Investigating the Causal Role of Motor Brain Areas in Rhythm Reproduction: A Transcranial Direct Current Stimulation Study
Bibliographic record
Abstract
Abstract Humans have an intrinsic tendency to move to music. However, our understanding of the neural mechanisms underlying the music-movement connection remains limited, and most studies have used correlational methods. Here, we used transcranial direct current stimulation (tDCS) to investigate the causal role of four brain regions commonly involved in movement timing and beat perception: the supplementary motor area (SMA), left and right premotor cortices (PMC), and the right cerebellum. On three different days, subjects received anodal, cathodal, or sham stimulation while reproducing strong-beat, weak-beat, and non-beat rhythms via finger tapping. Each subject only received stimulation in one of the four brain regions. As the SMA appears to play a primary role in beat perception, while the premotor cortex and cerebellum appear to have a more general role in timing, we predicted that the SMA stimulation would affect reproduction of rhythms with a beat, whereas premotor and cerebellar stimulation would affect reproduction of sequences with no beat. As expected, reproduction accuracy depended on beat strength; strong-beat rhythms were more accurately reproduced than weak and non-beat rhythms. Unexpectedly, tDCS had no effect on reproduction accuracy in any brain region. Thus, we found no evidence that modulating brain excitability in SMA, PMC, or cerebellum altered accuracy of rhythm reproduction. We discuss the implications of these results and future perspectives for this research.
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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 | 0.001 |
| 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".