Testing Bayesian models of belief updating in the context of depressive symptomatology.

Abstract

Predictive processing approaches to belief updating in depression propose that depression is related to more negative and more precise priors. Also, belief updating is assumed be negatively biased in comparison to normative Bayesian updating. There is a lack of efficient methods to mathematically model belief updating in depression. We validated a novel performance belief updating paradigm in a nonclinical sample (N=133). Participants repeatedly participated in a non-self-related emotion recognition task and received false feedback. Effects of the feedback manipulation and differences in depressive symptoms on belief updating were analysed in Bayesian multilevel analyses. Beliefs were successfully manipulated through the feedback provided. Depressive symptoms were associated with more negative updating than normative Bayesian updating but results were influenced by few cases. No evidence of biased change in beliefs or overly precise priors was found. Depressive symptoms were associated with more negative updating of generalised performance beliefs. There was cautious support for negatively biased belief updating associated with depressive symptoms, especially for generalised beliefs. The content of the task may not be self-relevant enough to cause strong biases. Further explication of Bayesian models of depression and replication in clinical samples is needed.