Audio-visual concert performances synchronize an audience’s heart rates
Bibliographic record
Abstract
Abstract People enjoy engaging with music. Live music concerts provide an excellent option to investigate real-world music experiences, and at the same time, use neurophysiological synchrony to assess dynamic engagement. In the current study, we assessed engagement in a live concert setting using synchrony of cardiorespiratory measures, comparing inter-subject, stimulus-response, correlation, and phase coherence. As engagement might be enhanced in a concert setting by seeing musicians perform, we presented audiences with audio-only (AO) and audio-visual (AV) piano performances. Only correlation measures were above chance level. In comparing time-averaged synchrony across conditions, AV performances evoked higher inter-subject correlation of heart rate (ISC-HR). However, self-reported engagement did not correspond to synchrony when averaged across music pieces. On the other hand, time-resolved analyses show that synchronized deceleration-acceleration heart rate (HR) patterns, typical of an ‘orienting response’ (an index of directed attention), occurred within music pieces at salient events of section boundaries. That is, seeing musicians perform heightened audience engagement at structurally important moments in Western classical music. Overall, we could show that multisensory information shapes dynamic engagement. By comparing different synchrony measures, we further highlight the advantages of timeseries analysis, specifically ISC-HR, as a robust measure of holistic musical listening experiences in naturalistic concert settings.
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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) | 0.004 | 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".