Predictive processing in mental illness: Hierarchical circuitry for perception and trauma

Predictive coding emerged as an explanation for how the brain can efficiently encode sensory stimuli. The hierarchical organization of neural circuits for perception thus passes prediction errors between computational layers. Extensions of this theory have provided a unifying understanding of Bayesian inference within neural circuits and psychiatric disorders. In particular, disorders of perception and belief have been explained as a Bayesian process of weighing prior beliefs (predictions) against new sensory data (prediction errors). The present issue of the Journal of Abnormal Psychology provides further evidence for how psychotic disorders develop and persist and how addiction- and trauma-related disorders may also be conceptualized. Trauma-related disorders in particular have begun to be identified as disorders of excessive accumulated prediction errors (uncertainty) over life. Here we summarize and reconcile recent advances in reinforcement learning momentum models with predictive processing and attempt to point out potential pitfalls for the application of hierarchical prediction models to stress. Future directions for understanding stress through this lens may need to involve updates to a purely hierarchical view or reframing long times cale molecular predictions as higher-order predictions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).