Judgment of Emotional Nonlinguistic Vocalizations: Age-Related Differences
Bibliographic record
Abstract
Humans make extensive use of vocal information to attribute emotional states to other individuals. To date, most studies exploring perception of vocal emotions have done so in the context of speech prosody, although nonlinguistic emotional vocalizations represent an important, perhaps more universal, means to express emotions. Here, we explored the perception of emotional nonlinguistic vocalizations in healthy individuals, with an emphasis on potential age- and sex-related differences. Sixty participants rated 563 positive (e.g., laughs, sexual vocalizations), negative (e.g., cries, screams of fear), and neutral vocalizations (e.g., coughs), according to the valence, intensity, and authenticity of the emotion expressed. Ratings were consistent among individuals, suggesting that valence is an adequate measure of emotional categorization. An important effect of age emerged: (a) age by vocalization category interactions were observed for both valence and intensity ratings, and (b) younger participants rated stimuli as more emotional than older individuals (i.e., higher valence for positive, lower for negative, and more intense for both positive and negative). We also found a sex effect in the authenticity ratings: older women rated the vocalizations as less authentic than younger women whereas authenticity judgments did not differ between the two age groups in men. Taken together, these findings suggest that, as previously observed for facial expressions and prosody, the judgments of emotional vocalizations may vary with age.
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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 | 0.004 |
| 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 | 0.000 | 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".