Interacting Cortical and Basal Ganglia Networks Underlying Finding and Tapping to the Musical Beat
Bibliographic record
Abstract
Humans are able to find and tap to the beat of musical rhythms varying in complexity from children's songs to modern jazz. Musical beat has no one-to-one relationship with auditory features-it is an abstract perceptual representation that emerges from the interaction between sensory cues and higher-level cognitive organization. Previous investigations have examined the neural basis of beat processing but have not tested the core phenomenon of finding and tapping to the musical beat. To test this, we used fMRI and had musicians find and tap to the beat of rhythms that varied from metrically simple to metrically complex-thus from a strong to a weak beat. Unlike most previous studies, we measured beat tapping performance during scanning and controlled for possible effects of scanner noise on beat perception. Results showed that beat finding and tapping recruited largely overlapping brain regions, including the superior temporal gyrus (STG), premotor cortex, and ventrolateral PFC (VLPFC). Beat tapping activity in STG and VLPFC was correlated with both perception and performance, suggesting that they are important for retrieving, selecting, and maintaining the musical beat. In contrast BG activity was similar in all conditions and was not correlated with either perception or production, suggesting that it may be involved in detecting auditory temporal regularity or in associating auditory stimuli with a motor response. Importantly, functional connectivity analyses showed that these systems interact, indicating that more basic sensorimotor mechanisms instantiated in the BG work in tandem with higher-order cognitive mechanisms in PFC.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".