A sensorimotor approach to disfluency adaptation in typically fluent adults
Bibliographic record
Abstract
Delaying auditory feedback (DAF) during speech is a potent auditory perturbation that can interrupt and prolong syllables and words, even though these disfluencies rarely affect typical speakers under non-altered feedback (NAF). Adaptation or compensation to auditory perturbations has been shown in typical speakers through a gradual reduction in the amplitude of formant shift responses. However, despite considerable research on DAF, it is still not known whether typical speakers adapt to DAF. In this study, we tested whether a comparable form of adaptation occurs with DAF in typical speakers as shown by a reduction in altered feedback disfluencies (AFD) during repeated consecutive readings, after a pause between readings and to a novel reading. We then tested for carryover effects after a single DAF exposure. A significant decrease in AFD rate was observed in 38 speakers that was sustained after a pause and for a novel reading. The adaptation effect extended to articulation rate (syllables/sec) in that rate increased for all speakers across readings. Evidence for carryover effects was inconclusive. By showing that typical speakers can adapt to DAF, the findings support a sensorimotor approach to auditory perturbation adaptation achieved through motor practice.
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 | 0.002 |
| 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".