Temporal coordination and adaptation to rate change in music performance.
Bibliographic record
Abstract
People often coordinate their actions with sequences that exhibit temporal variability and unfold at multiple periodicities. We compared oscillator- and timekeeper-based accounts of temporal coordination by examining musicians' coordination of rhythmic musical sequences with a metronome that gradually changed rate at the end of a musical phrase (Experiment 1) or at the beginning of a phrase (Experiment 2). The rhythms contained events that occurred at the same periodic rate as the metronome and at half the period. Rate change consisted of a linear increase or decrease in intervals between metronome onsets. Musicians coordinated their performances better with a metronome that decreased than increased in tempo (as predicted by an oscillator model), at both beginnings and ends of musical phrases. Model performance was tested with an oscillator period or timekeeper interval set to the same period as the metronome (1:1 coordination) or half the metronome period (2:1 coordination). Only the oscillator model was able to predict musicians' coordination at both periods. These findings suggest that coordination is based on internal neural oscillations that entrain to external sequences.
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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 | 0.010 |
| 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 | 0.001 |
| 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".