Adaptive Coding of Orofacial and Speech Actions in Motor and Somatosensory Spaces with and without Overt Motor Behavior
Bibliographic record
Abstract
Studies of speech motor control suggest that articulatory and phonemic goals are defined in multidimensional motor, somatosensory, and auditory spaces. To test whether motor simulation might rely on sensory-motor coding common with those for motor execution, we used a repetition suppression (RS) paradigm while measuring neural activity with sparse sampling fMRI during repeated overt and covert orofacial and speech actions. RS refers to the phenomenon that repeated stimuli or motor acts lead to decreased activity in specific neural populations and are associated with enhanced adaptive learning related to the repeated stimulus attributes. Common suppressed neural responses were observed in motor and posterior parietal regions in the achievement of both repeated overt and covert orofacial and speech actions, including the left premotor cortex and inferior frontal gyrus, the superior parietal cortex and adjacent intraprietal sulcus, and the left IC and the SMA. Interestingly, reduced activity of the auditory cortex was observed during overt but not covert speech production, a finding likely reflecting a motor rather an auditory imagery strategy by the participants. By providing evidence for adaptive changes in premotor and associative somatosensory brain areas, the observed RS suggests online state coding of both orofacial and speech actions in somatosensory and motor spaces with and without motor behavior and sensory feedback.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".