The effects of valence and arousal on the emotional modulation of time perception: Evidence for multiple stages of processing.
Bibliographic record
Abstract
Previous research has demonstrated that both emotional valence and arousal can influence the subjective experience of time. The current research extends this work by (1) identifying how quickly this emotional modulation of time perception can occur and (2) examining whether valence and arousal have different effects at different stages of perception. These questions were addressed using a temporal bisection task. In each block of this task, participants are trained to distinguish between two different exposure durations. Participants are then shown stimuli presented at a number of durations that fall between the two learned times, and are asked to indicate whether the test stimulus was closer in duration to the shorter or longer learned item. In the current study, participants completed blocks of trials in which the durations were "Short" (100-300 ms) or "Long" (400-1600 ms). Stimuli consisted of neutral photographs as well as four categories of emotional images: high-arousal negative, high-arousal positive, low-arousal negative, and low-arousal positive. In Short blocks, arousing and nonarousing negative images were judged to have been shown for shorter durations than they actually were (i.e., the duration was underestimated); this effect occurred at durations as brief as 133 ms. In Long blocks, the display time for highly arousing negative items was overestimated, whereas durations were underestimated for highly arousing positive items and less arousing negative items. These data suggest that arousal and valence have different effects at different stages of perception, possibly due to the different neural structures involved at each stage of the emotional modulation of time perception.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".