Computational psychiatry: a Rosetta Stone linking the brain to mental illness
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Mismatches between inputs and expectations are signalled and reduced by ignoring, changing, or updating.
“Mismatches are signalled and minimised in several possible ways: they can be ignored (experiencing what we expect rather than what is presented by our senses), changed (altering the world to fit expectation), or updated (a changed model of the world).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Friston, 2009, The free-energy principle: a rough guide to the brain?, Trends Cogn Sci source
Not checked in the free run.
- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Error minimisation can be viewed as a core survival principle.
“Error minimisation can be seen as a core principle of survival (avoid surprises to stay alive) 8”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Friston, 2009, The free-energy principle: a rough guide to the brain?, Trends Cogn Sci source
Not checked in the free run.
- Interpretationreasoning§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Anomalies in how prediction error arises, what it is like, and how it is responded to can explain the emergence of psychosis.
“we propose that anomalies in the genesis of prediction error, its nature, and our responses to it can explain the emergence of psychosis.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [8] Friston, 2009, The free-energy principle: a rough guide to the brain?, Trends Cogn Sci source
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- cites [9] Gross, 1972, Sensory disorders in schizophrenia, Arch Psychiatr Nervenkr source
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- cites [10] Corlett, 2010, Toward a neurobiology of delusions, Prog Neurobiol source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
People with schizophrenia may also disregard evidence that contradicts their decision.
“They might also ignore pieces of evidence that contradict that decision.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [4] Garety, 2001, A cognitive model of the positive symptoms of psychosis, Psychol Med source
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- cites [5] Langdon, 2014, Jumping to delusions in early psychosis, Cogn Neuropsychiatry source
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- cites [6] Wittorf, 2012, Specificity of jumping to conclusions and attributional biases: a comparison between patients with schizophrenia, depression, and anorexia nervosa, Cogn Neuropsychiatry source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Predictive coding models explain psychotic experiences as attempts to account for and reduce uncertainty.
“Predictive coding models explain these experiences as attempts to account for and minimise uncertainty.10”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Corlett, 2010, Toward a neurobiology of delusions, Prog Neurobiol source
Not checked in the free run.
- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
In uncertain decision settings, people with schizophrenia and delusions (or proneness to them) tend to rely on less information than people without schizophrenia.
“When people with schizophrenia (who have or are prone to delusions) make decisions in an uncertain setting, they tend to draw on less information in reaching that decision than does a person without schizophrenia.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [4] Garety, 2001, A cognitive model of the positive symptoms of psychosis, Psychol Med source
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- cites [5] Langdon, 2014, Jumping to delusions in early psychosis, Cogn Neuropsychiatry source
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- cites [6] Wittorf, 2012, Specificity of jumping to conclusions and attributional biases: a comparison between patients with schizophrenia, depression, and anorexia nervosa, Cogn Neuropsychiatry source
Not checked in the free run.
- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Successful functioning requires comparing actual inputs with expectations.
“To function successfully in this way, actual inputs should be compared with expectations.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Friston, 2009, The free-energy principle: a rough guide to the brain?, Trends Cogn Sci source
Not checked in the free run.
- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Dopamine modulates the prediction-error signal by increasing its gain.
“Dopamine modulates the prediction error signal, enhancing its gain.16”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] Friston, 2012, Dopamine, affordance and active inference, PLoS Comput Biol source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
The jumping-to-conclusions bias is invoked in models as a mechanism that explains delusions.
“the jumping to conclusions (JTC) bias is invoked as an explanatory mechanism in models of delusions.4”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [4] Garety, 2001, A cognitive model of the positive symptoms of psychosis, Psychol Med source
Not checked in the free run.
- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
In predictive coding, the brain is a model of its world, reproducing past regularities in its structure and function.
“Within this framework, the brain is a model of its world, recapitulating in its structure and function past experiences of regularities.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Friston, 2009, The free-energy principle: a rough guide to the brain?, Trends Cogn Sci source
Not checked in the free run.
- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Animal behaviour can be viewed as mediated by competition between prior learning and new experiences.
