Neural Representations of Beat and Rhythm in Motor and Association Regions
Bibliographic record
Abstract
Abstract Humans perceive a pulse, or beat, underlying musical rhythm. Beat strength correlates with activity in the basal ganglia and SMA, suggesting these regions support beat perception. However, the basal ganglia and SMA make up a general timing network active during rhythm and timing perception, regardless of beat. Therefore, activity in these regions may represent basic rhythmic features, in addition to beat. Using RSA, we characterized the neural representation of rhythm in the basal ganglia, SMA, and across the whole brain. During fMRI, participants heard 12 rhythms – 4 strong-beat, 4 weak-beat, and 4 non-beat. Multi-voxel activity patterns for each rhythm, and for the mean of each condition, were tested for uniqueness. Activity patterns in beat-sensitive regions should alter as a function of beat strength, eliciting greater dissimilarities between rhythms with different beat strength than between rhythms with similar beat strength. Indeed, mean activity patterns in the putamen and SMA were significantly dissimilar for strong-beat and non-beat conditions, and dissimilarity between activity patterns across all 12 rhythms correlated with beat strength models, not basic rhythmic features. Whole-brain analyses also identified beat-sensitivity in the IFG, and inferior parietal cortex. These findings build upon univariate work suggesting that motor and association regions are beat-sensitive.
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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 | 0.001 | 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".