchair Players, Excludedfrom Final Analyses, Ages, Rankings and Years o f Tennis Playing Experience Between August 31st, 2002 October I st, 2003.

Participant Number

Participant Group

Age

1 11 12 15 16 20 23 24 31 32 34 51 52 53 55 56 62 64 65 66 72 75 85 87

WC WC WC WC WC WC WC WC WC WC WC AB AB AB AB AB AB AB AB AB AB AB AB AB

35 43 33 40 40 56 33 33 39 52 31 21 22 18 19 22 21 20 18 22 27 19 18 21

Ranking

171 220 190 250 231 90 210 63 97 45 90 505 452 600 950 970 700 460 711 901 440 470 580 1100

Years Playing Disability Category 6 9 2 14 7 14 24 8 12 15 15 12 12 13 12 10 10 12 12 11 13 14 10 12

Note: WC refers to Wheelchair, AB refers to Able-Bodied. Note: Rankings reported as the highest achieved rank between August 3 1st 2002 to

October 1st 2003.


5 5 5 1 1 3 1 1 4 1 4


173

Table 9 Average Number o f Fixations and Fixation Durations in Total and Within Lookzones fo r Able-Bodied and Wheelchair Groups

Able-Bodied Group (Mean Trial Length 6070 ms; SD = 0.79) (w = 31) M SD

Wheelchair Group (Mean Trial Length 2470 ms; SD = 0.39) (n = 32) M SD

Average number of fixations

8.08

3.40

4.66

1.57

Average fixation durations (ms)

430

1.20

460

1.17

Average number of fixations in lookzones

7.57

1.15

4.41

0.28

Average fixation durations in lookzones (ms)

432

1.10

430

0.20

Note: Fixations are the number of stopping points calculated from when they first began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended.


174

Table 10 Average Number o f Fixations and Fixation Durations in Total and Within Lookzones fo r Wheelchair (WC) Predictive and WC Real Time Groups

WC Predictive Group (Mean Trial 1Length 2470 ms; SD = 0.39) (* = 17) M SD

WC Real Time Group (Mean Trial Length 2470 ms; SD = 0.39) (* = 1 5 ) SD M

Average number of fixations

4.63

1.53

4.67

1.58

Average fixation durations (ms)

460

1.18

460

1.17

Average number of fixations in lookzones

4.54

0.27

4.40

0.28

Average fixation durations in lookzones (ms)

430

0.19

430

0.20

Note: The WC Predictive Group was composed of participants who predicted the tossing area ahead of time and shifted their gaze in anticipation of the movement. The WC Real Time Group was participants who followed the motion as it was produced by the model. Fixations are the number of stopping points calculated from when they first began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended.


175

Table 11 Average Number o f Fixations and Fixation Durations fo r each Lookzone fo r Able-Bodied and Wheelchair Tennis Groups Able-Bodied Group (Mean Trial Length 6070 ms; SD = 0.79) (« = 31) M SD

Wheelchair Group (Mean Trial Length 2470 ms; SD = 0.39) (n = 32) M SD

Lookzone GBP Average number of fixations Average fixation durations (ms)

1.24 575

0.76 2.29

0.34 480

1.12 2.13

Lookzone NDA Average number of fixations Average fixation durations (ms)

2.42 480

0.87 1.78

1.21 490

2.02 1.45

Lookzone ARS Average number of fixations Average fixation durations (ms)

2.24 380

0.89 2.78

1.37 440

2.13 1.28

Lookzone B Average number of fixations Average fixation durations (ms)

1.40 200

1.03 0.26

0.46 280

0.71 1.11

Lookzone P Average number of fixations Average fixation durations (ms)

0.44 530

0.42 2.52

1.00 570

16.26 1.60

Note: Fixations are the number of stopping points calculated from when they first began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended. Lookzone abbreviations include: GBP (General Body Position), NDA (Non Dominant Arm), ARS (Arm Racquet Shoulder), B (Ball), P (Predictive).


