Imagining the beat: causal evidence for dorsal premotor cortex (dPMC) role in beat imagery via transcranial magnetic stimulation (TMS)
Bibliographic record
Abstract
The ability to internally generate and maintain a rhythmic pulse, i.e., beat imagery, is a fundamental aspect of musical cognition. While recent theories propose that premotor regions support internal temporal predictions during rhythm perception and imagery, direct causal evidence remains limited. In this study, we investigated the specific contributions of the dorsal premotor cortex (dPMC) and the supplementary motor area (SMA) to beat imagery using transcranial magnetic stimulation (TMS). Forty-two non-musicians listened to rhythmic musical excerpts and judged whether a probe tone, presented after a short silent period, was temporally aligned with the imagined beat. TMS (three pulses at 10 Hz) was delivered over dPMC, SMA, or a sham control site (coil tilted 90° over M1) immediately before the imagery phase. Participants also completed the Bucknell Auditory Imagery Scale (BAIS) to assess individual differences in auditory imagery abilities. Results showed that TMS over the dPMC significantly modulated beat imagery performance, particularly in individuals with lower auditory imagery scores. No effects were observed following SMA stimulation. These findings provide causal evidence for the involvement of the dPMC in the endogenous generation of rhythmic structure and suggest a functional dissociation between motor-related areas in beat-based timing. Moreover, the interaction between stimulation effects and individual imagery abilities indicates that the neural response to TMS is shaped by individual functional states. Collectively, these results highlight the flexible and context-dependent nature of rhythm imagery mechanisms and support a predictive role for the dPMC, and more broadly, the dorsal auditory stream, in internally guided beat processing.
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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".