Adaptation of speech production to palatal perturbation: Kinematic analysis using electromagnetometry
Bibliographic record
Abstract
The extent to which adults are able to flexibly adapt their speech production to altered sensory-motor conditions has considerable implications for oral-motor rehabilitation, yet it remains poorly understood. Previous studies have explored the effects of different physical and perceptual perturbations on speech production, yielding mixed results with respect to the timecourse and extent of adaptation. A major limitation of these studies has been their reliance on acoustical measures as the sole index of motor adaptation. The difficulty with such an approach is the potentially complex relationship between acoustic changes and their underlying articulatory patterns. In the present study, speech adaptation to a palatal perturbation (5-mm thickened alveolar ridge) was investigated by directly measuring articulatory kinematics using electromagnetometry. In contrast to previous claims, subjects were found to produce a significant change in speech-related tongue movement immediately upon insertion and removal of the artificial palate. In addition, subjects gradually fine-tuned their tongue position with continued practice. The observed patterns are consistent with the idea of two independent adaptation processes: a rapid adaptation process that is driven by the mere presence of the false palate and a slower adaptation process driven by the continued presence of the palatal perturbation.
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 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".