Computational phenotyping of aberrant belief updating in individuals with schizotypal traits and schizophrenia
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Abstract
BACKGROUND: Psychotic experiences are thought to emerge from various interrelated patterns of disrupted belief updating, such as overestimating the reliability of sensory information and misjudging task volatility, yet these substrates have never been jointly addressed under one computational framework, and it is not clear to what degree they reflect trait-like computational patterns. METHODS: We introduce a novel hierarchical Bayesian model that describes how individuals simultaneously update their beliefs about the task volatility and noise in observation. We applied this model to data from a modified predictive inference task in a test-retest study with healthy volunteers (N = 45, 4 sessions) and examined the relationship between model parameters and schizotypal traits in a larger online sample (N = 437) and in a cohort of patients with schizophrenia (N = 100). RESULTS: The interclass correlations were moderate to high for model parameters and excellent for averaged belief trajectories and precision-weighted learning rates estimated through hierarchical Bayesian inference. We found that uncertainty about the task volatility was related to schizotypal traits and to positive symptoms in patients, when learning to gain rewards. In contrast, negative symptoms in patients were associated with more rigid beliefs about observational noise, when learning to avoid losses. CONCLUSIONS: These findings suggest that individuals with schizotypal traits across the psychosis continuum are less likely to learn or use higher-order statistical regularities of the environment and showcase the potential of clinically relevant computational phenotypes for differentiating symptom groups in a transdiagnostic manner.
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In the HGF, dynamic changes in observation noise can be accommodated by hidden states that serve as noise parents to observable outcome states.
“In the HGF, dynamic changes in observation noise can easily be accommodated by hidden states that serve as noise parents to observable outcome states (see Appendix 6.3 for the equations, Figure 7 for an example simulation, and Mikus et al., 2025 , for an application to a dataset in computational psychiatry).”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Mikus et al., 2025
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Schuster, B. A., & Lamm, C. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.111206 · Cited by 2 · see citing works
Self-citationIncreasing the Interpretability of Psychosis Models
Buck, J., & Horga, G. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.10.022 · Cited by 2 · see citing works
Gibbs-Dean, T., Katthagen, T., Hu, R., Westwater, M. L., Spencer, T. J., & Diederen, K. M. J. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00146-6 · Cited by 1 · see citing works
Yamamoto, S., Buckingham, G., Arthur, T., & Harris, D. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00494.2025 · Cited by 1 · see citing works
Environmental uncertainty shapes human effort learning
Bi, R., Grohn, J., Lockwood, P. L., Klein-Flügge, M. C., & Weber, L. A. E. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003791 · Cited by 1 · see citing works
Environmental dynamics shape human learning: change points versus random walksPreprint
Foucault, C., Weber, L. A. E., & Hunt, L. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.26.690700 · Cited by 1 · see citing works
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
Self-citationClosed-form predictive coding via hierarchical Gaussian filters
Baskakovs, A., Estebe, S., Enevoldsen, K., Nielbo, K., Mathys, C., & Legrand, N. (2026) · arXiv · doi:10.48550/arxiv.2605.20293
Self-citationElectroconvulsive therapy in erotomanic delusion: a path to belief updating
Montet, I., Longo, A., & Bottemanne, H. (2025) · Nordic Journal of Psychiatry · doi:10.1080/08039488.2025.2528094
Environmental dynamics shape human learning: change points versus random walksPreprint
Foucault, C., Weber, L. A. E., & Hunt, L. T. (2026) · eLife · doi:10.7554/elife.110137.1
Environmental dynamics shape human learning: change points versus random walksPreprint
Foucault, C., Weber, L. A. E., & Hunt, L. T. (2026) · eLife · doi:10.7554/elife.110137
The generalized Hierarchical Gaussian Filter
Weber, L. A., Waade, P. T., Legrand, N., Møller, A. H., Stephan, K. E., & Mathys, C. (2026) · doi:10.7554/elife.110174.1
Self-citationArtificial intelligence analytics for diagnosis, prognosis, and outcomes prediction
Jalali, S., You, Q., Xu, V., Chen, L., Zini, J., Zamorano, T., Luo, Y., Friesen, T., Fontana, G. J., & Benrimoh, D. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33151-0.00009-5
Neurocomputational mechanisms underlying cognitive flexibility: a translational perspective
Bähner, F., Oh, S., Hummos, A., Sandbrink, K. J., Meyer‐Lindenberg, A., & Collins, A. G. E. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.08.003
- Authors
- Mikus, N., Lamm, C., & Mathys, C.
- Affiliations
- University of Vienna (AT)
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2025-01
- Volume
- 97
- Issue
- 2
- Pages
- 188-197
- ISSN
- 0006-3223 (ISSN-L)
- OpenAlex
- W4402071290
- Cited by
- 16
- References
- 97
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 3
- FWCI
- 4.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biological Psychiatry (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Scopus (Elsevier) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Vienna Science and Technology Fund (VRG13-007)
- Aarhus University Research Foundation (AUFF-E-2019-7-10)
- 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 and Behavioral Psychology Studies
- Keywords
- belief updating, schizotypy, schizophrenia, computational phenotyping, measurement noise, Bayesian hierarchical model, positive symptoms, negative symptoms, psychosis continuum
- MeSH terms
- Adult, Bayes Theorem, Culture, Female, Humans, Male, Middle Aged, Models, Psychological, Phenotype, Schizophrenia, Schizophrenic Psychology, Schizotypal Personality Disorder, Young Adult
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.
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