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Depression: A Decision-Theoretic Analysis
Open accessAbstract
The manifold symptoms of depression are common and often transient features of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioral implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.
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“From a dynamical systems perspective, this corresponds to a pathological attractor state in neural activity (47, 48).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [47]
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“Bipolar disorder, marked by oscillations between manic (or hypo-manic) and depressive states, can be conceptualised in predictive coding terms as an instability in how the brain regulates precision across different states (47, 53).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [47]
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“Efforts in computational psychiatry have sought to characterize changes in decision-making processes in the context of depression and have revealed dysfunction in the processes underlying model-based decision-making (for review see ref. 55).”
From Paying attention to attention in depression (2019) · cites this paper as [55]
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Metadata
- Authors
- Huys, Q. J. M., Daw, N. D., & Dayan, P.
- Affiliations
- University of Zurich (CH)
- New York University (US)
- Gatsby Computational Neuroscience Unit (GB)
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 2015-07-08
- Volume
- 38
- Issue
- 1
- Pages
- 1-23
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2164531443
- Cited by
- 221
- References
- 165
- Citations by year
- 2026: 18 · 2025: 16 · 2024: 24 · 2023: 35 · 2022: 28 · 2021: 20 · 2020: 22 · 2019: 15 · 2018: 14 · 2017: 14 · 2016: 8 · 2015: 7
- FWCI
- 1.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- Wellcome Trust (091593)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- depression, decision-making, Bayesian decision theory, model-based reasoning, pessimism, state estimation, computational psychiatry, adaptive behavior
- MeSH terms
- Bayes Theorem, Decision Making, Decision Theory, Depression, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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