Body sway reflects joint emotional expression in music ensemble performance
Bibliographic record
Abstract
Joint action is essential in daily life, as humans often must coordinate with others to accomplish shared goals. Previous studies have mainly focused on sensorimotor aspects of joint action, with measurements reflecting event-to-event precision of interpersonal sensorimotor coordination (e.g., tapping). However, while emotional factors are often closely tied to joint actions, they are rarely studied, as event-to-event measurements are insufficient to capture higher-order aspects of joint action such as emotional expression. To quantify joint emotional expression, we used motion capture to simultaneously measure the body sway of each musician in a trio (piano, violin, cello) during performances. Excerpts were performed with or without emotional expression. Granger causality was used to analyze body sway movement time series amongst musicians, which reflects information flow. Results showed that the total Granger-coupling of body sway in the ensemble was higher when performing pieces with emotional expression than without. Granger-coupling further correlated with the emotional intensity as rated by both the ensemble members themselves and by musician judges, based on the audio recordings alone. Together, our findings suggest that Granger-coupling of co-actors' body sways reflects joint emotional expression in a music ensemble, and thus provide a novel approach to studying joint emotional expression.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".