Advancements to the understanding of expert visual anticipation skill in striking sports.
Bibliographic record
Abstract
Superior performance in striking sports requires anticipation skill because of constraints imposed on the performer, which can make it extremely difficult to achieve the motor skill goal. This article reviews the empirical literature on expert visual anticipation in striking sports since 2012 to determine if it has contributed to advancement of a theoretical model. First, methodologies used to study visual anticipation are briefly described. Second, an existing model is outlined to present what is known about the theoretical underpinning of expert visual anticipation. Third, empirical evidence of key factors that contribute to expert visual anticipation are discussed. Moreover, whether anticipation skill can be improved and transferred to different contexts is discussed. The review identifies that there are multiple key factors that contribute to expert visual anticipation performance, which need to be more thoroughly accommodated as part of the theoretical model. There is still less empirical evidence of learning and transfer of visual anticipation skill even though both of these are vital to improve motor skill performance, as well as apply any improvement to anticipation skill in different in situ settings. Collectively, this review provides an update of the research on expert visual anticipation and identifies future research directions that can continue to further knowledge in striking sports. © 2017 Canadian Psychological Association.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".