The Effects of Physical and Mental Fatigue on Time Perception
Bibliographic record
Abstract
The perception of time holds a foundational significance regarding how we elucidate the chronological progression of events. While some studies have examined exercise effects on time perception during exercise periods, there are no studies investigating the effects of exercise fatigue on time perception after an exercise intervention. This study investigated the effects of physical and mental fatigue on time estimates over 30 s immediately post-exercise and 6 min post-test. Seventeen volunteers were subjected to three conditions: physical fatigue, mental fatigue, and control. All participants completed a familiarization session and were subjected to three 30 min experimental conditions (control, physical fatigue (cycling at 65% peak power output), and mental fatigue (Stroop task)) on separate days. Time perception, heart rate, and body temperature were recorded pre-test; at the start of the test; 5, 10, 20, 30 seconds into the interventions; post-test; and at the 6 min follow-up. Rating of perceived exertion (RPE) was recorded four times during the intervention. Physical fatigue resulted in a significant (p = 0.001) underestimation of time compared to mental fatigue and control conditions at the post-test and follow-up, with no significant differences between mental fatigue and control conditions. Heart rate, body temperature, and RPE were significantly (all p = 0.001) higher with physical fatigue compared to mental fatigue and control conditions during the intervention and post-test. This study demonstrated that cycling-induced fatigue led to time underestimation compared to mental fatigue and control conditions. It is crucial to consider that physical fatigue has the potential to lengthen an individual’s perception of time estimates in sports or work environments.
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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 | 0.001 | 0.004 |
| 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".