Adaptive control of vowel formant frequency: Evidence from real-time formant manipulation
Bibliographic record
Abstract
Auditory feedback during speech production is known to play a role in speech sound acquisition and is also important for the maintenance of accurate articulation. In two studies the first formant (F1) of monosyllabic consonant-vowel-consonant words (CVCs) was shifted electronically and fed back to the participant very quickly so that participants perceived the modified speech as their own productions. When feedback was shifted up (experiment 1 and 2) or down (experiment 1) participants compensated by producing F1 in the opposite frequency direction from baseline. The threshold size of manipulation that initiated a compensation in F1 was usually greater than 60 Hz. When normal feedback was returned, F1 did not return immediately to baseline but showed an exponential deadaptation pattern. Experiment 1 showed that this effect was not influenced by the direction of the F1 shift, with both raising and lowering of F1 exhibiting the same effects. Experiment 2 showed that manipulating the number of trials that F1 was held at the maximum shift in frequency (0, 15, 45 trials) did not influence the recovery from adaptation. There was a correlation between the lag-one autocorrelation of trial-to-trial changes in F1 in the baseline recordings and the magnitude of compensation. Some participants therefore appeared to more actively stabilize their productions from trial-to-trial. The results provide insight into the perceptual control of speech and the representations that govern sensorimotor coordination.
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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 | 0.001 | 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 | 0.001 |
| 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".