Searching for an anchor in an unpredictable world: A computational model of obsessive-compulsive disorder
Open accessAbstract
In this article, we develop a computational model of obsessive-compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients' own actions and the unfolding of possible events. Clinically, this corresponds both to patients' difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model, including a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e., checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and overresponsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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In OCD, excessive precision-weighting of prediction errors, especially in the realm of threat or danger predictions, is present.
“Computationally, this maps to excessive precision-weighting of prediction errors, especially in the realm of threat or danger predictions (56, 57).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [56]
The “Reward prediction errors in OCD” section proposes increased weighting of prediction errors in OCD, while the “High transition uncertainty” section considers this across sensory, thought-related, and reward domains. The paper acknowledges that OCD often has threat-related content, but it does not say that prediction-error weighting is especially elevated for threat or danger predictions. Page numbers were not available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredComputational models suggest that increasing the precision of certain prediction errors can produce OCD-like repetitive checking behaviour as the system tries to resolve an endless stream of perceived mismatches.
“Computational models suggest that increasing the precision of certain prediction errors can produce OCD-like repetitive checking behaviour as the system tries to resolve an endless stream of perceived mismatches (56, 58).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [56]
The section on compulsions says the model produces highly weighted prediction errors and compulsive responses to perceived mismatches; the simulations also show checking can persist under high transition uncertainty. But the model increases uncertainty about action-dependent transitions, not the precision of prediction errors. The paper explicitly distinguishes its mechanism from increasing sensory precision. (LLM, conf=0.86)
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References 157 · Cited by 89
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- Authors
- Fradkin, I., Adams, R. A., Parr, T., Roiser, J. P., & Huppert, J. D.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Wellcome Centre for Human Neuroimaging (GB)
- University College London (GB)
- Corresponding author
- Fradkin, Isaac
- Venue
- Psychological Review
- Published
- 2020-10
- Volume
- 127
- Issue
- 5
- Pages
- 672-699
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W3008435479
- Cited by
- 89
- References
- 157
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 17 · 2023: 13 · 2022: 14 · 2021: 18 · 2020: 3
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Israel Science Foundation (1698/15)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Obsessive-Compulsive Spectrum Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Clinical Psychology › Psychoanalysis and Psychopathology Research
- Keywords
- obsessive-compulsive disorder, computational model, Bayesian brain, uncertainty, state transitions, prediction, checking behavior, indecision, goal-directed behavior, harm avoidance, not just right experiences, sensory hypersensitivity, compulsive behavior
- MeSH terms
- Bayes Theorem, Computer Simulation, Emotions, Habits, Humans, Models, Psychological, Obsessive-Compulsive Disorder, Reproducibility of Results, Uncertainty
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