Speech motor learning alters auditory and somatosensory event-related potentials
Bibliographic record
Abstract
Speech motor learning is dependent upon changes to motor function, but it also results in changes to sensory systems. However, the neural mechanisms of sensory plasticity associated with speech motor learning are little understood. We here examined whether auditory and somatosensory cortical processes are changed in conjunction with speech motor learning. We tested native speakers of American English. Altered auditory feedback (AAF) training was used as a motor learning task. As subjects repeated aloud the speech utterance “head,” the produced sound was feedbacked through headphones while the first formant of /ea/ was gradually decreased over 50 repetitions and held at a maximum change for 110 repetitions. In order to evaluate the effects of the resulting adaptation on cortical sensory processes, we recorded auditory and somatosensory event-related potentials (ERPs) using 64-channel electroencephalography before and after AAF training. Auditory ERPs were elicited by using the synthesized vowel sound “e.” Somatosensory ERPs were elicited by facial skin deformation. We found changes to auditory and somatosensory ERPs following AAF training in individuals who showed adaptation to altered auditory feedback. The changes in ERPs were correlated with the amount of adaptation. This suggests that speech motor learning alters somatosensory and auditory cortical 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 | 0.000 |
| 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".