Virtual Partners Improve Synchronization in Human−Machine Trios
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it