Aberrant hierarchical prediction errors are associated with transition to psychosis: A computational single-trial analysis of the mismatch negativity
Open accessAbstract
Background: Mismatch negativity (MMN) reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals at clinical high risk for psychosis (CHR-P). Here, we adopt a computational approach to develop a mechanistic model of MMN reductions in CHR-P individuals and patients early in the course of schizophrenia (ESZ). Methods: Electroencephalography (EEG) was recorded in 38 CHR-P individuals (15 converters), 19 ESZ patients (≤5 years), and 44 healthy controls (HC) during three different auditory oddball MMN paradigms including 10% duration-, frequency-, or double-deviants, respectively. We modelled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated EEG amplitudes over sensor space and time. Results: Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P and ESZ groups compared to HC. Furthermore, low-level precision-weighted prediction errors were significantly different in CHR-P that later converted to psychosis compared to non-converters. Conclusions: Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model appears to capture pathophysiological mechanisms relevant to early psychosis and the risk for future psychosis in CHR-P individuals, and may serve as a predictive biomarker and mechanistic target for novel treatment development.
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The HGF provides an approximately Bayes-optimal solution to stepwise belief updating, useful for ideal observer analyses, and its parameters can be fit to behavioural responses of individual participants.
“The model provides an approximately Bayes-optimal solution to stepwise belief updating, useful for ideal observer analyses (Stefanics et al., 2018; Weber et al., 2020, 2022; Hauke et al., 2022), but the parameters of the HGF can also be fit to behavioural responses of individual participants.”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Hauke et al., 2022
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The generalized Hierarchical Gaussian Filter
Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., & Mathys, C. (2026) · eLife · doi:10.7554/elife.110174
- Authors
- Hauke, D. J., Charlton, C. E., Schmidt, A., Griffiths, J., Woods, S. W., Ford, J. M., Srihari, V. H., Roth, V., Diaconescu, A. O., & Mathalon, D. H.
- Affiliations
- University College London (GB)
- Centre for Addiction and Mental Health (CA)
- University of Basel (CH)
- Yale University (US)
- San Francisco VA Medical Center (US)
- Corresponding author
- Hauke, Daniel J.
- ORCID
- Venue
- Biological Psychiatry Cognitive Neuroscience and Neuroimaging
- Publisher
- openRxiv
- Published
- 2022-12-20
- Volume
- 8
- Issue
- 12
- Pages
- 1176-1185
- ISSN
- 2451-9022 (ISSN-L)
- OpenAlex
- W4385446803
- Cited by
- 18
- References
- 76
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 7 · 2023: 3
- FWCI
- 3.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Biological Psychiatry Cognitive Neuroscience and Neuroimaging (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation
- U.S. Department of Veterans Affairs (1IK6CX002519)
- Brain and Behavior Research Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (200054, 167952)
- Krembil Foundation
- National Institutes of Health (MH076989)
- National Institute of Mental Health (R01MH103831, U01MH076989, R01MH076989)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- mismatch negativity, clinical high risk for psychosis, transition to psychosis, schizophrenia, prediction error, hierarchical Gaussian filter, predictive coding, auditory oddball paradigm, sensory learning, early psychosis, biomarkers, computational modeling
- MeSH terms
- Electroencephalography, Humans, Psychotic Disorders, Schizophrenia, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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