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Record W2289086212 · doi:10.1017/cbo9780511816796.027

Expert Performance in Sport: A Cognitive Perspective

2006· book-chapter· en· W2289086212 on OpenAlexaff
Nicola J. Hodges, Janet L. Starkes, Clare MacMahon

Bibliographic record

VenueCambridge University Press eBooks · 2006
Typebook-chapter
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsPsychologyAthletesCognitionSport psychologyPerspective (graphical)RecallCognitive psychologyPerceptionAnticipation (artificial intelligence)Cognitive scienceApplied psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The goal of this chapter is to present what is currently known about expert performance in sport. Research on expert performance in sport is a relatively recent area of inquiry covering only the last 30 years. Our view of its evolution is that there have been three overlapping phases in its development. During the 1970s and 1980s much of sport research employed recipient paradigms popular within experimental and cognitive psychology. Typical research of this time involved testing skilled and less-skilled or novice groups of athletes on sport-specific tests of recall and recognition, temporal and spatial occlusion of visual information, and anticipation (Abernethy, Thomas, & Thomas, 1993; Starkes, Helsen, & Jack, 2000). Again, following general trends in psychology verbal-protocol analyses of expert athletes were also published (Chiesi, Spilich, & Voss, 1979; McPherson, 1993a). At the end of the 1980s and early in the 1990s, developments in the recording and analyses of eye movements (Goulet, Bard, & Fleury, 1989; Vickers, 1992) and kinematic data (Carnahan, 1993) made it feasible to examine the eye movements of expert performers in contrast with less-skilled individuals to determine what athletes focused on and how their eye-movement patterns differed from less-skilled athletes (for reviews see Starkes et al., 2000; Williams, Davids, &Williams, 1999). The focus until the 1990s was largely perceptual-cognitive and aimed at establishing where differences existed between experts and novices within a particular sport domain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.258
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations100
Published2006
Admission routes1
Has abstractyes

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