Evidence for early arousal‐based differentiation of emotions in children’s musical production
Bibliographic record
Abstract
Accurate perception and production of emotional states is important for successful social interactions across the lifespan. Previous research has shown that when identifying emotion in faces, preschool children are more likely to confuse emotions that share valence, but differ in arousal (e.g. sadness and anger) than emotions that share arousal, but differ on valence (e.g. anger and joy). Here, we examined the influence of valence and arousal on children's production of emotion in music. Three-, 5- and 7-year-old children recruited from the greater Hamilton area (N = 74) 'performed' music to produce emotions using a self-pacing paradigm, in which participants controlled the onset and offset of each chord in a musical sequence by repeatedly pressing and lifting the same key on a MIDI piano. Key press velocity controlled the loudness of each chord. Results showed that (a) differentiation of emotions by 5-year-old children was mainly driven by arousal of the target emotion, with differentiation based on both valence and arousal at 7 years and (b) tempo and loudness were used to differentiate emotions earlier in development than articulation. The results indicate that the developmental trajectory of emotion understanding in music may differ from the developmental trajectory in other domains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".