Modulation of auditory-motor learning in response to formant perturbation as a function of delayed auditory feedback
Bibliographic record
Abstract
The interaction of language production and perception has been substantiated by empirical studies where speakers compensate their speech articulation in response to the manipulated sound of their voice heard in real-time as auditory feedback. A recent study by Max and Maffett [(2015). Neurosci. Lett. 591, 25-29] reported an absence of compensation (i.e., auditory-motor learning) for frequency-shifted formants when auditory feedback was delayed by 100 ms. In the present study, the effect of auditory feedback delay was studied when only the first formant was manipulated while delaying auditory feedback systematically. In experiment 1, a small yet significant compensation was observed even with 100 ms of auditory delay unlike the past report. This result suggests that the tolerance of feedback delay depends on different types of auditory errors being processed. In experiment 2, it was revealed that the amount of formant compensation had an inverse linear relationship with the amount of auditory delay. One of the speculated mechanisms to account for these results is that as auditory delay increases, undelayed (and unperturbed) somatosensory feedback is given more preference for accuracy control of vowel formants.
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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) | 0.001 | 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 | 0.001 |
| 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".