Neural representations of beat and rhythm in motor and association regions
Bibliographic record
Abstract
Humans perceive a pulse, or beat, underlying musical rhythm. Beat strength correlates with activity in the basal ganglia and supplementary motor area, suggesting these regions support beat perception. However, the basal ganglia and supplementary motor area are part of a general rhythm and timing network (regardless of the beat) and may also represent basic rhythmic features (e.g. tempo, number of onsets). To characterize the encoding of beat-related and other basic rhythmic features, we used representational similarity analysis. During functional magnetic resonance imaging, participants heard 12 rhythms-4 strong-beat, 4 weak-beat, and 4 nonbeat. Multi-voxel activity patterns for each rhythm were tested to determine which brain areas were beat-sensitive: those in which activity patterns showed greater dissimilarities between rhythms of different beat strength than between rhythms of similar beat strength. Indeed, putamen and supplementary motor area activity patterns were significantly dissimilar for strong-beat and nonbeat conditions. Next, we tested whether basic rhythmic features or models of beat strength (counterevidence scores) predicted activity patterns. We found again that activity pattern dissimilarity in supplementary motor area and putamen correlated with beat strength models, not basic features. Beat strength models also correlated with activity pattern dissimilarities in the inferior frontal gyrus and inferior parietal lobe, though these regions encoded beat and rhythm simultaneously and were not driven by beat alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".