ArticleNeuroImage. Clinical2020
Atypical processing of uncertainty in individuals at risk for psychosis.
Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 79 citations in OpenAlex.
- Prior Expectations of Volatility Following Psychotherapy for Delusions: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Altered semantic prediction error processing with increasing schizotypal and autistic traits.Translational psychiatry · 2026Article
- Reward-induced endogenous pain inhibition scales with action-outcome certainty in humans.Pain · 2026Article
- Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty.Psychological review · 2026Article
- Anhedonia and Anxious Arousal Are Associated With Distinct Expectations About the Statistics of a Volatile Environment.Biological psychiatry global open science · 2026Article
- Neural Dynamics of Social Cognition: A Single-Trial Computational Analysis of Learning Under Uncertainty.Human brain mapping · 2026Article
- Self-Reflection Protects Behavior from Volatile Beliefs Linked to Paranoia.Computational psychiatry (Cambridge, Mass.) · 2026Article
- The Impact of Prior Beliefs about Volatility on Adaptive Behavior.Journal of cognition · 2026Article
- Dissecting how psychopathic traits are linked to learning in different contexts: A multilevel computational and electrophysiological approach.Cognitive, affective & behavioral neuroscience · 2025Article
- The joint estimation of uncertainty and its relationship with psychotic-like traits and psychometric schizotypy.Npj mental health research · 2025Article
- Temporal stability of semantic predictions in subclinical autistic and schizotypal personality traits.Schizophrenia (Heidelberg, Germany) · 2025Article
- Beyond reward learning deficits: Exploration-exploitation instability reveals computational heterogeneity in value-based decision making in early psychosis.medRxiv : the preprint server for health sciences · 2025Article
- Cognitive assessment in the Accelerating Medicines Partnership® Schizophrenia Program: harmonization priorities and strategies in a diverse international sample.Schizophrenia (Heidelberg, Germany) · 2025Article
- 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
- Neural correlates of uncertainty processing in psychosis spectrum disorder.Brain communications · 2025Article
- Article
- Are mental dysfunctions autonomous from brain dysfunctions? A perspective from the personal/subpersonal distinction.Discover mental health · 2024Review
- A prefrontal thalamocortical readout for conflict-related executive dysfunction in schizophrenia.Cell reports. Medicine · 2024Article
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Computational processes of simultaneous learning of stochasticity and volatility in humans.Nature communications · 2024Article
Corrections and comments
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Authors and funding
11 authors at 8 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour - with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental 'volatility' - and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals' behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.
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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.