Degree of handedness, emotion, and the perceived duration of auditory stimuli
Bibliographic record
Abstract
Humans exhibit a remarkable ability to accurately judge time intervals, but this ability varies among individuals and across situations. Research suggests that arousal and attentional factors are consistently associated with subjective time distortions, and emotions such as anger, which can elicit arousal and attract attention, have frequently been studied in this context. Typically, viewing angry faces seems to consistently produce time overestimation relative to neutral faces, and the present study investigates the possibility that this effect extends to angry voices by means of a temporal bisection task. Additionally, this paper furthers previous findings that interhemispheric interaction as quantified by handedness strength (i.e., the degree to which one favours one hand over the other) is related to how individuals perceive future time points on an imagined time task, and explores the possibility that handedness strength differences may also manifest as differences in bisection task performance. Results showed that handedness strength was associated with differences in time perception in both objective (bisection) and subjective (imagined) contexts. Bisection task data further revealed that the angry stimulus was associated with decreased temporal sensitivity and a greater propensity to categorise stimuli as "short" as compared to the same stimulus spoken in a neutral voice, which contrasts with studies conducted using angry faces. Possible attentional explanations for these findings and suggestions for future research directions are discussed.
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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 | 0.004 |
| 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 | 0.001 | 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".