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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.814111/full |
| _version_ | 1811286183920009216 |
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| author | Teresa Katthagen Sophie Fromm Sophie Fromm Sophie Fromm Lara Wieland Lara Wieland Lara Wieland Florian Schlagenhauf Florian Schlagenhauf Florian Schlagenhauf Florian Schlagenhauf |
| author_facet | Teresa Katthagen Sophie Fromm Sophie Fromm Sophie Fromm Lara Wieland Lara Wieland Lara Wieland Florian Schlagenhauf Florian Schlagenhauf Florian Schlagenhauf Florian Schlagenhauf |
| author_sort | Teresa Katthagen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T02:56:00Z |
| format | Article |
| id | doaj.art-7a6baf3ec31b4b9ea959265be1efa07a |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-04-13T02:56:00Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-7a6baf3ec31b4b9ea959265be1efa07a2022-12-22T03:05:38ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402022-04-011310.3389/fpsyt.2022.814111814111Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational ApproachesTeresa Katthagen0Sophie Fromm1Sophie Fromm2Sophie Fromm3Lara Wieland4Lara Wieland5Lara Wieland6Florian Schlagenhauf7Florian Schlagenhauf8Florian Schlagenhauf9Florian Schlagenhauf10Department of Psychiatry and Neurosciences, CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyDepartment of Psychiatry and Neurosciences, CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyEinstein Center for Neurosciences, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyBernstein Center for Computational Neuroscience, Berlin, GermanyDepartment of Psychiatry and Neurosciences, CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyEinstein Center for Neurosciences, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyBernstein Center for Computational Neuroscience, Berlin, GermanyDepartment of Psychiatry and Neurosciences, CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyEinstein Center for Neurosciences, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyBernstein Center for Computational Neuroscience, Berlin, GermanyNeuroCure Clinical Research Center, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, GermanyTo 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2022.814111/fullcomputational psychiatryBayesian learningHierarchical Gaussian Filterchange point detectionschizophreniapsychosis |
| spellingShingle | Teresa Katthagen Sophie Fromm Sophie Fromm Sophie Fromm Lara Wieland Lara Wieland Lara Wieland Florian Schlagenhauf Florian Schlagenhauf Florian Schlagenhauf Florian Schlagenhauf Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches Frontiers in Psychiatry computational psychiatry Bayesian learning Hierarchical Gaussian Filter change point detection schizophrenia psychosis |
| 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 | computational psychiatry Bayesian learning Hierarchical Gaussian Filter change point detection schizophrenia psychosis |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.814111/full |
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