“The animal's behaviour can be thought of as being mediated by a competition between its previous learning and its new experiences.23”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Eisenhardt, 2007, Extinction learning, reconsolidation and the internal reinforcement hypothesis, Neurobiol Learn Mem source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Prediction error and attentional salience are mediated by separate but interacting neurotransmitter systems, dopamine and acetylcholine.
“key drivers of earning -prediction error and attentional salience 18 are mediated by separate but interacting neurotransmitter systems (dopamine and acetylcholine) 20,21 .”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [18] Schultz, 2000, Neuronal coding of prediction errors, Annu Rev Neurosci source
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- cites [20] Chiba, 1995, Basal forebrain cholinergic lesions disrupt increments but not decrements in conditioned stimulus processing, J Neurosci source
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- cites [21] Bao, 2001, Cortical remodelling induced by activity of ventral tegmental dopamine neurons, Nature source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
The brain and the world mutually shape each other.
“The brain makes the world and the world makes the brain.30”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] Clark, 2013, Whatever next? Predictive brains, situated agents, and the future of cognitive science, The Behav Brain Sci source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Many examples exist in which Bayesian models can account for neural data.
“There have been many examples in which Bayesian models can account for the neural data.27”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] Deneve, 2008, Bayesian spiking neurons II: learning, Neural Comput source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
A patient may experience delusions because of a disruption in either the prediction-error or attentional-salience system.
“A patient might experience delusions because of a disruption to either system.10”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Corlett, 2010, Toward a neurobiology of delusions, Prog Neurobiol source
Not checked in the free run.
- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Bayesian methods can analyse data even if the neural or cognitive system that produced the data is not necessarily Bayesian.
“That is, Bayesian methods can be used to analyse data without the neural or cognitive system that engender those data necessarily being Bayesian.26”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] Bowers, 2012, Bayesian just-so stories in psychology and neuroscience, Psychol Bull source
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- Interpretationcitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Such examples are subtly different from proof of how the system works.
“However, such examples are subtly different from proof of how the system works.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] Deneve, 2008, Bayesian spiking neurons II: learning, Neural Comput source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
In a 2011 study, participants who received D-cycloserine after cognitive behavioural therapy showed greater adoption of new explanatory beliefs than those who received placebo.
“In a 2011 investigation 24 , participants given D-cycloserine (which boosts NMDA receptor function) after cognitive behavioural therapy showed an enhanced adoption of new explanatory beliefs compared with those who received placebo.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] Gottlieb, 2011, D-cycloserine facilitation of cognitive behavioral therapy for delusions in schizophrenia, Schizophr Res source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Disruptions to prediction error and attentional salience attribution can appear behaviourally as aberrant learning and ultimately delusional beliefs.
“Disruptions to both processes (prediction error and attentional salience attribution) can manifest in behaviour as aberrant learning and, ultimately, delusional beliefs.10”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Corlett, 2010, Toward a neurobiology of delusions, Prog Neurobiol source
Not checked in the free run.
- Interpretationcitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
A good fit of a Bayesian model to data does not imply that the system works in a Bayesian way.
“And even if a Bayesian model provides a good fit for the data generated, that does not mean that the system functions in a Bayesian manner.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] Bowers, 2012, Bayesian just-so stories in psychology and neuroscience, Psychol Bull source
Not checked in the free run.
- Premisecitation
Computational psychiatry has antecedents in cognitive neuropsychiatry.
“Computational psychiatry has its antecedents in cognitive neuropsychiatry. 2”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [2] Halligan, 2001, Cognitive neuropsychiatry: towards a scientific psychopathology, Nat Rev Neurosci source
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- Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Neurophysiological recording in awake, behaving monkeys supported the predicted split in glutamate receptor signalling, with AMPA for bottom-up and NMDA for top-down processing.
“One prediction of the Bayesian model of psychosis that was borne out by the data is the division of labour in glutamate receptor signalling between bottom-up (AMPA) and top-down (NMDA).This prediction was supported with neurophysiological recording in awake, behaving monkeys.28”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [28] Self, 2012, Different glutamate receptors convey feedforward and recurrent processing in macaque V1, Proc Natl Acad of Sci USA source
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- Rests on an assumption: The physical anatomy of the brain implements Bayesian mechanisms of top-down prediction of sensory inputs and bottom-up prediction errors.
