The DOI matches its Crossref record; 361 of the 365 references it cites were found in a registry or an index.
Reinforcement learning in depression: A review of computational research
Abstract
Despite being considered primarily a mood disorder, major depressive disorder (MDD) is characterized by cognitive and decision making deficits. Recent research has employed computational models of reinforcement learning (RL) to address these deficits. The computational approach has the advantage in making explicit predictions about learning and behavior, specifying the process parameters of RL, differentiating between model-free and model-based RL, and the computational model-based functional magnetic resonance imaging and electroencephalography. With these merits there has been an emerging field of computational psychiatry and here we review specific studies that focused on MDD. Considerable evidence suggests that MDD is associated with impaired brain signals of reward prediction error and expected value ('wanting'), decreased reward sensitivity ('liking') and/or learning (be it model-free or model-based), etc., although the causality remains unclear. These parameters may serve as valuable intermediate phenotypes of MDD, linking general clinical symptoms to underlying molecular dysfunctions. We believe future computational research at clinical, systems, and cellular/molecular/genetic levels will propel us toward a better understanding of the disease.
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In the citing paper
“People with depression frequently experience anhedonia, which may result in blunted sensitivity to reward [11-15].”
From Examining the effectiveness of gamification in mental health apps for depression: Systematic review and meta-analysis (2021) · cites this paper as [12]
In the citing paper
“Depression generally has been associated with hyposensitivity to rewards, particularly among those with anhedonic symptoms [11-13,16,17].”
From Examining the effectiveness of gamification in mental health apps for depression: Systematic review and meta-analysis (2021) · cites this paper as [12]
In the citing paper
“Their study was consistent with evidence that reward sensitivity and not learning rates are reduced in anhedonic depression (Huys et al., 2013; Chen et al., 2015).”
From The orbitofrontal cortex: reward, emotion and depression (2020) · cites this paper as Chen et al., 2015
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Metadata
- Authors
- Chen, C., Takahashi, T., Nakagawa, S., Inoue, T., & Kusumi, I.
- Affiliations
- Hokkaido University (JP)
- Corresponding author
- Chen, Chong
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2015-08
- Volume
- 55
- Pages
- 247-267
- ISSN
- 0149-7634 (ISSN-L)
- OpenAlex
- W291512641
- Cited by
- 233
- References
- 365
- Citations by year
- 2026: 17 · 2025: 26 · 2024: 34 · 2023: 31 · 2022: 29 · 2021: 19 · 2020: 17 · 2019: 20 · 2018: 10 · 2017: 14 · 2016: 11 · 2015: 4
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Education, Culture, Sports, Science and Technology
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- major depressive disorder, reinforcement learning, computational psychiatry, model-free reinforcement learning, model-based reinforcement learning, reward prediction error, reward sensitivity, endophenotypes, decision-making deficits, cognitive deficits in depression
- MeSH terms
- Animals, Brain, Computer Simulation, Major Depressive Disorder, Humans, Motivation, Reinforcement, Psychology
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