Enhanced recognition of vocal emotions in individuals with naturally good musical abilities.
Bibliographic record
Abstract
= 169) comprised musically trained and untrained listeners who varied widely in their musical skills, as assessed through self-report and performance-based measures. The emotion recognition tasks required listeners to categorize emotions in nonverbal vocalizations (e.g., laughter, crying) and in speech prosody. Music training was associated positively with emotion recognition across tasks, but the effect was small. We also found a positive association between music perception abilities and emotion recognition in the entire sample, even with music training held constant. In fact, untrained participants with good musical abilities were as good as highly trained musicians at recognizing vocal emotions. Moreover, the association between music training and emotion recognition was fully mediated by auditory and music perception skills. Thus, in the absence of formal music training, individuals who were "naturally" musical showed musician-like performance at recognizing vocal emotions. These findings highlight an important role for factors other than music training (e.g., predispositions and informal musical experience) in associations between musical and nonmusical domains. (PsycInfo Database Record (c) 2022 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".