Perceptual recalibration of speech sounds following speech motor learning
Bibliographic record
Abstract
The functional sensorimotor nature of speech production has been demonstrated in studies examining speech adaptation to auditory and/or somatosensory feedback manipulations. These studies have focused primarily on flexible motor processes to explain their findings, without considering modifications to sensory representations resulting from the adaptation process. The present study explores whether the perceptual representation of the /s-/ contrast may be adjusted following the alteration of auditory feedback during the production of /s/-initial words. Consistent with prior studies of speech adaptation, talkers exposed to the feedback manipulation were found to adapt their motor plans for /s/-production in order to compensate for the effects of the sensory perturbation. In addition, a shift in the /s-/ category boundary was observed that reduced the functional impact of the auditory feedback manipulation by increasing the perceptual "distance" between the category boundary and subjects' altered /s/-stimuli-a pattern of perceptual adaptation that was not observed in two separate control groups. These results suggest that speech adaptation to altered auditory feedback is not limited to the motor domain, but rather involves changes in both motor output and auditory representations of speech sounds that together act to reduce the impact of the perturbation.
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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 | 0.004 |
| 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 | 0.001 |
| 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".