Individual variability in auditory feedback processing: Responses to real-time formant perturbations and their relation to perceptual acuity
Bibliographic record
Abstract
In this study, both between-subject and within-subject variability in speech perception and speech production were examined in the same set of speakers. Perceptual acuity was determined using an ABX auditory discrimination task, whereby speakers made judgments between pairs of syllables on a /ɛ/ to /æ/ acoustic continuum. Auditory feedback perturbations of the first two formants were implemented in a production task to obtain measures of compensation, normal speech production variability, and vowel spacing. Speakers repeated the word "head" 120 times under varying feedback conditions, with the final Hold phase involving the strongest perturbations of +240 Hz in F1 and -300 Hz in F2. Multiple regression analyses were conducted to determine whether individual differences in compensatory behavior in the Hold phase could be predicted by perceptual acuity, speech production variability, and vowel spacing. Perceptual acuity significantly predicted formant changes in F1, but not in F2. These results are discussed in consideration of the importance of using larger sample sizes in the field and developing new methods to explore feedback processing at the individual participant level. The potential positive role of variability in speech motor control is also considered.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".