“Music is my drug”: Alexithymia, empathy, and emotional responding to music
Bibliographic record
Abstract
Like alcohol or other drugs, music is often enjoyed by humans for its mood-altering effects. However, there is substantial individual variation in emotional responding to music (ERM). The present study investigated potential roles of trait variables in ERM. Recruitment and testing of 205 adult regular music listeners was accomplished online. They were asked to complete the Geneva Emotional Music Scale (GEMS) retrospectively by rating the felt intensity of 45 music-related emotions based on what they typically experienced when listening to their favorite music. They also completed instruments assessing traits of alexithymia, affect intensity, and empathy, as well as the Big Five factors. Alexithymia, affect intensity, and empathy, but not the Big Five, were moderately positively correlated with ERM as measured by GEMS. In a hierarchical regression, alexithymia and empathy were significant positive predictors of ERM after controlling for the other variables; extraversion was also significant in the final model. The role of empathy as a predictor of ERM was consistent with the emotional contagion interpretation of ERM. The unexpected positive relationship of alexithymia with ERM suggests that alexithymic listeners may rely on music to help them experience emotions more fully. Limitations and potential implications of the findings are discussed.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".