Dynamics of Saccadic Adaptation: Differences Between Athletes and Nonathletes
Bibliographic record
Abstract
PURPOSE: The aim of the study was to delineate differences in saccadic adaptation characteristics between a population of racquet sports athletes and nonathletes. METHODS: Eye movements were recorded at 120 Hz using a video-based eye tracker (ELMAR 2020) in a sample of 27 athletes (varsity badminton and squash players) and 14 nonathletes (<3 hours/week participation in recreational sports). Responses to negative positional error and positive positional error were studied in two sessions on separate days. Negative positional errors were induced by displacing the stimuli backwards by 3 degrees from the initial target step (12 degrees). Likewise, positive positional errors were induced by displacing the stimuli forward by 3 degrees . Amplitude gains were calculated for trials before, during, and after the adaptation phase. The magnitude and the rate of change of saccadic adaptation were determined from the amplitude gains. Differences between the groups were compared using regression analysis. RESULTS: No significant differences were found between the two groups in the magnitude of saccadic adaptation, both for negative (athletes -60%, nonathletes -57%) and positive (athletes +26%, and nonathletes +27%) positional error. Racquet sports athletes showed a significantly faster rate of adaptation for the positive positional error. A significant difference was not observed in the rate of adaptation for the negative positional error. CONCLUSIONS: Racquet sports athletes and nonathletes adapt to positional error signals by similar amounts. However, racquet sports athletes respond to positive positional errors at a faster rate, suggesting that a strategic component or environmental influences (such as practice) may play a role in saccadic adaptation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".