Emotional valence contributes to music-induced analgesia
Bibliographic record
Abstract
The capacity of music to soothe pain has been used in many traditional forms of medicine. Yet, the mechanisms underlying these effects have not been demonstrated. Here, we examine the possibility that the modulatory effect of music on pain is mediated by the valence (pleasant-unpleasant dimension) of the emotions induced. We report the effects of listening to pleasant and unpleasant music on thermal pain in healthy human volunteers. Eighteen participants evaluated the warmth or pain induced by 40.0, 45.5, 47.0 and 48.5 degrees C thermal stimulations applied to the skin of their forearm while listening to pleasant and unpleasant musical excerpts matched for their high level of arousal (relaxing-stimulating dimension). Compared to a silent control condition, only the pleasant excerpts produced highly significant reductions in both pain intensity and unpleasantness, demonstrating the effect of positive emotions induced by music on pain (Pairwise contrasts with silence: p's<0.001). Correlation analyses in the pleasant music condition further indicated that pain decreased significantly (p's<0.05) with increases in self-reports of music pleasantness. In contrast, the unpleasant excerpts did not modulate pain significantly, and warmth perception was not affected by the presence of pleasant or unpleasant music. Those results support the hypothesis that positive emotional valence contributes to music-induced analgesia. These findings call for the integration of music to current methods of pain control.
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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 | 0.001 |
| 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".