Expert Performance in Sport: A Cognitive Perspective
Bibliographic record
Abstract
Introduction 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".