Recognition of vocal socioemotional expressions at varying levels of emotional intensity.
Bibliographic record
Abstract
Nonverbal expressions of emotion can vary in intensity, from ambiguous to prototypical exemplars: for instance, facial displays of happiness may range from a faint smile to a full-blown grin. Previous work suggests that the accuracy with which facial expressions are recognized as the intended emotion increases with emotional intensity, although this pattern depends on the displayed emotion. Less is known about the association between emotional intensity and the recognition of vocal emotional expressions (affective prosody), which also convey information about others' socioemotional intent but are perceived and interpreted differently than facial expressions. The current study examined listeners' ability to recognize emotional intent in morphed vocal prosody recordings that varied in emotional intensity from neutral to prototypical exemplars of basic emotions (anger, disgust, fear, happiness, sadness) and social expressions (friendliness, meanness). Results suggest that listeners' accuracy in identifying the intended emotional intent in each recording increased nonlinearly with emotional intensity. This pattern varied by emotion type: for instance, accuracy for anger rose steeply with increasing emotional intensity before plateauing, whereas accuracy for happiness remained unchanged across low-intensity exemplars but increased thereafter. These findings highlight emotion-specific ways in which dynamic changes in emotional intensity inform perceptions of socioemotional intent in emotional prosody. Moreover, these results also point to potential challenges in emotional communication in social interactions that rely primarily on the voice, with many low-intensity expressions having a higher probability of being misinterpreted. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".