Repetition Suppression in Auditory–Motor Regions to Pitch and Temporal Structure in Music
Bibliographic record
Abstract
Music performance requires control of two sequential structures: the ordering of pitches and the temporal intervals between successive pitches. Whether pitch and temporal structures are processed as separate or integrated features remains unclear. A repetition suppression paradigm compared neural and behavioral correlates of mapping pitch sequences and temporal sequences to motor movements in music performance. Fourteen pianists listened to and performed novel melodies on an MR-compatible piano keyboard during fMRI scanning. The pitch or temporal patterns in the melodies either changed or repeated (remained the same) across consecutive trials. We expected decreased neural response to the patterns (pitch or temporal) that repeated across trials relative to patterns that changed. Pitch and temporal accuracy were high, and pitch accuracy improved when either pitch or temporal sequences repeated over trials. Repetition of either pitch or temporal sequences was associated with linear BOLD decrease in frontal-parietal brain regions including dorsal and ventral premotor cortex, pre-SMA, and superior parietal cortex. Pitch sequence repetition (in contrast to temporal sequence repetition) was associated with linear BOLD decrease in the intraparietal sulcus (IPS) while pianists listened to melodies they were about to perform. Decreased BOLD response in IPS also predicted increase in pitch accuracy only when pitch sequences repeated. Thus, behavioral performance and neural response in sensorimotor mapping networks were sensitive to both pitch and temporal structure, suggesting that pitch and temporal structure are largely integrated in auditory-motor transformations. IPS may be involved in transforming pitch sequences into spatial coordinates for accurate piano performance.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".