The effects of tongue loading and auditory feedback on vowel production
Bibliographic record
Abstract
This study investigated the role of sensory feedback during the production of front vowels. A temporary aftereffect induced by tongue loading was employed to modify the somatosensory-based perception of tongue height. Following the removal of tongue loading, tongue height during vowel production was estimated by measuring the frequency of the first formant (F1) from the acoustic signal. In experiment 1, the production of front vowels following tongue loading was investigated either in the presence or absence of auditory feedback. With auditory feedback available, the tongue height of front vowels was not modified by the aftereffect of tongue loading. By contrast, speakers did not compensate for the aftereffect of tongue loading when they produced vowels in the absence of auditory feedback. In experiment 2, the characteristics of the masking noise were manipulated such that it masked energy either in the F1 region or in the region of the second and higher formants. The results showed that the adjustment of tongue height during the production of front vowels depended on information about F1 in the auditory feedback. These findings support the idea that speech goals include both auditory and somatosensory targets and that speakers are able to make use of information from both sensory modalities to maximize the accuracy of speech production.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".