ArticleChronic stress (Thousand Oaks, Calif.)
Applying Reinforcement Learning to Rodent Stress Research.
Article in Chronic stress (Thousand Oaks, Calif.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Computational modeling reveals cognitive processes in simple rodent depression tests.Cell reports methods · 2025Article
- Psilocybin restrains activity-based anorexia in female rats by enhancing cognitive flexibility: contributions from 5-HT1A and 5-HT2A receptor mechanisms.Molecular psychiatry · 2024Article
- Competitive and cooperative games for probing the neural basis of social decision-making in animals.Neuroscience and biobehavioral reviews · 2023Review
- Early-life stress biases responding to negative feedback and increases amygdala volume and vulnerability to later-life stress.Translational psychiatry · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Rodent models are an invaluable tool for studying the pathophysiological mechanisms underlying stress and depressive disorders. However, the widely used behavioral assays to measure depressive-like states in rodents have serious limitations. In this commentary, we suggest that learning tasks, particularly those that can be analyzed with the framework of reinforcement learning, are ideal for assaying reward processing deficits relevant to depression. The key advantages of these tasks are their repeatable, quantifiable nature and the link to clinical studies. By optimizing the behavioral readout of stress-induced phenotypes in rodents, a reinforcement learning-based approach may help bridge the translational gap and advance antidepressant discovery.
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Registered trials
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