Individual Differences in Music-Perceived Emotions
Bibliographic record
Abstract
Previous music and emotion research suggests that individual differences in empathy, alexithymia, personality traits, and musical expertise might play a role in music-perceived emotions. In this study, we investigated the relationship between these individual characteristics and the ability of participants to recognize five basic emotions (happiness, sadness, tenderness, fear, and anger) conveyed by validated excerpts of film music. One hundred and twenty participants were recruited through an online platform and completed an emotion recognition task as well as the IRI (Interpersonal Reactivity Index), TAS-20 (Toronto Alexithymia Scale), BFI (Big Five Inventory), and Gold-MSI (Goldsmiths Musical Sophistication Index). While participants recognized the emotions depicted by the music at levels that were better than chance, their performance accuracy was negatively associated with the externally oriented thinking subscale from the TAS-20. Our results suggest that alexithymia, previously linked to a deficit in perception of facial and vocal expressions of emotion, is also associated with difficulties in perception of emotions conveyed by music.
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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 | 0.000 | 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) | 0.003 | 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".