The flash-lag effect in ball sports players
Bibliographic record
Abstract
Estimating motion and trajectories is a core function of human visual system. It is particularly relevant for ball sports players such as baseball or tennis players, who must accurately catch or hit a ball to win their game. The flash-lag illusion is a motion-based illusion where one will see a transiently appearing “flash” lagging behind a moving object. It could be caused by predictive mechanisms which help to anticipate the position of the moving object. In this study, we wanted to test whether subjects who are trained at anticipating trajectories such as ball sports players are particularly sensitive to that illusion. We tested and compared three groups of participants, all students from the Université de Montréal: ball sports players, non-ball sports athletes, and controls on a standard flash-lag effect paradigm. A bar was horizontally moving on screen and a flashed bar was presented either above or below the moving bar. The participants had to report whether the flashed bar appeared left or right relative to the moving bar. We observed a typical flash-lag effect in all groups with no difference between groups. This suggest that the flash-lag effect could not be due to anticipation mechanisms (e.g. latency difference or mis localization) or that training those mechanisms has no effect.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".