- May combine two claims. 10
- Classified as probably not a substantial claim. 3
May combine two claims.
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessPredictive coding theory holds that the brain aims to predict the world and improve its predictions by reducing errors.
“predictive coding theory, which asserts that the brain strives to predict the world and optimise its predictions by minimising errors.8”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Friston, 2009, The free-energy principle: a rough guide to the brain?, Trends Cogn Sci source
Not checked in the free run.
May combine two claims.
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessPerturbed dopamine transmission is related to psychosis through disrupted learning mechanisms, linking psychotic experiences to neurochemistry.
“relating perturbed dopamine transmission to psychosis via disrupted learning mechanisms, [12][13][14] thus linking psychotic experiences to neurochemistry.10”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [12] Kapur, 2003, Psychosis as a state of aberrant salience: a framework linking biology, phenomenology, and pharmacology in schizophrenia, Am J Psychiatry source
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- cites [13] Miller, 1976, Schizophrenic psychology, associative learning and the role of forebrain dopamine, Med Hypotheses source
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- cites [14] Gray, 1991, The neuropsychology of schizophrenia, Behav Brain Sci source
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Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationcitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessThe JTC bias may arise from altered noise in decision making.
“For example, the JTC bias might arise because of altered noise in decision making.7”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] Moutoussis, 2011, Bayesian modelling of Jumping-to-Conclusions bias in delusional patients, Cogn Neuropsychiatry source
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May combine two claims.
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessIn early psychosis, the world becomes strange and unpredictable, with complex, distressing, and socially isolating experiences.
“In the early phases of psychosis, the world becomes a strange, unpredictable place, with complex, distressing, and socially isolating experiences.9”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] Gross, 1972, Sensory disorders in schizophrenia, Arch Psychiatr Nervenkr source
Not checked in the free run.
May combine two claims.
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessPredictive coding includes top-down signalling through NMDA glutamate receptors and bottom-up prediction-error signalling through AMPA receptors.
“In simplified terms, predictive coding includes top-down signalling via N-methyl-D-aspartate (NMDA) glutamate receptors and bottom-up (prediction error) signalling via α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptors.15”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [15] Friston, 2005, A theory of cortical responses, Philos Trans R Soc Lond B Biol Sci source
Not checked in the free run.
May combine two claims.
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessThe brain infers the causes of its inputs from experience and tests those inferences by predicting future inputs.
“The brain infers the causes of its inputs on the basis of such experience and tests its inferences by making predictions about ensuing inputs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Friston, 2009, The free-energy principle: a rough guide to the brain?, Trends Cogn Sci source
Not checked in the free run.
May combine two claims.
Interpretationcitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessThe JTC bias is not specific, and on its own it has very limited explanatory value.
“However, although the bias is invoked in this way to explain delusions, it is not specific 6 and, alone, its explanatory value is very restricted.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] Wittorf, 2012, Specificity of jumping to conclusions and attributional biases: a comparison between patients with schizophrenia, depression, and anorexia nervosa, Cogn Neuropsychiatry source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationcitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessBayesian analysis should not be confused with Bayesian processing.
“So, Bayesian analysis should not be conflated with Bayesian processing.26”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [26] Bowers, 2012, Bayesian just-so stories in psychology and neuroscience, Psychol Bull source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable).
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessThe brain is embedded in an environment that should be described in social and environmental terms.
“The brain is embedded in an environment that should be described in terms that include the social and the environmental.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] Clark, 2013, Whatever next? Predictive brains, situated agents, and the future of cognitive science, The Behav Brain Sci source
Not checked in the free run.
May combine two claims.
Interpretationcitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessThe balance between extinction and reconsolidation of memories may be disrupted in people with psychosis, with less extinction learning and possible strengthening of beliefs during extinction trials.
“This balance between extinction and reconsolidation of memories might be disrupted in people with psychosis, such that there is reduced extinction learning and possibly even belief-strengthening on extinction trials.22”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22] Corlett, 2009, Why do delusions persist?, Front Hum Neurosci source
Not checked in the free run.
- Rests on an assumption: Delusions, though fixed and resistant to contradictory evidence, can expand to incorporate such evidence in a way that strengthens the delusional belief rather than abandoning it.
May combine two claims.
