The predictive coding account of psychosis

Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neuro-computational mechanisms of psychosis. One successful approach involves predictive coding and Bayesian inference. Here, inferences regarding the current state of the world are made by combining prior beliefs with incoming sensory signals. Mismatches between prior beliefs and incoming signals constitute prediction errors that drive new learning. Psychosis has been suggested to result from a decreased precision in the encoding of prior beliefs relative to the sensory data, thereby garnering maladaptive inferences.

Source: Biological Psychiatry - May 25, 2018 Category: Psychiatry Authors: Philipp Sterzer, Rick A. Adams, Paul Fletcher, Chris Frith, Stephen M. Lawrie, Lars Muckli, Predrag Petrovic, Peter Uhlhaas, Martin Voss, Philip R. Corlett Tags: Review Source Type: research