Inner speech and the neurobiology of psychosis
Bibliographic record
Abstract
Aberrations of inner speech have been linked to psychotic symptoms such as thought insertion and auditory verbal hallucinations. These symptoms may reflect failures of prediction and source monitoring. Normally, efference copies of speech motor commands are sent to auditory cortices and suppressed, helping distinguish self-generated from external input. If suppression malfunctions, predicted auditory input may become perceptually salient. Further, if self-monitoring or error detection-related regions are also impaired (e.g., anterior cingulate cortex, ACC), inner speech may be misattributed as external. We tested this proposal using neuroimaging meta-analyses, examining how the brain systems in overt and inner speech production in neurotypical participants overlap with findings from psychosis spectrum participants performing a range of tasks. They showed increased activity in motor-related regions associated with inner speech (e.g., ventral premotor cortices) and decreased grey matter in bilateral auditory cortices and ACC, in regions specific to overt speech. Coactivation-based network analyses revealed that these ventral premotor and auditory regions form distinct, inversely coupled audiomotor networks. Classification suggests the ventral premotor network supports 'higher-level' language processing, while the audiomotor network supports 'lower-level' speech and self-referential processing. Overall, results accord with the proposal that psychotic symptoms like auditory verbal hallucinations derive from phenotypic hyperactivation in inner speech-related regions that yield affectively salient efference copy signals that are insufficiently suppressed and monitored as self-produced. In line with a hierarchical predictive-processing account, disruption of a distributed recurrent system distorts self-awareness and conscious experience.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".