Auditory Hallucinations in Schizophrenia and Nonschizophrenia Populations: A Review and Integrated Model of Cognitive Mechanisms
Bibliographic record
Abstract
While the majority of cognitive studies on auditory hallucinations (AHs) have been conducted in schizophrenia (SZ), an increasing number of researchers are turning their attention to different clinical and nonclinical populations, often using SZ findings as a model for research. Recent advances derived from SZ studies can therefore be utilized to make substantial progress on AH research in other groups. The objectives of this article were to (1) present an up-to-date review regarding the cognitive mechanisms of AHs in SZ, (2) review findings from cognitive research conducted in other clinical and nonclinical groups, and (3) integrate these recent findings into a cohesive framework. First, SZ studies show that the cognitive underpinnings of AHs include self-source-monitoring deficits and executive and inhibitory control dysfunctions as well as distortions in top-down mechanisms, perceptual and linguistic processes, and emotional factors. Second, consistent with SZ studies, findings in other population groups point to the role of top-down processing, abnormalities in executive inhibition, and negative emotions. Finally, we put forward an integrated model of AHs that incorporates the above findings. We suggest that AHs arise from an interaction between abnormal neural activation patterns that produce salient auditory signals and top-down mechanisms that include signal detection errors, executive and inhibition deficits, a tapestry of expectations and memories, and state characteristics that influence how these experiences are interpreted. Emotional factors play a particular prominent role at all levels of this hierarchy. Our model is distinctively powerful in explaining a range of phenomenological characteristics of AH across a spectrum of disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".