Functional Overlap between Regions Involved in Speech Perception and in Monitoring One's Own Voice during Speech Production
Bibliographic record
Abstract
The fluency and the reliability of speech production suggest a mechanism that links motor commands and sensory feedback. Here, we examined the neural organization supporting such links by using fMRI to identify regions in which activity during speech production is modulated according to whether auditory feedback matches the predicted outcome or not and by examining the overlap with the network recruited during passive listening to speech sounds. We used real-time signal processing to compare brain activity when participants whispered a consonant-vowel-consonant word ("Ted") and either heard this clearly or heard voice-gated masking noise. We compared this to when they listened to yoked stimuli (identical recordings of "Ted" or noise) without speaking. Activity along the STS and superior temporal gyrus bilaterally was significantly greater if the auditory stimulus was (a) processed as the auditory concomitant of speaking and (b) did not match the predicted outcome (noise). The network exhibiting this Feedback Type x Production/Perception interaction includes a superior temporal gyrus/middle temporal gyrus region that is activated more when listening to speech than to noise. This is consistent with speech production and speech perception being linked in a control system that predicts the sensory outcome of speech acts and that processes an error signal in speech-sensitive regions when this and the sensory data do not match.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".