Left Lateralized Enhancement of Orofacial Somatosensory Processing Due to Speech Sounds
Bibliographic record
Abstract
PURPOSE: Somatosensory information associated with speech articulatory movements affects the perception of speech sounds and vice versa, suggesting an intimate linkage between speech production and perception systems. However, it is unclear which cortical processes are involved in the interaction between speech sounds and orofacial somatosensory inputs. The authors examined whether speech sounds modify orofacial somatosensory cortical potentials that were elicited using facial skin perturbations. METHOD: Somatosensory event-related potentials in EEG were recorded in 3 background sound conditions (pink noise, speech sounds, and nonspeech sounds) and also in a silent condition. Facial skin deformations that are similar in timing and duration to those experienced in speech production were used for somatosensory stimulation. RESULTS: The authors found that speech sounds reliably enhanced the first negative peak of the somatosensory event-related potential when compared with the other 3 sound conditions. The enhancement was evident at electrode locations above the left motor and premotor area of the orofacial system. The result indicates that speech sounds interact with somatosensory cortical processes that are produced by speech-production-like patterns of facial skin stretch. CONCLUSION: Neural circuits in the left hemisphere, presumably in left motor and premotor cortex, may play a prominent role in the interaction between auditory inputs and speech-relevant somatosensory processing.
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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) | 0.006 | 0.001 |
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".