Virtual Partners Improve Synchronization in Human−Machine Trios
Bibliographic record
Abstract
The interplay between auditory and motor processes in sensorimotor synchronization is crucial for achieving a cohesive group performance, particularly in musical groups. This study addressed the impact of virtual partners on synchronization performance in human trios. With a novel methodology, the study utilized virtual partners driven by computational models to simulate real-time synchronization with human participants. Trio synchronization with three synchronization models was compared: linear error-correction, Kuramoto oscillators, and delay-coupled oscillators. Forty-eight musically trained adults performed synchronization tasks in both in-phase and anti-phase rhythms with either a human confederate or one of the three computational models as the third partner, forming 24 trios. Synchronization stability and accuracy were significantly enhanced in trios that contained a virtual partner compared to those with a human confederate. Model optimizations revealed a stronger coupling of participants with each other than with virtual partners for in-phase rhythms, and a stronger coupling of virtual partners with participants than of participants with each other in anti-phase rhythms; these patterns were obtained for the oscillator models but not for the linear model. Additionally, participants reported higher perceived synchronization success, greater control over performance, and stronger social relationships with virtual partners than with the human confederate. These findings highlight the potential of virtual partners for improving synchronization and suggest avenues for further research in the use of adaptive agents in group performance 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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".