Finding and Keeping the Beat: Neural Mechanisms Differ as Beat Perception Unfolds
Bibliographic record
Abstract
Abstract Perception of a regular beat is essential to our ability to synchronize movements to music in an anticipatory fashion. Beat perception requires multiple, distinct neural functions, corresponding to the perceptual stages that occur over time, including 1) detection that regularity is present ( beat finding ), 2) prediction of future regular events to enable anticipation ( beat continuation ), and 3) dynamic adjustment of predictions as the rhythmic stimulus changes ( beat adjustment ). The striatum has been shown to be crucial for beat perception generally, although it is unclear how, or whether, distinct regions of the striatum contribute to these different stages of beat perception. Here, we used fMRI to investigate the activity of striatal subregions during the different stages of beat perception. Participants listened to pairs of rhythms (polyrhythms) whose temporal structure induced distinct perceptual stages— finding, continuation , and adjustment of the beat. Dorsal putamen was preferentially active during beat finding , whereas the ventral putamen was preferentially active during beat adjustment . We also observed that anterior insula activity was sensitive to metrical structure (greater when polyrhythms were metrically incongruent than when they were congruent). These data implicate the dorsal putamen in the detection of regularity, possibly by detection of coincidences between cortical oscillations, and the ventral putamen in the adjustment of regularity perception, possibly by integration of prediction errors in ongoing beat predictions. Additionally, activity in the supramarginal and superior temporal gyri correlated with beat tapping performance, and activity in the superior temporal gyrus correlated with beat perception (performance on the Beat Alignment Test).
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 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".