ReviewJournal of neurology, neurosurgery, and psychiatry2016
Computational Psychiatry: towards a mathematically informed understanding of mental illness.
Review in Journal of neurology, neurosurgery, and psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
132 citing papers in PubMed.
- Computational mechanisms of learning and forgetting differentiate affective and substance use disorders.Molecular psychiatry · 2026Article
- The recoverability, reliability, and generalizability of reward processing parameters and relation to mental health symptoms.Psychological medicine · 2026Article
- Widely used bandit tasks elicit diverging belief updating phenotypes in healthy adults.Research square · 2026Article
- Learning as the unifying mechanism of psychedelic action.Journal of psychopharmacology (Oxford, England) · 2026Review
- Autistic children sample costly information with increased variability due to inflexible updating.Communications psychology · 2026Article
- TechnologicalNature. Mental health · 2026Article
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- Article
- Addressing low statistical power in computational modelling studies in psychology and neuroscience.Nature human behaviour · 2026Article
- Antidepressant drugs have pharmacological- and time-dependent effects on reinforcement learning in healthy volunteers: An 8 weeks randomized double-blind placebo-controlled study.medRxiv : the preprint server for health sciences · 2026Article
- ESMR: a process framework for calibration dynamics across psychosomatic and psychiatric phenomena.Frontiers in psychiatry · 2026Article
- Gone fishin'… for distinct patterns of belief-updating in late-life worry and rumination.Frontiers in psychiatry · 2026Article
- A Rosetta Stone hypothesis for neurophenomenology: mathematical predictions from predictive processing.Neuroscience of consciousness · 2026Article
- Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD.Frontiers in computational neuroscience · 2026Article
- PAD-S/CSA as a candidate shared representation layer for computational psychotherapy: minimal architecture and a staged validation roadmap.Frontiers in psychiatry · 2026Article
- Anxiety and Beyond: Diversity in Ventral Hippocampus Circuits and Function.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Review
- Computational signatures of uncertainty are reflected in motor cortex excitatory neurochemistry.Nature communications · 2025Article
- A computational approach to understanding effort-based decision-making in depression.Psychological medicine · 2025Article
- A computational approach to understanding effort-based decision-making in depression.bioRxiv : the preprint server for biology · 2025Article
- How mood shapes belief updating bias in depression.Cognitive, affective & behavioral neuroscience · 2025Review
72 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, while avoiding biological reductionism and artificial categorisation. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ('helplessness'), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders, such as Parkinson's disease, and some pitfalls to avoid when applying its methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.