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Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches

To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the lea...

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Autores principales: Katthagen, Teresa, Fromm, Sophie, Wieland, Lara, Schlagenhauf, Florian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039658/
https://www.ncbi.nlm.nih.gov/pubmed/35492702
http://dx.doi.org/10.3389/fpsyt.2022.814111
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author Katthagen, Teresa
Fromm, Sophie
Wieland, Lara
Schlagenhauf, Florian
author_facet Katthagen, Teresa
Fromm, Sophie
Wieland, Lara
Schlagenhauf, Florian
author_sort Katthagen, Teresa
collection PubMed
description To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.
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spelling pubmed-90396582022-04-27 Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches Katthagen, Teresa Fromm, Sophie Wieland, Lara Schlagenhauf, Florian Front Psychiatry Psychiatry To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences. Frontiers Media S.A. 2022-04-12 /pmc/articles/PMC9039658/ /pubmed/35492702 http://dx.doi.org/10.3389/fpsyt.2022.814111 Text en Copyright © 2022 Katthagen, Fromm, Wieland and Schlagenhauf. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Katthagen, Teresa
Fromm, Sophie
Wieland, Lara
Schlagenhauf, Florian
Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
title Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
title_full Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
title_fullStr Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
title_full_unstemmed Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
title_short Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
title_sort models of dynamic belief updating in psychosis—a review across different computational approaches
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039658/
https://www.ncbi.nlm.nih.gov/pubmed/35492702
http://dx.doi.org/10.3389/fpsyt.2022.814111
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