Please say what this word is—Vowel-extrinsic normalization in the sensorimotor control of speech.
Bibliographic record
Abstract
The extent to which the adaptive nature of speech perception influences the acoustic targets underlying speech production is not well understood. For example, listeners can rapidly accommodate to talker-dependent phonetic properties-a process known as vowel-extrinsic normalization-without altering their speech output. Recent evidence, however, shows that reinforcement-based learning in vowel perception alters the processing of speech auditory feedback, impacting sensorimotor control during vowel production. This suggests that more automatic and ubiquitous forms of perceptual plasticity, such as those characterizing perceptual talker normalization, may also impact the sensorimotor control of speech. To test this hypothesis, we set out to examine the possible effects of vowel-extrinsic normalization on experimental subjects' interpretation of their own speech outcomes. By combining a well-known manipulation of vowel-extrinsic normalization with speech auditory-motor adaptation, we show that exposure to different vowel spectral properties subsequently alters auditory feedback processing during speech production, thereby influencing speech motor adaptation. These findings extend the scope of perceptual normalization processes to include auditory feedback and support the idea that naturally occurring adaptations found in speech perception impact speech production. (PsycINFO Database Record
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".