Empathy and Emotional Contagion as a Link Between Recognized and Felt Emotions in Music Listening
Bibliographic record
Abstract
Previous studies have shown that there is a difference between recognized and induced emotion in music listening. In this study, empathy is tested as a possible moderator between recognition and induction that is, on its own, moderated via music preference evaluations and other individual and situational features. Preference was also tested to determine whether it had an effect on measures of emotion independently from emotional expression. A web-based experiment gathered from 3,164 music listeners emotion, empathy, and preference ratings in a between-subjects design embedded in a music-personality test. Stimuli were a sample of 23 musical excerpts (each 30 seconds long, five randomly assigned to each participant) from various musical styles chosen to represent different emotions and preferences. Listeners in the recognition rating condition rated measures of valence and arousal significantly differently than listeners in the felt rating condition. Empathy ratings were shown to modulate this relationship: when empathy was present, the difference between the two rating types was reduced. Furthermore, we confirmed preference as one major predictor of empathy ratings. Emotional contagion was tested and confirmed as an additional direct effect of emotional expression on induced emotions. This study is among the first to explicitly test empathy and emotional contagion during music listening, helping to explain the often-reported emotional response to music in everyday life.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".