Effect of Exercise-Related Factors on the Perception of Time
Bibliographic record
Abstract
The concept of time whether considered through the lenses of physics or physiology is a relative measure. Alterations in time perception can have serious implications in sport, fitness and work. Accurate perception of time is an important skill with many time constrained sports (i.e., basketball, North American football, tennis, gymnastics, figure skating, ice hockey, and others), and work environments (i.e., workers who need to synchronize their actions such as police and military). In addition, time distortions may play a role in exercise adherence. Individuals may be disinclined to continue with healthy, exercise activities that seem protracted (time dilation). Two predominant theories (scalar expectancy theory and striatal beat frequency model) emphasize the perception of the number of events in a period and the role of neurotransmitters in activating and coordinating cortical structures, respectively. A number of factors including age, sex, body temperature, state of health and fitness, mental concentration and exercise intensity level have been examined for their effect on time perception. However, with the importance of time perception for work, sport and exercise, there is limited research on this area. Since work, sports, and exercise can involve an integration of many of these aforementioned factors, they are interventions that need further investigations. The multiplicity of variables involved with work, sport, and exercise offer an underdeveloped but fruitful field for future research. Thus, the objective of this review was to examine physiological and psychological factors affecting human perception of time and the mechanisms underlying time perception and distortion with activity.
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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 | 0.002 |
| 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".