Adaptation to a physical alteration of the vocal apparatus: The effect of visual self-perception on speech motor plasticity
Bibliographic record
Abstract
Adapting speech movements to novel or perturbed conditions relies critically on the processing of self-produced sensory information. In the context of a physical alteration of the vocal apparatus (e.g., palatal prosthesis), talkers have been shown to adapt following 10–15 min of practice to produce improved acoustic output. It is possible that additional information, such as ultrasound imaging of the tongue, may help talkers adapt even more effectively to such perturbations. Providing visual feedback of the tongue surface in real-time has shown promise in the treatment of speech-sound disorders. However, it remains unclear whether the addition of such visual information will influence speech plasticity on the timescale examined in experimental studies of speech adaptation. Here, we examine how neurotypical talkers adapt to a palatal prosthesis, relying on auditory and somatosensory feedback alone (n = 15), or with the addition of ultrasound feedback of the tongue, either in the mid-sagittal (n = 15) or coronal plane (n = 15). Differences in adaptation performance between the three feedback conditions were observed, both in the patterns of speech adaptation and the learning after-effects. The results indicate that talkers will rapidly integrate visual articulatory information into their control of oral speech movements in order to guide productions towards improved acoustic outcomes.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".