Explaining delusions: Reducing uncertainty through basic and computational neuroscience

Erin J. Feeney, Stephanie M. Groman, Jane R. Taylor, Philip R. Corlett

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Delusions, the fixed false beliefs characteristic of psychotic illness, have long defied understanding despite their response to pharmacological treatments (e.g., D2 receptor antagonists). However, it can be challenging to discern what makes beliefs delusional compared with other unusual or erroneous beliefs. We suggest mapping the putative biology to clinical phenomenology with a cognitive psychology of belief, culminating in a teleological approach to beliefs and brain function supported by animal and computational models. We argue that organisms strive to minimize uncertainty about their future states by forming and maintaining a set of beliefs (about the organism and the world) that are robust, but flexible. If uncertainty is generated endogenously, beliefs begin to depart from consensual reality and can manifest into delusions. Central to this scheme is the notion that formal associative learning theory can provide an explanation for the development and persistence of delusions. Beliefs, in animals and humans, may be associations between representations (e.g., of cause and effect) that are formed by minimizing uncertainty via new learning and attentional allocation. Animal research has equipped us with a deep mechanistic basis of these processes, which is now being applied to delusions. This work offers the exciting possibility of completing revolutions of translation, from the bedside to the bench and back again. The more we learn about animal beliefs, the more we may be able to apply to human beliefs and their aberrations, enabling a deeper mechanistic understanding.

Original languageEnglish (US)
Pages (from-to)263-272
Number of pages10
JournalSchizophrenia bulletin
Volume43
Issue number2
DOIs
StatePublished - Mar 1 2017
Externally publishedYes

Bibliographical note

Funding Information:
NIH Medical Scientist Training Program Training Grant (T32GM007205)

Publisher Copyright:
© 2017 The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Associative learning
  • Behavioral neuroscience
  • Cognitive neuroscience
  • Computational psychiatry
  • Delusions

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