Perceptual Judgments for Table Tennis Serve Recognition: An Event-Related Potentials Study
Bibliographic record
Abstract
In the present study, visual attention processes in complex, sport-related decision-making tasks were examined. Psychophysiological and performance data recorded from 15 advanced table tennis athletes and 15 intermediate level undergraduates were compared. A total of 240 three-dimensional pictures of stimuli composed of a white ball and hitting location (black shade point) were presented via a screen, in which 25% represented side-backspin serves, and the other 75% represented non-side-topspin serves. Participants were instructed to report the types of serves. The results indicated that table tennis athletes responded more quickly and accurately. C1 and P1components were induced in the occipital region, N1 in the central region, and P3 in all regions. For table tennis athletes, in the phase of early sensory processing for stimuli features (such as hitting location), the cerebral cortex was activated at a higher level in comparison with undergraduates. This may be caused by the long-term exercise training. Athletes have to be very sensitive to the physical features of relevant movement stimuli. In the phase of recognizing stimuli structures or patterns, advanced athletes’ cerebral cortexes were activated higher and faster. This may help them more effectively match visual information about serves to patterns stored in long-term memory.
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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 | 0.003 |
| 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) | 0.003 | 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".