Motor regions contribute to speech perception: awareness, adaptation and categorisation
Bibliographic record
Abstract
Functional imaging and TMS studies show that motor and premotor cortex responds to heard speech though the functional significance of this response is unclear. Three recent fMRI studies, showing modulation of motor responses to heard speech in the absence of overt spoken or manual responses, may shed light on how regions typically associated with speech production contribute to perception. (1) Motor activity remains robust during light anaesthetic sedation, but is obliterated for deeply sedated participants who are no longer aware of speech. (2) Motor cortex responds more to distorted yet intelligible noise-vocoded words than to clear speech or unintelligible noise. This neural correlate of listening effort is also observed for "clear-then-vocoded" presentations that enhance perceptual adaptation compared to a matched "vocoded-then-clear" condition that doesn't enhance adaptation. (3) During paired priming of audio-morphed syllables we see a greater response to acoustic changes that cross phonological category boundaries compared to acoustically-equivalent within-category changes. Additional responses to between-category pairs extend to motor regions, suggesting that neural correlates of categorical perception include regions involved in speech production. These findings illustrate how perceptual awareness, adaptation and categorisation of speech all engage motor regions. Implications for neurobiological accounts of speech perception will be discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".