ABSTRACT
Inspired by the explanatory power of computational modeling in basic cognitive neuroscience (Schultz et al., 1997; Behrens et al., 2007; Rao and Ballard, 1999; Dayan and Daw, 2008) researchers in the field of computational psychiatry aim to characterize mental disorder in terms of differences in information processing, specified explicitly and precisely in mathematical terms. In this chapter, we illustrate the power of this diverse emerging field using examples drawn from computational investigations of schizophrenia. In focusing on applications of the computational approach to the mind, our outlook will be somewhat more empirical than other, more theoretical chapters in this volume. We focus on two main approaches within computational psychiatry: reinforcement learning (Sutton and Barto, 1998) and predictive processing (Friston and Kiebel, 2009). Finally, we consider whether computational approaches can offer a plausible philosophical account of delusions.
