Athletics and attention: Bi-directional influences in the lab and on the field
Bibliographic record
Abstract
Selectively attending to some information while ignoring other information is crucial for athletic success. Active participation in athletics is also beneficial when attention is measured in the laboratory. This review examines this bi-directional relationship between athletics and attention. The introduction orients readers to the concept of selective visual attention. In the following section we review the evidence that athletic participation influences performance on laboratory measures of visual attention, including tasks of spatial orienting, spatial shifting, attention distribution, temporal sequencing, and the control of action. In the third section we review how attention measures are influenced by contextual factors that are also known to influence athletic performance. These include behavioral practices like exercise, sleep, and hydration; environmental factors like thermal stress, competition, and distraction; and individual differences in personality, age, and gender. In the next section we situate all this empirical evidence in the evolving theoretical understanding of attention in the cognitive sciences over the past five decades. In doing so, it becomes clear that research on athletics is an important database to consider when developing models of attention. By bringing these literatures together, a stronger theoretical foundation is sought that may contribute positively to research on both optimal athletic performance and framework development in attention.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".