Integration of auditory and somatosensory error signals in the neural control of speech movements
Bibliographic record
Abstract
We investigated auditory and somatosensory feedback contributions to the neural control of speech. In task I, sensorimotor adaptation was studied by perturbing one of these sensory modalities or both modalities simultaneously. The first formant (F1) frequency in the auditory feedback was shifted up by a real-time processor and/or the extent of jaw opening was increased or decreased with a force field applied by a robotic device. All eight subjects lowered F1 to compensate for the up-shifted F1 in the feedback signal regardless of whether or not the jaw was perturbed. Adaptive changes in subjects' acoustic output resulted from adjustments in articulatory movements of the jaw or tongue. Adaptation in jaw opening extent in response to the mechanical perturbation occurred only when no auditory feedback perturbation was applied or when the direction of adaptation to the force was compatible with the direction of adaptation to a simultaneous acoustic perturbation. In tasks II and III, subjects' auditory and somatosensory precision and accuracy were estimated. Correlation analyses showed that the relationships 1) between F1 adaptation extent and auditory acuity for F1 and 2) between jaw position adaptation extent and somatosensory acuity for jaw position were weak and statistically not significant. Taken together, the combined findings from this work suggest that, in speech production, sensorimotor adaptation updates the underlying control mechanisms in such a way that the planning of vowel-related articulatory movements takes into account a complex integration of error signals from previous trials but likely with a dominant role for the auditory modality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".