176

Table 12 Average Number o f Fixations and Fixation Durations fo r each Lookzone fo r Wheelchair (WC) Predictive and WC Real Time Groups WC Predictive Group (Mean Trail Length 2470 ms; SD = 0.39) (« = 17) SD M

WC Real Time Group (Mean Trail Length 2470 ms; SD = 0.39) (n = 15) M SD

Lookzone GBP Average number of fixations Average fixation durations (ms)

0.20 480

1.00 1.08

0.45 485

0.93 0.79

Lookzone NDA Average number of fixations Average fixation durations (ms)

1.40 500

0.97 0.83

1.64 600

0.83 0.64

Lookzone ARS Average number o f fixations Average fixation durations (ms)

0.54 420

0.54 0.45

1.72 480

0.81 0.35

Lookzone B Average number of fixations Average fixation durations (ms)

0.70 222

0.51 0.73

0.43 200

0.63 0.11

Lookzone P Average number of fixations Average fixation durations (ms)

1.71 605

1.79 0.53

N/A N/A

N/A N/A

Note: The WC Predictive group was composed of participants who predicted the tossing area ahead of time. The WC Real Time group included participants who followed the motion as it was produced by the model. Fixations are the number of stopping points calculated from when they began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended. Lookzone abbreviations include: GBP (General Body Position), NDA (Non Dominant Arm), ARS (Arm Racquet Shoulder), B (Ball) and P (Predictive).


177

Table 13 Average Number o f Fixations and Fixation Durations fo r Temporal Phases o f Able-Bodied and Wheelchair Tennis Groups

Able-Bodied Participants (n ==31)

Wheelchair Participants (n =: 32)

(Mean Trial Length 6070 ms; SD = 0.79)

(Mean Trial Length 2470 ms; SD = 0.39)

M

M

SD

SD

Ritual phase Average Number of Fixations Average Fixation Durations (ms)

(M = 4320 ms, SD = 0.93) 3.30 2.19 635 1.19

Ritual Phase

Preparatory phase Average Number of Fixations Average Fixation Durations (ms)

(M= 900 ms, SD = 0.20) 1.75 1.03 505 0.86

Prep+Exec Phases (M = 930 ms, SD = 0.18) 1.78 0.76 410 1.10

Execution phase Average Number of Fixations Average Fixation Durations (ms)

(M= 570, SD = 0.37) 1.41 1.04 375 0.76

Finishing phase Average Number of Fixations Average Fixation Durations (ms)

(M= 280, SD = 0.11) 1.09 0.69 210 0.23

Finishing

(M = 1130 ms, SD =0.14) 2.00 1.78 550 1.83

(M = 410 ms, SD = 0.13) 0.51 0.33 230 0.39

Note: Fixations are the number of stopping points calculated from when they first began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended.


178

Table 14 Average Number o f Fixations and Fixation Durations for Temporal Phases for Wheelchair (WC) Predictive and WC Real Time Groups WC]Predictive Group (n = 17) (Mean Trial Length 2470 ms; SD = 0.39) M SD

WC]Real Time Group (n = 15) (Mean Trial Length 2470 ms; SD = 0.39) M SD

Ritual phase Average number of fixations Average fixation durations (ms)

(M= 1130 ms, SD —0.14) 1.78 2.01 1.72 560

(M= 1130 ms, SD = 0.14) 2.08 1.78 540 1.91

Preparatory + Execution Phase Average number of fixations Average fixation durations (ms)

(M= 930 ms, SD = 0.18) 1.80 0.75 1.00 430

(M = 930 ms, SD = 0.18) 1.75 0.90 400 0.79

Finishing phase Average number of fixations Average fixation durations (ms)

(M= 410 ms, SD = 0.13) 0.63 0.59 230 0.37

(M= 410 ms, SD = 0.13) 0.41 0.54 265 0.47

Note: The WC Predictive Group was composed of participants who predicted the tossing area ahead of time. The WC Real Time Group was participants who followed the motion as it was produced by the model. Fixations are the number of stopping points calculated from when they first began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended.


