The DOI matches its Crossref record; 167 of the 167 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,845
REMEMBERING: A STUDY IN EXPERIMENTAL AND SOCIAL PSYCHOLOGY
Bartlett, F. C., & Burt, C. (1933) · British Journal of Educational Psychology · doi:10.1111/j.2044-8279.1933.tb02913.x · Cited by 7,475
Well-being : the foundations of hedonic psychology
Kahneman, D. J., Diener, E. F., & Schwarz, N. (1999) · Cited by 7,472
From inflammation to sickness and depression: when the immune system subjugates the brain
Dantzer, R., O'Connor, J. C., Freund, G. G., Johnson, R. W., & Kelley, K. W. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2297 · Cited by 7,395
(1975) · Medical Entomology and Zoology · Cited by 7,245
Learned helplessness in humans: Critique and reformulation.
Abramson, L. Y., Seligman, M. E., & Teasdale, J. D. (1978) · Journal of Abnormal Psychology · doi:10.1037//0021-843x.87.1.49 · Cited by 7,121
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067
Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008) · Perspectives on Psychological Science · doi:10.1111/j.1745-6924.2008.00088.x · Cited by 5,172
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Research works printed but not linked: 5
- 10.1037/0021-843X.87.1.49 · DOI · Paper page
- Candy M, 2008, Cochrane Database Syst. Rev., 2008, CD006722
- Lewinsohn P, 1979, The Psychobiology of Depressive Disorders: Implications for the Effects of Stress, 291
- Marr D, 1982, Vision
- World Health Organ, 1990, The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines
Cited by
Sorted by citations
Computational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002
Self-citationNeuroscience of apathy and anhedonia: a transdiagnostic approach
Husain, M., & Roiser, J. P. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0029-9 · Cited by 682
A distributional code for value in dopamine-based reinforcement learning
Dabney, W., Kurth-Nelson, Z. L., Uchida, N., Starkweather, C. K., Hassabis, D., Munos, R., & Botvinick, M. (2020) · Nature · doi:10.1038/s41586-019-1924-6 · Cited by 462
LeDoux, J. E., & Daw, N. D. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2018.22 · Cited by 405
Self-citationMood as representation of momentum
Eldar, E., Rutledge, R. B., Dolan, R. J., & Niv, Y. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.07.010 · Cited by 383
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 ›
Are you an author, or do you see a problem with this page? Request a review
