Activity in regions sensitive to auditory speech is modified during speech production: fMRI evidence for an efference copy
Bibliographic record
Abstract
Models of speech production postulate that, in order to facilitate rapid and precise control of articulation, the predicted auditory feedback is sent to the auditory system to be compared with incoming sensory data. If this is so, an ‘‘error’’ signal may be observed when the predicted auditory feedback and the sensory consequences of vocalization do not match. We used event-related fMRI to look for the neural concomitants of such an error signal. In two conditions volunteers whispered ‘‘ted.’’ In one of these, voice-gated noise was used to mask the auditory feedback, which should result in an error signal. Two other conditions were yoked to the production conditions (either clearly heard or masked), but were listen-only and therefore no error signal would be expected. We acquired whole-brain EPI data from 21 subjects using a fast-sparse design. Activity in the superior temporal gyrus bilaterally was significantly greater for clear than masked speech during the listen-only trials and significantly higher for masked than for clear speech in the production trials. This crossover interaction indicates that speech production results in corollary discharge in the auditory system and furthermore suggests that this corollary discharge reflects expectations about the sensory concomitants of speech acts.
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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 | 0.000 | 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".