ArticleSchizophrenia (Heidelberg, Germany)2022
Atypical prediction error learning is associated with prodromal symptoms in individuals at clinical high risk for psychosis.
Article in Schizophrenia (Heidelberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 31 citations in OpenAlex.
- Linking reduced prefrontal microcircuit inhibition in schizophrenia to EEG biomarkers in silico.PLoS computational biology · 2026Article
- Internal monitoring deficits attenuate post-error slowing in psychosis-proneness.Schizophrenia research. Cognition · 2026Article
- A Computational Account of the Development and Evolution of Psychotic Symptoms.Biological psychiatry · 2025Review
- Localizing hierarchical prediction errors and precisions during an oddball task with volatility: Computational insights and relationship with psychosocial functioning in healthy individuals.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Reviewing explore/exploit decision-making as a transdiagnostic target for psychosis, depression, and anxiety.Cognitive, affective & behavioral neuroscience · 2024Review
- Article
- A new predictive coding model for a more comprehensive account of delusions.The lancet. Psychiatry · 2024Review
- Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol.PloS one · 2024Article
- Thalamo-cortical circuits during sensory attenuation in emerging psychosis: a combined magnetoencephalography and dynamic causal modelling study.Schizophrenia (Heidelberg, Germany) · 2023Article
- Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings.Psychoradiology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reductions in the auditory mismatch negativity (MMN) have been well-demonstrated in schizophrenia rendering it a promising biomarker for understanding the emergence of psychosis. According to the predictive coding theory of psychosis, MMN impairments may reflect disturbances in hierarchical information processing driven by maladaptive precision-weighted prediction errors (pwPEs) and enhanced belief updating. We applied a hierarchical Bayesian model of learning to single-trial EEG data from an auditory oddball paradigm in 31 help-seeking antipsychotic-naive high-risk individuals and 23 healthy controls to understand the computational mechanisms underlying the auditory MMN. We found that low-level sensory and high-level volatility pwPE expression correlated with EEG amplitudes, coinciding with the timing of the MMN. Furthermore, we found that prodromal positive symptom severity was associated with increased expression of sensory pwPEs and higher-level belief uncertainty. Our findings provide support for the role of pwPEs in auditory MMN generation, and suggest that increased sensory pwPEs driven by changes in belief uncertainty may render the environment seemingly unpredictable. This may predispose high-risk individuals to delusion-like ideation to explain this experience. These results highlight the value of computational models for understanding the pathophysiological mechanisms of psychosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.