Computational psychiatry
Open accessAbstract
Computational ideas pervade many areas of science and have an integrative explanatory role in neuroscience and cognitive science. However, computational depictions of cognitive function have had surprisingly little impact on the way we assess mental illness because diseases of the mind have not been systematically conceptualized in computational terms. Here, we outline goals and nascent efforts in the new field of computational psychiatry, which seeks to characterize mental dysfunction in terms of aberrant computations over multiple scales. We highlight early efforts in this area that employ reinforcement learning and game theoretic frameworks to elucidate decision-making in health and disease. Looking forwards, we emphasize a need for theory development and large-scale computational phenotyping in human subjects.
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The autonomisation of goals from primary drives is a key feature of higher animals like humans and becomes more evident in pathological conditions.
“The ‘autonomisation’ of goals from primary drives is a key feature of higher animals like humans (Pezzulo and Castelfranchi, 2009) and becomes more evident in pathological conditions, where certain goals such as gambling can become pathological and maladaptive (Montague et al., 2012).”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Montague et al., 2012
The ‘Computational modeling in decision-making’ section (page unavailable) describes addiction as seeking a drug despite acknowledging the damage, supporting the general point that a pursued goal can become maladaptive in a psychiatric condition. It does not identify gambling as an example or say that the separation of goals from primary drives becomes more evident in pathological conditions; the earlier claim that this separation is a feature of higher animals is cited to a different source. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIf a model indicates that a patient's symptoms stem from low precision in a certain circuit, one might choose a drug that boosts that neuromodulator.
“On the pharmacological side, if a model indicates that a patient’s symptoms stem from low precision in a certain circuit (implying maybe inadequate neuromodulatory drive), one might choose a drug that boosts that neuromodulator (1).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [1]
The Mathematical modeling section says biophysical models can predict the effects of medication on neuromodulatory systems. It does not connect low precision in a particular circuit to inadequate neuromodulatory drive or propose choosing a drug to boost that neuromodulator for an individual patient. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThese results underscore the integrative explanatory potential for computational models to reach a more complete understanding of brain function and its alterations.
“More generally, these results underscore the integrative explanatory potential for computational models to reach a more complete understanding of brain function and its alterations [6].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [6]
Computational psychiatry offers a new paradigm by integrating mathematical models with neurobiology to explain psychiatric phenomena in terms of aberrant brain computations.
“Computational psychiatry offers a new paradigm (1–5): by integrating mathematical models with neurobiology, it seeks to explain psychiatric phenomena in terms of aberrant brain computations (6).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [1]
State-space models have been used to simulate bipolar phenomena, where a parameter representing gain or arousal varies over time and pushes the system from one regime to another.
“State-space models have been used to simulate such phenomena, where a parameter representing gain or arousal varies over time and pushes the system from one regime to another (1).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [1]
The Mathematical modeling section discusses biophysical simulation and emergent behaviors, while the mood-disorders section discusses computational models especially of depression. Neither passage describes a state-space simulation of bipolar phenomena or a time-varying gain or arousal parameter that shifts the system between regimes (searched: state-space, bipolar, gain, arousal, and manic-depressive oscillations). (LLM, conf=0.72)
Experimental — accuracy not yet measured
Unlike 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”
From Computational psychiatry: a Rosetta Stone linking the brain to mental illness (2014) · cites this paper as [1]
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“Although its historical roots have rarely been discussed so far, Computational Psychiatry builds on seminal teleological theories of biological (and other) systems that provide fundamental constraints for any attempt of understanding brain function.”
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Computations of uncertainty mediate acute stress responses in humans
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Hot and cold cognition in depression
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Computational psychiatry: towards a mathematically informed understanding of mental illness
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The role of habit in compulsivity
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Computational approaches to psychiatry
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Inferring on the intentions of others by hierarchical Bayesian learning
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Depression: a decision-theoretic analysis
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Association of neural and emotional impacts of reward prediction errors with major depression
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Computational substrates of norms and their violations during social exchange
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Self-citationTwenty-Five Lessons from Computational Neuromodulation
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TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry
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Assessing inter individual differences with task related functional neuroimaging
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Prediction error, ketamine and psychosis: An updated model
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Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
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Deep temporal models and active inference
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Computational nosology and precision psychiatry
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Reduced model-based decision-making in schizophrenia.
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Computational psychiatry: a Rosetta Stone linking the brain to mental illness
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From Treatment to Healing:Envisioning a Decolonial Digital Mental Health
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“In doing so, the field can now avail itself of novel techniques both from machine learning and computational psychiatry (Huys et al. 2011; Montague et al. 2012; Wolfers et al. 2015; Chekroud et al. 2016) to hopefully provide individually valid predictors of differential risk.”
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Reciprocity and alignment: quantifying coupling in dynamic interactions
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Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology
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Unifying treatments for depression: an application of the free energy principle
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Striatal dopamine, reward, and decision making in schizophrenia
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What Underlies Political Polarization? A Manifesto for Computational Political Psychology
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“Computational psychiatry offers a new paradigm (1 – 5): by integrating mathematical models with neurobiology, it seeks to explain psychiatric phenomena in terms of aberrant brain computations (6).”
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- Authors
- Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Montague, P. Read
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 16
- Issue
- 1
- Pages
- 72-80
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC3556822
- OpenAlex
- W4245880239
- Cited by
- 809
- References
- 91
- Citations by year
- 2026: 55 · 2025: 71 · 2024: 61 · 2023: 59 · 2022: 65 · 2021: 50 · 2020: 64 · 2019: 54 · 2018: 77 · 2017: 57 · 2016: 59 · 2015: 52 · 2014: 38 · 2013: 27 · 2012: 18
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- Autism Speaks (AS1947)
- Wellcome Trust (091593, 091188, 088130)
- National Institute on Drug Abuse (R01DA011723)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- computational psychiatry, mental illness, reinforcement learning, game theory, decision-making, computational phenotyping, theory development, cognitive impairment
- MeSH terms
- Mental Disorders, Biological Psychiatry, Brain, Concept Formation, Decision Making, Game Theory, Humans, Models, Neurological, Neurosciences, Reinforcement, Psychology, Computational Biology
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