The Neural Correlates of Speech Motor Sequence Learning
Bibliographic record
Abstract
Speech is perhaps the most sophisticated example of a species-wide movement capability in the animal kingdom, requiring split-second sequencing of approximately 100 muscles in the respiratory, laryngeal, and oral movement systems. Despite the unique role speech plays in human interaction and the debilitating impact of its disruption, little is known about the neural mechanisms underlying speech motor learning. Here, we studied the behavioral and neural correlates of learning new speech motor sequences. Participants repeatedly produced novel, meaningless syllables comprising illegal consonant clusters (e.g., GVAZF) over 2 days of practice. Following practice, participants produced the sequences with fewer errors and shorter durations, indicative of motor learning. Using fMRI, we compared brain activity during production of the learned illegal sequences and novel illegal sequences. Greater activity was noted during production of novel sequences in brain regions linked to non-speech motor sequence learning, including the BG and pre-SMA. Activity during novel sequence production was also greater in brain regions associated with learning and maintaining speech motor programs, including lateral premotor cortex, frontal operculum, and posterior superior temporal cortex. Measures of learning success correlated positively with activity in left frontal operculum and white matter integrity under left posterior superior temporal sulcus. These findings indicate speech motor sequence learning relies not only on brain areas involved generally in motor sequencing learning but also those associated with feedback-based speech motor learning. Furthermore, learning success is modulated by the integrity of structural connectivity between these motor and sensory brain regions.
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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 | 0.001 | 0.024 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".