Table 15 Average Number o f Fixations and Fixation Durations fo r Temporal Phases and Lookzones o f the Able-Bodied Group (n = 31)

GBP

Lookzones

NDA

B

ARS

P

M

SD

M

SD

M

SD

M

SD

M

SD

0.90 700

1.18 1.26

1.19 705

2.32 0.36

0.80 670

3.16 0.83

0.08 305

4.14 1.30

0.34 630

1.09 0.23

Preparation phase (M= 900 ms, SD = 0.20) 0.22 Average number of fixations 510 Average fixation durations (ms)

1.91 0.54

0.80 660

0.88 1.03

0.39 510

0.97 0.89

0.31 320

1.18 0.10

0.10 370

0.83 0.11

Execution phase (M = 570 ms, SD = 0.37) Average number o f fixations Average fixation durations (ms)

0.06 230

2.19 0.07

0.35 370

1.36 0.89

0.80 475

0.57 0.23

0.21 290

2.11 0.14

N/A N/A

N/A N/A

Finishing phase (M= 280 ms, SD = 0.11) Average number of fixations Average fixation durations (ms)

0.03 185

0.81 0.23

0.08 200

1.01 0.19

0.25 210

0.23 0.11

0.80 220

0.66 0.08

N/A N/A

N/A N/A

Ritual phase (M= 4320 ms, SD = 0.93) Average number of fixations Average fixation durations (ms)

Note: The average length of each trial for able-bodied participants was 6070 ms (SD = 0.79). Fixations are the number o f stopping points calculated from when they began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended. Lookzone abbreviations include: GBP (General Body Position), NDA (Non Dominant Arm), ARS (Arm Racquet Shoulder), B (Ball) and P (Predictive). N/A refers to non-applicable, as the P lookzone was not present in the execution and finishing phases, and where no fixations are present no fixation durations can be calculated. Numbers in bold print represent the most number of fixations or longest fixation durations within a phase.


180

Table 16 Average Number o f Fixations and Fixation Durations fo r Temporal Phases and Lookzones o f the Wheelchair Group (n = 32) Lookzones

NDA

GBP

ARS

B

P

M

SD

M

SD

M

SD

M

SD

M

SD

0.30 600

1.06 1.08

0.79 670

2.18 0.99

0.41 645

1.09 1.21

0.02 210

2.83 0.86

0.61 640

15.56 2.11

Preparation/execution phase (M= 930 ms, SD = 0.18) 0.09 0.73 Average number of fixations 375 0.81 Average fixation durations (ms)

0.64 545

0.37 0.72

0.60 510

0.77 0.79

0.08 165

0.83 1.11

0.30 470

3.16 2.23

Finishing phase (M = 410 ms, SD = 0.13) Average number of fixations Average fixation durations (ms)

0 N/A

0 N/A

0.10 220

0.78 2.10

0.39 295

0.23 1.08

N/A N/A

N/A N/A

Ritual phase ( M - 1130 ms, SD = 0.14) Average number of fixations Average fixation durations (ms)

0 N/A

0 N/A

Note: The average length of each trial for wheelchair participants was 2470 ms (SD = 0.39). Fixations are the number of stopping points calculated from when they first began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended. Lookzone abbreviations include: GBP (General Body Position), NDA (Non Dominant Arm), ARS (Arm Racquet Shoulder), B (Ball) and P (Predictive). N/A refers to non-applicable, as the P lookzone was not present in the execution and finishing phases, and where no fixations are present no fixation durations can be calculated. Numbers in bold print represent the most number of fixations or longest fixation durations within a phase.


