The Effect of Emotional Spoken Words on Time Perception Depends on the Gender of the Speaker
Bibliographic record
Abstract
The aim of the present study was to investigate the influence of the emotional content of words marking brief intervals on the perceived duration of these intervals. Three independent variables were of interest: the gender of the person pronouncing the words, the gender of participants, and the valence (positive or negative) of the words in conjunction with their arousing properties. A bisection task was used and the tests, involving four different combinations of valence and arousing conditions (plus a neutral condition), were randomized within trials. The main results revealed that when the valence is negative, participants responded ‘short’ more often when words were pronounced by women rather than by men, and this effect occurred independently of the arousal condition. The results also revealed that overall, males responded ‘longer’more often than females. Finally, in the negative and low arousal condition, the Weber ratio was higher (lower sensitivity) when a male voice was used than when a female voice was used. This study shows that the gender of the person producing the stimuli whose duration is to be judged should be taken into account when analyzing the effect of emotion on 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".