Exercise-Induced Fatigue and Caffeine Supplementation Affect Psychomotor Performance but Not Covert Visuo-Spatial Attention
Bibliographic record
Abstract
Fatigue resulting from strenuous exercise can impair cognition and oculomotor control. These impairments can be prevented by administering psychostimulants such as caffeine. This study used two experiments to explore the influence of caffeine administered at rest and during fatiguing physical exercise on spatial attention-a cognitive function that is crucial for task-based visually guided behavior. In independent placebo-controlled studies, cohorts of 12 healthy participants consumed caffeine and rested or completed 180 min of stationary cycling. Covert attentional orienting was measured in both experiments using a spatial cueing paradigm. We observed no alterations in attentional facilitation toward spatial cues suggesting that covert attentional orienting is not influenced by exercise fatigue or caffeine supplementation. Response times were increased (impaired) after exercise and this deterioration was prevented by caffeine supplementation. In the resting experiment, response times across all conditions and cues were decreased (improved) with caffeine. Covert spatial attention was not influenced by caffeine. Together, the results of these experiments suggest that covert attentional orienting is robust to the effects of fatiguing exercise and not influenced by caffeine. However, exercise fatigue impairs response times, which can be prevented by caffeine, suggesting that pre-motor planning and execution of the motor responses required for performance of the cueing task are sensitive to central nervous system fatigue. Caffeine improves response time in both fatigued and fresh conditions, most likely through action on networks controlling motor function.
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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 | 0.000 |
| 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".