Gender and cerebral lateralization of audio-visual perception of emotion
Bibliographic record
Abstract
Presentation of brief (120ms, 160ms, 520ms) audio prosody-, video- and audio-visual clips containing congruent emotion (anger, fear, happiness, neutral, sadness/ positive- and negative valence) was used in a divided visual field technique/dichotic listening behavioral experiment consisting of 17 males and 17 females to investigate a possible gender difference in cerebral lateralization in perception of emotion. Clips were created from Montréal affective voices and the Montréal Pain and Affective Face Clips. Accuracy percentages of correct recognition of emotion were recorded. Findings showed no support for either the right-hemisphere- or the valence hypothesis. Gender as a between subject factor was non significant. Clips containing both audio and video had the highest accuracy score of all modalities. Audio-only prosody had significant lower accuracy score compared to video-only and audio-visual clips. Positive valence in the short length may have an early accuracy advantaged compared to negative valence in the audio-visual modality that dissipates in 120ms-160ms range, with the accuracy difference disappearing between the categories. The same advantage can be found in anger, while happiness, fear and neutral have no significant differences in accuracy in lengths in the audio-visual modality.
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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 | 0.001 |
| 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) | 0.006 | 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".