Hierarchical Bayesian models of delusion.


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Article
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Abstract

Researchers in the field of computational psychiatry have recently sought to model the formation and retention of delusions in terms of dysfunctions in a process of hierarchical Bayesian inference. I present a systematic review of such models and raise two challenges that have not received sufficient attention in the literature. First, the characteristic that is supposed to most sharply distinguish hierarchical Bayesian models from their competitors is their abandonment of the distinction between perception and cognition in favour of a unified inferential hierarchy. Standard ways of characterising this hierarchy, however, are inconsistent with the range of phenomena that delusions can represent. Second, there is little evidence that belief fixation in the healthy population is Bayesian, and an apparent abundance of evidence that it is not. As such, attempts to model delusions in terms of dysfunctions in a process of Bayesian inference are of dubious theoretical value.

Description
Keywords
Argumentative theory of reasoning, Backfire effect, Bayesian brain hypothesis, Bayesian just-so stories, Confirmation bias, Delusions, Motivated reasoning, Optimality, Predictive coding, Predictive processing, Psychosis, Rationality, Two-factor, Bayes Theorem, Delusions, Humans, Models, Theoretical
Journal Title
Conscious Cogn
Conference Name
Journal ISSN
1053-8100
1090-2376
Volume Title
61
Publisher
Elsevier BV
Sponsorship
AHRC (1653062)