Sensorimotor integration for speech motor learning involves the inferior parietal cortex
Bibliographic record
Abstract
Sensorimotor integration is important for motor learning. The inferior parietal lobe, through its connections with the frontal lobe and cerebellum, has been associated with multisensory integration and sensorimotor adaptation for motor behaviors other than speech. In the present study, the contribution of the inferior parietal cortex to speech motor learning was evaluated using repetitive transcranial magnetic stimulation (rTMS) prior to a speech motor adaptation task. Subjects' auditory feedback was altered in a manner consistent with the auditory consequences of an unintended change in tongue position during speech production, and adaptation performance was used to evaluate sensorimotor plasticity and short-term learning. Prior to the feedback alteration, rTMS or sham stimulation was applied over the left supramarginal gyrus (SMG). Subjects who underwent the sham stimulation exhibited a robust adaptive response to the feedback alteration whereas subjects who underwent rTMS exhibited a diminished adaptive response. The results suggest that the inferior parietal region, in and around SMG, plays a role in sensorimotor adaptation for speech. The interconnections of the inferior parietal cortex with inferior frontal cortex, cerebellum and primary sensory areas suggest that this region may be an important component in learning and adapting sensorimotor patterns for speech.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".