Athletes Demonstrate Superior Dynamic Visual Acuity
Bibliographic record
Abstract
SIGNIFICANCE: Athletes exhibit better dynamic visual acuity (DVA) compared with nonathletes, whereas action video game players (VGPs) perform more similarly to controls despite having similar static visual acuity and refractive errors. The differences in DVA between groups were not related to differences in static visual acuity, refractive error, or smooth pursuit gain. PURPOSE: The purpose of the study was to examine whether athletes and VGPs have superior DVA than controls (nonathletes, nongamers). METHODS: Forty-six participants (15 athletes, 11 VGPs, 20 controls) aged 21.7 years (standard deviation, 2.8 years) were recruited. Participants were emmetropic with equivalent monocular and binocular static visual acuity between groups. Dynamic visual acuity was assessed using predictable (horizontal) and unpredictable (random) motion targets at velocities of 5, 10, 20, and 30°/s. Smooth pursuit eye movements were assessed using a horizontal motion step-ramp stimulus at the same speeds. This study was pre-registered with the Center for Open Science (https://osf.io/eu7qc). RESULTS: At 30°/s, there were significant main effects of group (F = 4.762, P = .01) and motion type (F = 9.538, P = .004). Tukey post hoc analysis for groups indicated that athletes performed better than did the control group (t = -2.919, P < .02). An omnibus (group × motion type × speed) repeated measures ANOVA showed a main effect of speed (F = 110.137, P < .001) and a speed × motion-type interaction (F = 27.825, P < .001). Dynamic visual acuity decreased as speed increased, and the slope of the change was greater for random than for horizontal motion. Smooth pursuit gains were not significantly different between groups (P > .05). CONCLUSIONS: Athletes have superior dynamic visual acuity performance compared with controls at 30°/s. This between-group difference cannot be fully explained by differences in smooth pursuit eye movements and therefore may reflect other differences between the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".