Electropalatographic, acoustic, and perceptual data on adaptation to a palatal perturbation
Bibliographic record
Abstract
Exploring the compensatory responses of the speech production system to perturbation has provided valuable insights into speech motor control. The present experiment was conducted to examine compensation for one such perturbation-a palatal perturbation in the production of the fricative /s/. Subjects wore a specially designed electropalatographic (EPG) appliance with a buildup of acrylic over the alveolar ridge as well as a normal EPG palate. In this way, compensatory tongue positioning could be assessed during a period of target specific and intense practice and compared to nonperturbed conditions. Electropalatographic, acoustic, and perceptual analyses of productions of /asa/ elicited from nine speakers over the course of a one-hour practice period were conducted. Acoustic and perceptual results confirmed earlier findings, which showed improvement in production with a thick artificial palate in place over the practice period; the EPG data showed overall increased maximum contact as well as increased medial and posterior contact for speakers with the thick palate in place, but little change over time. Negative aftereffects were observed in the productions with the thin palate, indicating recalibration of sensorimotor processes in the face of the oral-articulatory perturbation. Findings are discussed with regard to the nature of adaptive articulatory skills.
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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 | 0.002 |
| 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".