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Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia.

J. D. Griffin, K. M. J. Diederen, J. Haarsma, I. C. Jarratt Barnham, B. R. H. Cook, E. Fernandez-Egea, S. Williamson, E. D. Van Sprang, R. Gaillard, F. Vinckier, I. M. Goodyer, Edward Bullmore, Raymond Dolan, Ian Goodyer, Peter Fonagy, Peter Jones, Samuel Chamberlain, Michael Moutoussis, Tobias Hauser, Sharon Neufeld, Rafael Romero-Garcia, Michelle St Clair, Petra Vértes, Kirstie Whitaker, Becky Inkster, Gita Prabhu, Cinly Ooi, Umar Toseeb, Barry Widmer, Junaid Bhatti, Laura Villis, Ayesha Alrumaithi, Sarah Birt, Aislinn Bowler, Kalia Cleridou, Hina Dadabhoy, Emma Davies, Ashlyn Firkins, Sian Granville, Elizabeth Harding, Alexandra Hopkins, Daniel Isaacs, Janchai King, Danae Kokorikou, Christina Maurice, Cleo McIntosh, Jessica Memarzia, Harriet Mills, Ciara O’Donnell, Sara Pantaleone, Jenny Scott, Beatrice Kiddle, Ela Polek, Pasco Fearon, John Suckling, Anne-Laura Van Harmelen, Rogier Kievit, Richard Bethlehem, G. K. Murray, P. C. Fletcher

Scientific reports July 30, 2024 DOI: 10.1038/s41598-024-68004-7 via PubMed

Summary

AI-generated from the abstract

People with first episode psychosis and treatment-resistant schizophrenia show reduced ability to stabilize their decision-making strategies in uncertain environments, resembling effects previously seen with the drug ketamine. In two studies, participants completed a probabilistic reversal learning task. Those with first episode psychosis made more errors and shifted strategies too often after misleading feedback. The treatment-resistant schizophrenia group also shifted strategies more, though their overall accuracy was not significantly reduced. Computational modeling revealed that only the treatment-resistant schizophrenia group showed altered confidence-based modulation of responding, similar to ketamine effects, though these modeling results are considered preliminary due to model limitations.

Study at a glance

Characteristics Observational cohort Peer reviewed
Population People with at-risk mental state, first episode psychosis, and treatment-resistant schizophrenia, plus matched controls
Keywords Psychosis Schizophrenia Decision-making Cognitive behavior Mental health
Citations 5
Key finding Both first episode psychosis and treatment-resistant schizophrenia groups showed reduced stabilization of responding in a noisy environment, but only the treatment-resistant schizophrenia group replicated ketamine-like effects in confidence-based modulation of responding.

Abstract

We used a probabilistic reversal learning task to examine prediction error-driven belief updating in three clinical groups with psychosis or psychosis-like symptoms. Study 1 compared people with at-risk mental state and first episode psychosis (FEP) to matched controls. Study 2 compared people diagnosed with treatment-resistant schizophrenia (TRS) to matched controls. The design replicated our previous work showing ketamine-related perturbations in how meta-level confidence maintained behavioural policy. We applied the same computational modelling analysis here, in order to compare the pharmacological model to three groups at different stages of psychosis. Accuracy was reduced in FEP, reflecting increased tendencies to shift strategy following probabilistic errors. The TRS group also showed a greater tendency to shift choice strategies though accuracy levels were not significantly reduced. Applying the previously-used computational modelling approach, we observed that only the TRS group showed altered confidence-based modulation of responding, previously observed under ketamine administration. Overall, our behavioural findings demonstrated resemblance between clinical groups (FEP and TRS) and ketamine in terms of a reduction in stabilisation of responding in a noisy environment. The computational analysis suggested that TRS, but not FEP, replicates ketamine effects but we consider the computational findings preliminary given limitations in performance of the model.

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