Sport expertise and physical exercise are associated with “hot” executive functioning: An electrophysiological examination of reward processing in collegiate athletes
Bibliographic record
Abstract
OBJECTIVE: Prior research has revealed potential effects of sports expertise and physical exercise on cognition, though there is limited research examining their effects on the "hot," emotional-processing aspects of executive functioning (e.g., valence and reward processing important for decision-making). The present study aimed to address this gap by examining event-related brain potentials (ERPs) during a reward-processing task in athletes versus non-athletes, while also investigating if sport expertise and exercise influence this electrophysiological response. METHOD: A total of 45 participants, including 22 athletes (55% women, 45% men) and 23 non-athlete controls (57% women, 43% men) between the ages of 18-27, completed a "virtual T-maze" environment task involving a rewarded forced choice that elicits the reward positivity (Rew-P), an ERP component associated with reward processing. Rew-P peak amplitude was compared between groups, and both sport expertise and frequency of strenuous exercise were investigated as potential predictors of the Rew-P in athletes. RESULTS: = .01) each accounted for a significant proportion of variability in the Rew-P peak amplitude in athletes. CONCLUSIONS: Results indicate that, for young adults, sport expertise and physical exercise may each account for heightened electrophysiological reward sensitivity in athletes. Potential implications are discussed for decision-making, an integral cognitive process in sports that is driven by reward processing, and the role of reward-seeking and motivation in sport proficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".