Interacting Cortical and Basal Ganglia Networks Underlying Finding and Tapping to the Musical Beat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it