Can perceptual training alter the effect of visual biofeedback in speech-motor learning?
Bibliographic record
Abstract
Recent work showing that a period of perceptual training can modulate the magnitude of speech-motor learning in a perturbed auditory feedback task could inform clinical interventions or second-language training strategies. The present study investigated the influence of perceptual training on a clinically and pedagogically relevant task of vocally matching a visually presented speech target using visual-acoustic biofeedback. Forty female adults aged 18-35 yr received perceptual training targeting the English /æ-ɛ/ contrast, randomly assigned to a condition that shifted the perceptual boundary toward either /æ/ or /ɛ/. Participants were then asked to produce the word head while modifying their output to match a visually presented acoustic target corresponding with a slightly higher first formant (F1, closer to /æ/). By analogy to findings from previous research, it was predicted that individuals whose boundary was shifted toward /æ/ would also show a greater magnitude of change in the visual biofeedback task. After perceptual training, the groups showed the predicted difference in perceptual boundary location, but they did not differ in their performance on the biofeedback matching task. It is proposed that the explicit versus implicit nature of the tasks used might account for the difference between this study and previous findings.
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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) | 0.002 | 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".