Categorical emotion recognition from voice improves during childhood and adolescence
Bibliographic record
Abstract
Converging evidence demonstrates that emotion processing from facial expressions continues to improve throughout childhood and part of adolescence. Here we investigated whether this is also the case for emotions conveyed by non-linguistic vocal expressions, another key aspect of social interactions. We tested 225 children and adolescents (age 5-17) and 30 adults in a forced-choice labeling task using vocal bursts expressing four basic emotions (anger, fear, happiness and sadness). Mixed-model logistic regressions revealed a small but highly significant change with age, mainly driven by changes in the ability to identify anger and fear. Adult-level of performance was reached between 14 and 15 years of age. Also, across ages, female participants obtained better scores than male participants, with no significant interaction between age and sex effects. These results expand the findings showing that affective prosody understanding improves during childhood; they document, for the first time, continued improvement in vocal affect recognition from early childhood to mid- adolescence, a pivotal period for social maturation.
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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 | 0.002 |
| 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) | 0.001 | 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".