TY - JOUR
T1 - Computational Phenotyping of Aberrant Belief Updating in Individuals With Schizotypal Traits and Schizophrenia
AU - Mikus, Nace
AU - Lamm, Claus
AU - Mathys, Christoph
N1 - Publisher Copyright:
© 2024 Society of Biological Psychiatry
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
KW - Bayesian inference
KW - Predictive inference task
KW - Predictive processing
KW - Psychotic symptoms
KW - Symptom differentiation
KW - Test-retest reliability
UR - https://www.scopus.com/pages/publications/85208767483
U2 - 10.1016/j.biopsych.2024.08.021
DO - 10.1016/j.biopsych.2024.08.021
M3 - Journal article
C2 - 39218138
AN - SCOPUS:85208767483
SN - 0006-3223
VL - 97
SP - 188
EP - 197
JO - Biological Psychiatry
JF - Biological Psychiatry
IS - 2
ER -