Visual congruency of performers’ movements enhances vocal music reward through Mu entrainment
Bibliographic record
Abstract
There is emerging evidence that a performer's body movements may enhance music-induced pleasure. However, the neural mechanism underlying such modulation remains largely unexplored. This study utilized behavioural, psychophysiological, and electroencephalographic data collected from 32 listeners (analysed sample = 31), as they watched and listened to vocal (Mandarin lyrics) and violin performances of pop music videos. None were familiar with Mandarin, and none had significant training in string instruments. Stimuli featured either congruent or incongruent audiovisual parings within the same instrument. We found that congruent visual movements, as opposed to incongruent ones, significantly increased both subjective pleasure ratings and skin conductance responses. While Mu-band power suppression occurred in the presence of visual movements regardless of congruency; congruent movements enhanced the coherence between the music envelope and Mu-band oscillations (so-called Mu entrainment). Effect sizes for both measures were greater for vocal than violin music, though no interaction was observed. Mediation analysis demonstrated that Mu entrainment to vocal music significantly mediated the visual modulation of music-induced pleasure and that this effect occurs primarily for familiar vocal rather than unfamiliar violin movements. In conclusion, our study provides evidence that congruent visual movements enhance music pleasure by promoting Mu entrainment, potentially through sensorimotor integration mechanisms.
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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".