Cross-Cultural Differences in the Processing of Non-Verbal Affective Vocalizations by Japanese and Canadian Listeners
Bibliographic record
Abstract
The Montreal Affective Voices (MAVs) consist of a database of non-verbal affect bursts portrayed by Canadian actors, and high recognitions accuracies were observed in Canadian listeners. Whether listeners from other cultures would be as accurate is unclear. We tested for cross-cultural differences in perception of the MAVs: Japanese listeners were asked to rate the MAVs on several affective dimensions and ratings were compared to those obtained by Canadian listeners. Significant Group × Emotion interactions were observed for ratings of Intensity, Valence, and Arousal. Whereas Intensity and Valence ratings did not differ across cultural groups for sad and happy vocalizations, they were significantly less intense and less negative in Japanese listeners for angry, disgusted, and fearful vocalizations. Similarly, pleased vocalizations were rated as less intense and less positive by Japanese listeners. These results demonstrate important cross-cultural differences in affective perception not just of non-verbal vocalizations expressing positive affect (Sauter et al., 2010), but also of vocalizations expressing basic negative emotions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".