Why use predictive processing to explain psychopathology? The case of anorexia nervosa

Predictive processing accounts are increasingly called upon to explain mental disorder. They seem to provide an attractive explanatory framework because the core idea of prediction error minimization can be applied to simultaneously account for several perceptual, attentional and reasoning deficits often implicated in mental disorder. However, it can be unclear how much is gained by such accounts: the proffered explanations can appear to have several weaknesses such as being too liberal, too shallow, or too wedded to formal notions of statistical learning. Here, we taxonomise the relatively unrecognised variety of explanatory tools under the framework and discuss how they can be employed to provide substantial explanations. We then apply the framework to anorexia nervosa, an eating disorder that is characterised by a complex set of perceptual, reasoning and decision-making problems. We conclude that the predictive processing framework is a valuable type of explanation for psychopathology