Distinct eye movement patterns enhance dynamic visual acuity
Bibliographic record
Abstract
Dynamic visual acuity (DVA) is the ability to resolve fine spatial detail in dynamic objects during head fixation, or in static objects during head or body rotation. This ability is important for many activities such as ball sports, and a close relation has been shown between DVA and sports expertise. DVA tasks involve eye movements, yet, it is unclear which aspects of eye movements contribute to successful performance. Here we examined the relation between DVA and the kinematics of smooth pursuit and saccadic eye movements in a cohort of 23 varsity baseball players. In a computerized dynamic-object DVA test, observers reported the location of the gap in a small Landolt-C ring moving at various speeds while eye movements were recorded. Smooth pursuit kinematics-eye latency, acceleration, velocity gain, position error-and the direction and amplitude of saccadic eye movements were linked to perceptual performance. Results reveal that distinct eye movement patterns-minimizing eye position error, tracking smoothly, and inhibiting reverse saccades-were related to dynamic visual acuity. The close link between eye movement quality and DVA performance has important implications for the development of perceptual training programs to improve DVA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".