Objective Neurophysiological Measures of Cognitive Performance in Elite Ice Hockey Players
Bibliographic record
Abstract
Introduction: Athletic peak performance is increasingly focused on cognitive and mental factors. In the current study, cognitive performance was measured by neurophysiological responses in elite Junior-A hockey players. Methods: Neurophysiological brain vital signs were extracted from event-related potentials (ERPs) to evaluate auditory sensation (the N100), basic attention (the P300), and cognitive processing (the N400). In total, we evaluated 348 athletes, across 17 teams, throughout different hockey arenas in British Columbia, Canada. While brain vital signs were collected to help manage concussion, the current investigation focused on a retrospective performance analysis of cognitive processing differences. Results: The results revealed three interesting findings: 1) Player position differences were detectable in sensory N100 latency, with significantly faster responses for forwards compared to defense; 2) Goalies showed significantly higher attention P300 amplitude compared to all other positions; and 3) Cognitive N400 processing differences were detectable only during competitive combine testing, showing 60ms latency differences between forwards and defense on average. Discussion: The current findings suggest that neurophysiological responses, which are also sensitive to concussion, may be used to identify sensory, attentional, and cognitive processing differences to help optimize peak performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".