Creating a shared musical interpretation: Changes in coordination dynamics while learning unfamiliar music together
Bibliographic record
Abstract
The ability to coordinate with others is fundamental for humans to achieve shared goals. Often, harmonious interpersonal coordination requires learning, such as ensemble musicians rehearing together to synchronize their low-level timing and high-level aesthetic musical expressions. We investigated how the coordination dynamics of a professional string quartet changed as they learned unfamiliar pieces together across eight trials. During all trials, we recorded each musician's body sway motion data, and quantified the group's body sway similarity (cross-correlation) and information flow (Granger causality) on each trial. In line with our hypothesis, group similarity increased, while group information flow decreased significantly across trials. In addition, there was a trend such that group similarity, but not information flow, was related to the quality of the performances. As the ensemble converged on a joint interpretation through rehearsing, their body sways reflected the change from interpersonal information flow for coordinative mutual adaptations and corrections, to synchronous musical coordination made possible by the musicians learning a common internally based expressive interpretation.
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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 | 0.008 |
| 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 | 0.001 |
| 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".