Elevations in EEG Power Spectra During the Recall of Sport Failure Amongst University Athletes
Bibliographic record
Abstract
Previously, we showed that university athletes demonstrate cardiac reactivity resembling an acute stress response while recalling a previous sport failure. Athletes who reported higher levels of self- compassion showed greater elevation of parasympathetic nervous system reactivity during recall of failure, and also showed more adaptive behavioural reactions, less maladaptive thoughts, and less negative affect during the task. Here, we analyzed changes in power spectra of the electroencephalogram (EEG) before, during, and after the recall of a previous sports failure, and whether individual differences in self-compassion or related constructs impact EEG changes during recall. Significant reactivity to, and recovery from, the recall task was observed across all EEG bandwidths: delta power decreased, and theta, low alpha, high alpha, low beta, and high beta power all increased from baseline to recall. Analogous EEG power changes were observed during recovery: delta power increased, while theta, low alpha, high alpha, low beta, and high beta power all decreased to baseline levels. Large gender differences were observed, with females generally showing greater EEG power across bandwidths during all phases of the experiment. Higher levels of self-compassion were associated with reduced theta power at baseline and during the recovery phase. Reactivity and recovery scores of EEG power were associated with self-reported self-esteem in the low alpha band: individuals reporting higher self-esteem showed greater increases in low alpha power during the recall task and greater decreases in low alpha power during recovery. These results are amongst the first to examine EEG power changes during experiences of acute stress and may have implications for improving how athletes recover from failures in sporting events.
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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) | 0.001 | 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".