Examining the impact of physiological stress on time perception: A systematic review and meta-analysis
Bibliographic record
Abstract
Stress is a ubiquitous experience that can significantly impact various aspects of human cognition and behavior, including time perception. Time perception, which refers to the ability to estimate and experience time intervals, plays a crucial role in everyday functioning and decision-making. However, whether stress affects how individuals perceive time remains unclear in literature. While some studies report that time perception can be underestimated (i.e., larger time production and shorter estimate) under certain stress conditions, others observe the opposite effect, with an overestimation of perceived time (i.e., shorter time production and larger estimate). To clarify the inconsistencies in the literature, we conducted a systematic review and meta-analysis of studies examining the effects of acute stress on time perception. The model, based on 437 participants exposed to stress and 434 control participants, demonstrated a significant effect favoring overestimated time under stress conditions (Cohen’s d = -0.40; 95% CI: -0.7037 to -0.1016). No significant effects of task type and socio-demographic factors were observed. • Under exposure to a physical stress, perceived time gets overestimated • A physical stress leads to a shorter time production and a larger time estimation • This overestimated perceived time is not influenced by socio-demographic factors • Neither the stress task nor the time perception task modulates this effect
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".