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Toward a neurobiology of delusions
Open accessAbstract
Delusions are the false and often incorrigible beliefs that can cause severe suffering in mental illness. We cannot yet explain them in terms of underlying neurobiological abnormalities. However, by drawing on recent advances in the biological, computational and psychological processes of reinforcement learning, memory, and perception it may be feasible to account for delusions in terms of cognition and brain function. The account focuses on a particular parameter, prediction error--the mismatch between expectation and experience--that provides a computational mechanism common to cortical hierarchies, fronto-striatal circuits and the amygdala as well as parietal cortices. We suggest that delusions result from aberrations in how brain circuits specify hierarchical predictions, and how they compute and respond to prediction errors. Defects in these fundamental brain mechanisms can vitiate perception, memory, bodily agency and social learning such that individuals with delusions experience an internal and external world that healthy individuals would find difficult to comprehend. The present model attempts to provide a framework through which we can build a mechanistic and translational understanding of these puzzling symptoms.
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“the learning function of dopamine may nonetheless mediate the transition from disordered perception to delusion. In this view, dopamine-modulated learning underlies the re-sculpting of generative models to accommodate persistently elevated prediction error signals (Corlett et al., 2010).”
From An Interoceptive Predictive Coding Model of Conscious Presence (2012) · cites this paper as Corlett et al., 2010
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Importantly, NMDAR dependent signaling is thought to be essential for updating and encoding representations of beliefs (Corlett et al., 2010; Vinckier et al., 2016).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Corlett et al., 2010
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Evidence from anatomical and physiological studies has established bridges between the computational and physiological components of this hierarchical precision-weighted message passing: prediction error signaling via forward connections likely rests on glutamatergic (AMPA and NMDA) receptors, prediction signaling via backward connections probably exclusively on NMDA receptors, while precision”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Corlett et al., 2010
Experimental — accuracy not yet measuredIn the citing paper
“Predictive coding models explain these experiences as attempts to account for and minimise uncertainty.10”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [10]
In the citing paper
“we propose that anomalies in the genesis of prediction error, its nature, and our responses to it can explain the emergence of psychosis.”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [10]
In the citing paper
“Disruptions to both processes (prediction error and attentional salience attribution) can manifest in behaviour as aberrant learning and, ultimately, delusional beliefs.10”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [10]
In the citing paper
“A patient might experience delusions because of a disruption to either system.10”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [10]
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BackgroundInfluential
Metadata
- Authors
- Corlett, P. R., Taylor, J. R., Wang, X.-J., Fletcher, P. C., & Krystal, J. H.
- Affiliations
- Yale University (US)
- University of Cambridge (GB)
- Corresponding author
- Corlett, P.R.
- Venue
- Progress in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2010-11
- Volume
- 92
- Issue
- 3
- Pages
- 345-369
- ISSN
- 0301-0082 (ISSN-L)
- PMC
- PMC3676875
- OpenAlex
- W2018699045
- Cited by
- 394
- References
- 438
- Citations by year
- 2026: 31 · 2025: 16 · 2024: 24 · 2023: 20 · 2022: 29 · 2021: 30 · 2020: 33 · 2019: 22 · 2018: 20 · 2017: 35 · 2016: 27 · 2015: 32 · 2014: 25 · 2013: 22 · 2012: 21
- FWCI
- 34.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Progress in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust (064351)
- National Institute on Drug Abuse (R01DA011717, R01DA015222)
- Topics
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- delusions, prediction error, reinforcement learning, hierarchical prediction, frontostriatal circuits, amygdala, parietal cortex, memory, perception, bodily agency, social learning, computational psychiatry
- MeSH terms
- Brain, Cognition, Computer Simulation, Culture, Delusions, Humans, Learning, Memory, Models, Neurological, Nervous System Diseases, Neurobiology, Perception
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