​Belief Updating in Psychosis, Depression and Anxiety Disorders: a systematic review across computational modelling approaches

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)

Abstract

Alterations in belief updating are proposed to underpin symptoms of psychiatric illness, including psychosis, depression, and anxiety. Key parameters underlying belief updating can be captured using computational modelling techniques, aiding the identification of unique and shared deficits, and improving diagnosis and treatment. We systematically reviewed research that applied computational modelling to probabilistic tasks measuring belief updating in stable and volatile (changing) environments, across clinical and subclinical psychosis (n = 17), anxiety (n = 9), depression (n = 9) and transdiagnostic samples (n = 9). Depression disorders related to abnormal belief updating in response to the valence of rewards, evidenced in both stable and volatile environments. Whereas psychosis and anxiety disorders were associated with difficulties adapting to changing contingencies specifically, indicating an inflexibility and/or insensitivity to environmental volatility. Higher-order learning models revealed additional difficulties in the estimation of overall environmental volatility across psychosis disorders, showing increased updating to irrelevant information. These findings stress the importance of investigating belief updating in transdiagnostic samples, using homogeneous experimental and computational modelling approaches.

Original languageEnglish
Article number105087
JournalNeuroscience and Biobehavioral Reviews
Volume147
Early online date24 Feb 2023
DOIs
Publication statusPublished - Apr 2023

Keywords

  • computational modelling, belief-updating, transdiagnostic, learning, prediction-error

Fingerprint

Dive into the research topics of '​Belief Updating in Psychosis, Depression and Anxiety Disorders: a systematic review across computational modelling approaches'. Together they form a unique fingerprint.

Cite this