181

Table 17 Average Number o f Fixations and Fixation Durations fo r Temporal Phases and Lookzones o f the Wheelchair (WC) Predictive Group (n = 17)

GBP

Lookzones

NDA

F»

B

ARS

M

SD

M

SD

M

SD

M

SD

M

SD

0.20 555

1.00 0.36

0.73 700

1.32 0.41

0.13 600

0.64 0.98

0.12 190

1.73 0.39

0.98 730

1.71 0.32

Preparation/execution phase (M = 930 ms, SD = 0..18) Average number of fixations 0.03 0.34 400 0.34 Average fixation durations (ms)

0.70 600

1.00 0.32

0.20 470

0.39 0.67

0.16 175

0.81 0.71

0.73 480

0.69 0.73


0 N/A

0 N/A

0.21 190

0.47 0.47

0.41 300

0.93 0.98

N/A N/A

N/A N/A


0 N/A

0 N/A

Note: WC Predictive Group was composed of participants who predicted the tossing area ahead o f time. Fixations are the number of stopping points calculated from when they began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended. The average length o f each trial for wheelchair participants was 2470 ms (SD = 0.39). Lookzone abbreviations include: GBP (General Body Position), NDA (Non Dominant Arm), ARS (Arm Racquet Shoulder), B (Ball) and P (Predictive). N/A refers to non-applicable, as the P lookzone was not present in the execution and finishing phases, and where no fixations are present no fixation durations can be calculated. Numbers in bold print represent the most number of fixations or longest fixation durations within a phase.


182

Table 18 Average Number o f Fixations and Fixation Durations fo r Temporal Phases and Lookzones o f the Wheelchair (WC) Real Time Group (n = 15)

GBP

Lookzones

NDA

B

ARS

P

M

SD

M

SD

M

SD

M

SD

M

0.32 610

1.00 0.99

1.01 730

0.74 0.99

0.74 660

0.81 1.03

0.01 160

0.32 0.80

0 0 N/A N/A

Preparation/execution phase (M= 930 ms, £D = 0.18) Average number of fixations 0.13 0.51 Average fixation durations (ms) 360 0.70

0.74 545

0.20 0.61

0.97 550

0.70 0.79

0.10 165

0.47 0.19

0 0 N/A N/A


0 N/A

0 N/A

0.10 230

0.63 0.47

0.32 290

0.10 0.38

N/A NA N/A N/A


0 N/A

0 N/A

SD

Note: The WC Real Time Group was participants who followed the motion of the serve as it was produced by the model. The average length o f each trial for wheelchair participants was 2470 ms (SD = 0.39). Fixations are the number of stopping points calculated from when they began. Fixation durations are measures of fixations (>100 ms) from when they first occurred to when they ended. Lookzone abbreviations include: GBP (General Body Position), NDA (Non Dominant Arm), ARS (Arm Racquet Shoulder), B (Ball) and P (Predictive). N/A refers to non-applicable, as the P lookzone was not present in the execution and finishing phases, and where no fixations are present no fixation durations can be calculated. Numbers in bold print represent the most number of fixations or longest fixation durations within a phase.


183

Table 19 Average Pursuit Tracking Time fo r Able-Bodied and Wheelchair Tennis Groups within Lookzone B (the Ball)

Ritual phase

Preparatory phase

Execution phase

Finishing phase

Total service time

Able-Bodied Group (n = 31) (Mean Trial Length 6070 ms; SD = 0.79) M SD %

Wheelchair Group (n = 32) (Mean Trial Length 2470 ms; SD = 0.39) M SD

(M= 4320 ms, SD = 0.93) 1015 1.18

Ritual phase 23.50

(M= 900 ms, SD = 0.20) 10 0.76

1.11

(M= 570 ms, SD = 0.37) 38 0.56

6.67

(M= 280 ms, 5D = 0.11) 51 0.91

18.21

(M= 6070 ms, SD = 0.79) 1115 1.04

18.37

Prep/exec

Finishing

Total

%

CM= 1130 ms, SD = 0.14) 30 0.49

2.70

(M= 930 ms, SD = 0.18) 141 1.33

15.16

(M= 410 ms, SD = 0.13) 289 0.22

70.49

(M= 2470 ms, SD = 0.39) 460 0.76

18.62

Note: Pursuit tracking is a measurement of time as movements of the eye (