Topical Review: Perceptual‐cognitive Skills, Methods, and Skill‐based Comparisons in Interceptive Sports
Bibliographic record
Abstract
SIGNIFICANCE: We give a comprehensive picture of perceptual-cognitive (PC) skills that could contribute to performance in interceptive sports. Both visual skills that are low level and unlikely influenced by experience and higher-level cognitive-attentional skills are considered, informing practitioners for identification and training and alerting researchers to gaps in the literature.Perceptual-cognitive skills and abilities are keys to success in interceptive sports. The interest in identifying which skills and abilities underpin success and hence should be selected and developed is likely going to grow as technologies for skill testing and training continue to advance. Many different methods and measures have been applied to the study of PC skills in the research laboratory and in the field, and research findings across studies have often been inconsistent. In this article, we provide definitional clarity regarding whether a skill is primarily visual attentional (ranging from fundamental/low-level skills to high-level skills) or cognitive. We review those skills that have been studied using sport-specific stimuli or tests, such as postural cue anticipation in baseball, as well as those that are mostly devoid of sport context, considered general skills, such as dynamic visual acuity. In addition to detailing the PC skills and associated methods, we provide an accompanying table of published research since 1995, highlighting studies (for various skills and sports) that have and have not differentiated across skill groups.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".