Relationship between dynamic visual acuity and multiple object tracking performance
Bibliographic record
Abstract
We assessed the association between measures of dynamic visual acuity and a multiple object tracking task in physically active young adults. Ninety-four young adults performed the dynamic visual acuity and multiple object tracking tasks. Dynamic visual acuity was measured for horizontal and random walk motion paths at four target speeds (5, 10, 20, and 30°/s). For the multiple object tracking task, participants had to track three out of eight balls for 10 s, and the object speed was adjusted by a staircase procedure. We found that multiple object tracking performance was associated with better identification of horizontally and randomly moving targets in the dynamic visual acuity test ( p < .001, r = −.35 [−.52, −.16]; and p < .001, r = −.52 [−.65, −.35]; respectively). This effect was consistent across all target speeds (all p-values<0.05). However, static visual acuity did not correlate with any measure of dynamic visual acuity or multiple object tracking ( p > 0.170 in all cases). This study provides novel insights into the association between the ability to identify horizontally and randomly moving targets and track multiple objects. Future studies are needed to determine the potential utility of dynamic visual acuity for talent identification and predicting sports performance in real-game situations.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".