ArticleCurrent directions in psychological science2023
Leveraging Decision Science to Characterize Depression.
Article in Current directions in psychological science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Reward pursuit during a translational reward task correlates with anhedonia reductions following rTMS in patients with major depressive disorder.Translational psychiatry · 2026Article
- Higher motivation and pleasure scores predict more reliance on model-free decision making.Cognitive, affective & behavioral neuroscience · 2025Article
- Representation of Anticipated Rewards and Punishments in the Human Brain.Annual review of psychology · 2025Review
- Cross-species translational paradigms for assessing positive valence system as defined by the RDoC matrix.Journal of neurochemistry · 2025Review
- Decomposition of Reinforcement Learning Deficits in Disordered Gambling via Drift Diffusion Modeling and Functional Magnetic Resonance Imaging.Computational psychiatry (Cambridge, Mass.) · 2024Article
- Simulated synapse loss induces depression-like behaviors in deep reinforcement learning.Frontiers in computational neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This brief review examines the potential to use decision science to objectively characterize depression. We provide a brief overview of the existing literature examining different domains of decision-making in depression. Because this overview highlights the specific role of reinforcement learning as an important decision process affected in the disorder, we then introduce reinforcement learning modeling and explain how this approach has identified specific reinforcement learning deficits in depression. We conclude with ideas for future research at the intersection of decision science and depression, emphasizing the potential for decision science to help uncover underlying mechanisms and targets for the treatment of depression.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.