TY - JOUR
T1 - Belief Updating in Psychosis, Depression and Anxiety Disorders: a systematic review across computational modelling approaches
AU - Gibbs-Dean, Toni
AU - Katthagen, Teresa
AU - Tsenkova, Iveta
AU - Ali, Rubbia
AU - Liang, Xinyi
AU - Spencer, Tom
AU - Diederen, Kelly
N1 - Funding Information:
Many thanks for the additional data curation work carried out by: Ashok Velineli, Carina Kuehne, Miao Liu, Ying Zhou, Yungen Cheah, Sambodhi Baiid, Seema Jasiwal, Lin-Yu Wu.
Publisher Copyright:
© 2023 The Authors
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - computational modelling, belief-updating, transdiagnostic, learning, prediction-error
UR - http://www.scopus.com/inward/record.url?scp=85149071566&partnerID=8YFLogxK
U2 - 10.1016/j.neubiorev.2023.105087
DO - 10.1016/j.neubiorev.2023.105087
M3 - Review article
SN - 0149-7634
VL - 147
JO - Neuroscience and Biobehavioral Reviews
JF - Neuroscience and Biobehavioral Reviews
M1 - 105087
ER -