Two Distinct Auditory-Motor Circuits for Monitoring Speech Production as Revealed by Content-Specific Suppression of Auditory Cortex
Bibliographic record
Abstract
Speech production, both overt and covert, down-regulates the activation of auditory cortex. This is thought to be due to forward prediction of the sensory consequences of speech, contributing to a feedback control mechanism for speech production. Critically, however, these regulatory effects should be specific to speech content to enable accurate speech monitoring. To determine the extent to which such forward prediction is content-specific, we recorded the brain's neuromagnetic responses to heard multisyllabic pseudowords during covert rehearsal in working memory, contrasted with a control task. The cortical auditory processing of target syllables was significantly suppressed during rehearsal compared with control, but only when they matched the rehearsed items. This critical specificity to speech content enables accurate speech monitoring by forward prediction, as proposed by current models of speech production. The one-to-one phonological motor-to-auditory mappings also appear to serve the maintenance of information in phonological working memory. Further findings of right-hemispheric suppression in the case of whole-item matches and left-hemispheric enhancement for last-syllable mismatches suggest that speech production is monitored by 2 auditory-motor circuits operating on different timescales: Finer grain in the left versus coarser grain in the right hemisphere. Taken together, our findings provide hemisphere-specific evidence of the interface between inner and heard speech.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".