Getting off the Bench: EEG and HRV Differences Between Starters and Nonstarters
Bibliographic record
Abstract
There is a strong interest in what separates top-level performers from those who have yet to achieve the same level, across all performance domains. Advances in psychophysiology give insight into where these differences may lie in the minds and bodies of athletes. The present study compared gymnasts who were selected for the competitive lineup to those who were considered part of the Division I gymnastics beam squad but did not compete. This study compared their initial baselines, their response to heart rate variability and neurofeedback training, and their posttraining levels. Baseline differences existed for self-perception of consistency and confidence. There were no pre or post differences in heart rate variability measures. Sensorimotor rhythm electroencephalography (EEG) biofeedback training was associated with a decline in busy brain waves for the competitors. The competitors also had a lower ratio of intensity (intensity/high alpha) in both the pre- and post-EEG measures.
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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 | 0.000 | 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) | 0.004 | 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".