The right sound at the right time: Cerebellar and ventral striatal involvement in imitating pitch and timing
Bibliographic record
Abstract
Acting on the world requires the right action at the right time. This is perhaps most easily seen in music where the meaning of a piece is encoded explicitly by both the pitches of musical notes and their duration. We used these features to operationalise the production of desired qualia of movements as note pitches as compared to movement timing as note durations. Participants listened to and imitated simple melodies as accurately as possible while lying in an ultra-high field 7T MRI scanner. Melodies consisted of either a series of different pitches of equal duration or a single pitch repeated at different durations. Both tasks engaged a broad motor network similar to speech and other complex dynamic movements. However, imitation for timing preferentially activated the ventral striatum of the basal ganglia while imitation for pitch preferentially activated lobule VI of the cerebellum and temporal lobe auditory association areas. These findings are consistent with the role of the basal ganglia in sound sequence learning and with the role of the cerebellum in refining movement based on sensory feedback. Imitating melodies provides a simple but effective framework for manipulating the qualities and timings of sound production by the speech-motor system, even when no words are spoken. • Imitation by singing or whistling is an effective probe of the “speech” motor system. • Singing and whistling are complementary probes for distinct subsets of the speech musculature. • Imitating stimuli with complex pitch patterns increases activation of the cerebellum (lobule VI). • Imitating stimuli with complex timing patterns increases activation of the basal ganglia (ventral striatum).
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".