Emotional responses to favorite and relaxing music predict music-induced hypoalgesia
Bibliographic record
Abstract
Introduction The hypoalgesic effect of music has long been established. However, the characteristics of music which are important for reducing pain have not been well-studied. Some research has compared subject-selected preferred music to unfamiliar music selected by researchers, and has typically found a superior effect from preferred music. In this study, we sought to discover what aspects of listeners' relationship with their preferred music was important in producing a hypoalgesic effect. Methods We conducted a thermal pain and music listening experiment with 63 participants (14 male, 49 female, mean age = 21.3), in which music excerpts were paired with thermal stimulations. Pain ratings of intensity and unpleasantness, as well as emotional response variables, were rated on visual analog scales. We also conducted brief structured interviews about participants' favorite music, on which we conducted thematic content analysis. Themes and emotion variables were analyzed for their effects on pain ratings. Results We first replicated the finding that favorite music outperforms experimenter-selected relaxing music in reducing pain unpleasantness (MD = −7.25, p < 0.001) and that the difference in hypoalgesia was partially mediated by an increase in musical chills (ab = −2.83, p < 0.01). We then conducted a theme analysis on the interview transcripts and produced four themes relating to emotional experience: moving/bittersweet, calming/relaxing, happy/cheerful, and energizing/activating. We found suggestive evidence that moving/bittersweet favorite music reduces pain unpleasantness through increased music pleasantness (ab = −5.48, p < 0.001) and more musical chills (ab = −0.57, p = 0.004). Discussion We find that music pleasantness and musical chills are salient predictors of music-induced hypoalgesia, and that different categories of favorite music derived from qualitative analysis may engage these emotional pathways to different degrees.
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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 | 0.006 |
| 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) | 0.008 | 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".