Adaptation to pitch-altered feedback is independent of one’s own voice pitch sensitivity
Bibliographic record
Abstract
Monitoring voice pitch is a fine-tuned process in daily conversations as conveying accurately the linguistic and affective cues in a given utterance depends on the precise control of phonation and intonation. This monitoring is thought to depend on whether the error is treated as self-generated or externally-generated, resulting in either a correction or inflation of errors. The present study reports on two separate paradigms of adaptation to altered feedback to explore whether participants could behave in a more cohesive manner once the error is of comparable size perceptually. The vocal behavior of normal-hearing and fluent speakers was recorded in response to a personalized size of pitch shift versus a non-specific size, one semitone. The personalized size of shift was determined based on the just-noticeable difference in fundamental frequency (F0) of each participant's voice. Here we show that both tasks successfully demonstrated opposing responses to a constant and predictable F0 perturbation (on from the production onset) but these effects barely carried over once the feedback was back to normal, depicting a pattern that bears some resemblance to compensatory responses. Experiencing a F0 shift that is perceived as self-generated (because it was precisely just-noticeable) is not enough to force speakers to behave more consistently and more homogeneously in an opposing manner. On the contrary, our results suggest that the type of the response as well as the magnitude of the response do not depend in any trivial way on the sensitivity of participants to their own voice pitch. Based on this finding, we speculate that error correction could possibly occur even with a bionic ear, typically even when F0 cues are too subtle for cochlear implant users to detect accurately.
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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 | 0.000 |
| 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".