dc.contributor.author
Katthagen, Teresa
dc.contributor.author
Fromm, Sophie
dc.contributor.author
Wieland, Lara
dc.contributor.author
Schlagenhauf, Florian
dc.date.accessioned
2022-08-26T12:29:40Z
dc.date.available
2022-08-26T12:29:40Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/36033
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-35748
dc.description.abstract
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.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
computational psychiatry
en
dc.subject
Bayesian learning
en
dc.subject
Hierarchical Gaussian Filter
en
dc.subject
change point detection
en
dc.subject
schizophrenia
en
dc.subject
belief updating
en
dc.subject
reinforcement learning
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Models of Dynamic Belief Updating in Psychosis—A Review Across Different Computational Approaches
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
814111
dcterms.bibliographicCitation.doi
10.3389/fpsyt.2022.814111
dcterms.bibliographicCitation.journaltitle
Frontiers in Psychiatry
dcterms.bibliographicCitation.originalpublishername
Frontiers Media SA
dcterms.bibliographicCitation.volume
13
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
35492702
dcterms.isPartOf.eissn
1664-0640