Understanding Cognitive Behavioral Therapy for Psychosis (CBTp) Through the Predictive Coding Framework

Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis (CBTp), are efficacious; but mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support in explaining the different symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in CBTp-based psychotherapy for persecutory delusions that target specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive-behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective CBTp systematically targets the way sensory data is selected, experienced and interpreted, allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.