Cited by 233Neural and Behavioral Psychology StudiesNeurotransmitter Receptor Influence on BehaviorAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

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Reinforcement learning in depression: A review of computational research

Chen, Chong, Takahashi, Taiki, Nakagawa, Shin, Inoue, Takeshi, Kusumi, Ichiro·2015·Neuroscience & Biobehavioral Reviews

DOI 10.1016/j.neubiorev.2015.05.005

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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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Authors: Chen, C., Takahashi, T., Nakagawa, S., Inoue, T., & Kusumi, I.
Affiliations: Hokkaido University (JP)
Corresponding author: Chen, Chong
ORCID: Chen, Chong
Nakagawa, Shin
Inoue, Takeshi
Kusumi, Ichiro
Venue: Neuroscience & Biobehavioral Reviews
Publisher: Elsevier BV
Published: 2015-08
Volume: 55
Pages: 247-267
ISSN: 0149-7634 (ISSN-L)
DOI: 10.1016/j.neubiorev.2015.05.005
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Reinforcement learning in depression: A review of computational research

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