Recalibration of auditory perception of speech due to orofacial somatosensory inputs during speech motor adaptation
Bibliographic record
Abstract
Speech motor control and learning rely on both somatosensory and auditory inputs. Somatosensory inputs associated with speech production can also affect the process of auditory perception of speech, and the somatosensory-auditory interaction may play a fundamental role in auditory perception of speech. In this report, we show that the somatosensory system contributes to perceptual recalibration, separate from its role in motor function. Subjects participated in speech motor adaptation to altered auditory feedback. Auditory perception of speech was assessed in phonemic identification tests before and after speech adaptation. To investigate a role of the somatosensory system in motor adaptation and subsequent perceptual change, we applied orofacial skin stretch in either a backward or forward direction during the auditory feedback alteration as a somatosensory modulation. We found that the somatosensory modulation did not affect the amount of adaptation at the end of training, although it changed the rate of adaptation. However, the perception following speech adaptation was altered depending on the direction of the somatosensory modulation. Somatosensory inflow rather than motor outflow thus drives changes to auditory perception of speech following speech adaptation, suggesting that somatosensory inputs play an important role in tuning of perceptual system. NEW & NOTEWORTHY This article reports that the somatosensory system works not equally with the motor system, but predominantly in the calibration of auditory perception of speech by speech production.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".