Bibliographic record
Abstract
Interactions between auditory and somatosensory information are relevant to the neural processing of speech since speech processes and certainly speech production involves both auditory information and inputs that arise from the muscles and tissues of the vocal tract. We previously demonstrated that somatosensory inputs associated with facial skin deformation alter the perceptual processing of speech sounds. We show here that the reverse is also true, that speech sounds alter the perception of facial somatosensory inputs. As a somatosensory task, we used a robotic device to create patterns of facial skin deformation that would normally accompany speech production. We found that the perception of the facial skin deformation was altered by speech sounds in a manner that reflects the way in which auditory and somatosensory effects are linked in speech production. The modulation of orofacial somatosensory processing by auditory inputs was specific to speech and likewise to facial skin deformation. Somatosensory judgments were not affected when the skin deformation was delivered to the forearm or palm or when the facial skin deformation accompanied nonspeech sounds. The perceptual modulation that we observed in conjunction with speech sounds shows that speech sounds specifically affect neural processing in the facial somatosensory system and suggest the involvement of the somatosensory system in both the production and perceptual processing of 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".