A Bayesian perspective on delusions: Suggestions for modifying two reasoning tasks

Abstract

Background and objectivesThere are a range of mechanistic explanations on the formation and maintenance of delusions. Within the Bayesian brain hypothesis, particularly within the framework of predictive coding models, delusions are seen as an aberrant inference process characterized by either a failure in sensory attenuation or an aberrant weighting of prior experience. Testing of these Bayesian decision theories requires measuring of both the patients' confidence in their beliefs and the confidence they assign new, incoming information. In the Bayesian framework we apply here, the former is referred to as the prior while the latter is usually called the data or likelihood. Methods and resultsThis narrative review will commence by giving an introduction to the basic concept underlying the Bayesian decision theory approach to delusion. A consequence of crucial importance of this sketch is that it provides a measure for the persistence of a belief. Experimental tasks measuring these parameters are presented. Further, a modification of two standard reasoning tasks, the beads task and the evidence integration task, is proposed that permits testing the parameters from Bayesian decision theory. LimitationsPatients differ from controls by the distress the delusions causes to them. The Bayesian Decision theory framework has no explicit parameter for distress. ConclusionsA more detailed reporting of differences between patients with delusions is warranted.