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Glutamatergic Model Psychoses: Prediction Error, Learning, and Inference
Open accessAbstract
Modulating glutamatergic neurotransmission induces alterations in conscious experience that mimic the symptoms of early psychotic illness. We review studies that use intravenous administration of ketamine, focusing on interindividual variability in the profundity of the ketamine experience. We will consider this individual variability within a hypothetical model of brain and cognitive function centered upon learning and inference. Within this model, the brains, neural systems, and even single neurons specify expectations about their inputs and responding to violations of those expectations with new learning that renders future inputs more predictable. We argue that ketamine temporarily deranges this ability by perturbing both the ways in which prior expectations are specified and the ways in which expectancy violations are signaled. We suggest that the former effect is predominantly mediated by NMDA blockade and the latter by augmented and inappropriate feedforward glutamatergic signaling. We suggest that the observed interindividual variability emerges from individual differences in neural circuits that normally underpin the learning and inference processes described. The exact source for that variability is uncertain, although it is likely to arise not only from genetic variation but also from subjects' previous experiences and prior learning. Furthermore, we argue that chronic, unlike acute, NMDA blockade alters the specification of expectancies more profoundly and permanently. Scrutinizing individual differences in the effects of acute and chronic ketamine administration in the context of the Bayesian brain model may generate new insights about the symptoms of psychosis; their underlying cognitive processes and neurocircuitry.
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“the main problem in schizophrenia lies not with the prediction of sensory input per se, but in the delicate balance of precision ascribed to prior beliefs and sensory evidence (Friston, 2005; Corlett et al., 2011).”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Corlett et al., 2011
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“induction of a delusional mood (Corlett et al., 2011)”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Corlett et al., 2011
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“The resulting aberrant encoding of precision could lead to an abnormally strong weighting of PE, which in turn leads to aberrant learning and the formation of delusional beliefs (53, 58, 59, 64).”
From The Predictive Coding Account of Psychosis (2018) · cites this paper as [64]
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“neurodevelopmental disorders such as autism spectrum disorder [132,133] and schizophrenia [134,135] have been linked to an atypical integration of prior and incoming information”
From How Do Expectations Shape Perception? (2018) · cites this paper as [135]
In the citing paper
“It is of interest to note that similar Bayesian theories have previously been stated in the context of schizophrenia (63, 68, 89–92).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [91]
In the citing paper
“For example, prediction error signaling in cortex likely rests on fast glutamatergic transmission, probably involving fast AMPA receptors under regulation by slower NMDA receptors (91, 108);”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [91]
In the citing paper
“predictions are probably conveyed by glutamatergic backward connections and exclusively via slow NMDA receptors (72, 91);”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [91]
In the citing paper
“as its antagonistic effects at dopamine receptors may interfere with precision-weighting of prediction errors, and thus updating of priors, in high-level regions of the cognitive hierarchy such as prefrontal cortex (91), which are critical for the development of abstract representations.”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [91]
In the citing paper
“Furthermore, ketamine does not routinely induce hallucinations, rather illusory percepts-alterations of stimuli that are actually present.19”
From Are hallucinations due to an imbalance between excitatory and inhibitory influences on the brain? (2016) · cites this paper as [19]
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Metadata
- Authors
- Corlett, P. R., Honey, G. D., Krystal, J. H., & Fletcher, P. C.
- Affiliations
- Yale University (US)
- Pfizer (United States) (US)
- University of Cambridge (GB)
- Corresponding author
- Corlett, Philip R
- Venue
- Neuropsychopharmacology
- Published
- 2010-09-22
- Volume
- 36
- Issue
- 1
- Pages
- 294-315
- ISSN
- 0893-133X (ISSN-L)
- 1740-634X
- PMC
- PMC3055519
- OpenAlex
- W2094348922
- Cited by
- 229
- References
- 300
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 14 · 2023: 10 · 2022: 9 · 2021: 19 · 2020: 13 · 2019: 9 · 2018: 12 · 2017: 21 · 2016: 20 · 2015: 29 · 2014: 19 · 2013: 18 · 2012: 17
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Neuropsychopharmacology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (064351)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Social Sciences › Psychology › Clinical Psychology › Psychedelics and Drug Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- ketamine, psychosis, glutamatergic neurotransmission, NMDA receptor blockade, prediction error, interindividual variability, subjective experience, expectancy violation, individual differences
- MeSH terms
- Animals, Bayes Theorem, Cognition, Humans, Learning, Models, Neurological, Psychoses, Substance-Induced, Psychotic Disorders, Receptors, Glutamate, Excitatory Amino Acid Antagonists, Glutamic Acid
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