Bibliographic record
Abstract
Many common behaviours require people to coordinate the timing of their actions with the timing of others' actions. We examined whether representations of musicians' actions are activated in coperformers with whom they must coordinate their actions in time and whether coperformers simulate each other's actions using their own motor systems during temporal coordination. Pianists performed right-hand melodies along with simple or complex left-hand accompaniments produced by themselves or by another pianist. Individual performers' preferred performance rates were measured in solo performance of the right-hand melody. The complexity of the left-hand accompaniment influenced the temporal grouping structure of the right-hand melody in the same way when it was performed by the self or by the duet partner, providing some support for the action corepresentation hypothesis. In contrast, accompaniment complexity had little influence on temporal coordination measures (asynchronies and cross-correlations between parts). Temporal coordination measures were influenced by a priori similarities between partners' preferred rates; partners who had similar preferred rates in solo performance were better synchronized and showed mutual adaptation to each other's timing during duet performances. These findings extend previous findings of action corepresentation and action simulation to a task that requires precise temporal coordination of independent yet simultaneous actions.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".