Pitch biases sensorimotor synchronization to auditory rhythms
Bibliographic record
Abstract
Current models of rhythm perception propose that humans track musical beats using the phase, period, and amplitude of sound patterns. However, a growing body of evidence suggests that pitch can also influence the perceived timing of auditory signals. In the present study, we conducted two experiments to investigate whether pitch also affects the tempo and asynchrony of sensorimotor synchronization. To do so, we asked participants to synchronize with a repeating tone, whose pitch on each trial was drawn from one of six different octaves (110–3520 Hz). In Experiment 1, we observed U-shaped patterns in both mean asynchrony and continuation tapping rates, with participants tapping latest and slowest when synchronizing to low and extremely high (above 2000 Hz) pitches, and tapping earliest and fastest to moderately high pitches. In Experiment 2, we found that extremely high pitches still produced slower timing than moderately high pitches when participants were exposed to an exclusively high-pitched context. We advocate for the incorporation of pitch into models of rhythm perception, and we discuss the possibility that there may exist two pitch-based influences on perceived tempo: a learned correlation between higher pitches and faster timing, and a U-shaped effect of stimulus frequency on neural dynamics.
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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 | 0.012 |
| 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 | 0.000 |
| 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".