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessIn animal conditioning, pairings of a stimulus with a salient event teach the animal that the stimulus predicts the event; presenting the stimulus without the event produces extinction learning.
“when an animal is exposed to pairings between an environmental stimulus (eg, a tone) and a salient event (eg, an electric shock), it learns that the tone predicts the shock.If the tone is then presented in the absence of shock, extinction learning occurs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Eisenhardt, 2007, Extinction learning, reconsolidation and the internal reinforcement hypothesis, Neurobiol Learn Mem source
Not checked in the free run.
May combine two claims.
PremisecitationUnlike cognitive neuropsychiatry, computational psychiatry formally specifies the processes and parameters by which they deviate from healthy processing, making it both qualitative and quantitative.
“However, computational psychiatry includes a formal specification of the processes and the parametric means through which they deviate from healthy processing; in short, unlike cognitive neuropsychiatry, computational psychiatry is both qualitative (which parameters change?) and quantitative (by how much?). 1”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] Montague, 2012, Computational psychiatry, Trends Cogn Sci source
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May combine two claims.
Premisecitation§ Computational psychiatry: a Rosetta Stone linking the brain to mental illnessWithin predictive coding, prediction error may lead to an updated belief or may be suppressed.
“This characteristic can be considered within the predictive coding framework, where prediction error might lead to an updated belief or might be suppressed.22”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22] Corlett, 2009, Why do delusions persist?, Front Hum Neurosci source
Not checked in the free run.
- Rests on an assumption: Delusions, though fixed and resistant to contradictory evidence, can expand to incorporate such evidence in a way that strengthens the delusional belief rather than abandoning it.
The paper relies on this without support.
Delusions, though fixed and resistant to contradictory evidence, can expand to incorporate such evidence in a way that strengthens the delusional belief rather than abandoning it.
“One puzzling characteristic of delusions is that, although they are fixed and impervious to contradictory evidence, they can nevertheless show remarkable elasticity, expanding to incorporate contradictory evidence in such a way that the delusional belief is strengthened rather than relinquished.”
The paper relies on this without support.
The physical anatomy of the brain implements Bayesian mechanisms of top-down prediction of sensory inputs and bottom-up prediction errors.
“The physical anatomy of the brain embodies Bayesian mechanisms of top-down prediction of sensory inputs and bottom-up prediction errors.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The separate but complementary findings from the ground-truth and computational analyses support the value of computational methods for understanding neurodevelopmental and psychiatric conditions and informing clinical practice.
“The distinct but complementary results provided by the ground truth and computational levels of analysis in our study underwrite the utility of computational approaches in better understanding neuro-developmental and psychiatric conditions with the aim of influencing clinical practice 47–49.”
From Adults with autism overestimate the volatility of the sensory environment (2017) · cites this paper as [48]
The paper’s opening and concluding discussions (page numbers not provided) argue that computational psychiatry links levels of analysis, helps explain mental illness, and offers a path toward clinical use. It does not specifically address neurodevelopmental conditions. The ground-truth and computational results are identified as findings of the citing authors’ own study, not findings attributed to this source. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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References 33 · Cited by 105
- Authors
- Corlett, P. R., & Fletcher, P. C.
- Affiliations
- Yale University (US)
- University of Cambridge (GB)
- Corresponding author
- Fletcher, Paul C
- Venue
- The Lancet Psychiatry
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 1
- Issue
- 5
- Pages
- 399-402
- ISSN
- 2215-0366 (ISSN-L)
- OpenAlex
- W1986402015
- Semantic Scholar
- 6b4fda3057d4d6dd99d791f18218ab6bc29606b7
- Cited by
- 105
- References
- 33
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 10 · 2023: 11 · 2022: 8 · 2021: 16 · 2020: 12 · 2019: 4 · 2018: 9 · 2017: 10 · 2016: 13 · 2015: 4
- FWCI
- 5.01
- Type
- journal article
- Language
- en
- Open access
- Locations
- The Lancet Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Funders
- Wellcome Trust (095692, 093875)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Keywords
- computational psychiatry, psychosis, cognitive modeling, reinforcement learning, neurochemistry, pharmacological interventions, cognitive intervention, social interaction, brain-behavior relationship, cognitive neuropsychiatry
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