A new look at the potential links between music practice, empathy, and prosociality
Bibliographic record
Abstract
Engaging in music practice is often assumed to increase empathy and prosociality. However, data in support of this relationship are limited, leaving unclear which components of empathy (cognitive empathy, emotional contagion, and emotional disconnection) and prosocial behaviors, if any, would be affected. Here, we recruited musicians with more than 2 years of musical experience ( n = 80) and nonmusicians ( n = 89) to measure empathy (using subjective and objective measures) and prosociality (using economic games). We hypothesized that musicians would score higher than nonmusicians on empathy and prosociality, and that musicians who practice more would show greater effects. Using classical and Bayesian analyses of variance (ANOVAs), we found no difference between musicians and nonmusicians in empathy and prosociality, and no correlation with the amount of practice. Exploratory analyses revealed associations between the age of onset of music practice and empathy, suggesting that it is not music practice per se but specifically its initiation in early life that could be linked to empathy. These findings challenge the common assumption that music practice in general increases empathy and prosociality and invites to explore in which specific contexts music practice does so (e.g., particular age ranges or group-based settings).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".