Instrumental music training relates to intensity assessment but not emotional prosody recognition in Mandarin
Bibliographic record
Abstract
Building on research demonstrating the benefits of music training for emotional prosody recognition in nontonal languages, this study delves into its unexplored influence on tonal languages. In tonal languages, the acoustic similarity between lexical tones and music, along with the dual role of pitch in conveying lexical and affective meanings, create a unique interplay. We evaluated 72 participants, half of whom had extensive instrumental music training, with the other half serving as demographically matched controls. All participants completed an online test consisting of 210 Chinese pseudosentences, each designed to express one of five emotions: happiness, sadness, fear, anger, or neutrality. Our robust statistical analyses, which included effect size estimates and Bayesian factors, revealed that music and nonmusic groups exhibit similar abilities in identifying the emotional prosody of various emotions. However, the music group attributed higher intensity ratings to emotional prosodies of happiness, fear, and anger compared to the nonmusic group. These findings suggest that while instrumental music training is not related to emotional prosody recognition, it does appear to be related to perceived emotional intensity. This dissociation between emotion recognition and intensity evaluation adds a new piece to the puzzle of the complex relationship between music training and emotion perception in tonal languages.
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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 | 0.000 | 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".