{
  "abstract": "In this article we consider the use of reinforcement learning (RL) as a formal framework for understanding traditional psychodynamic phenomena. We show how RL can be used to provide new insights as well as normative explanations for key psychodynamic constructs, grounding them in principles of reward maximization and adaptation. We illustrate this by reviewing two recent studies in which simulations of adaptive agents offer new explanations for psychodynamic processes, discuss how RL could account for both similarities as well as individual differences in human psychological profiles, and outline possible future directions for this approach. We conclude that, by providing a computational foundation for psychodynamic concepts, RL may contribute to the development of a more rigorous science of psychological dynamics and their disruption in clinical conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Zack Dulberg"
    },
    {
      "name": "Jonathan D. Cohen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c4fad0yx4s5pr.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/vcnxt_v3",
  "fulltext_url": null,
  "id": "c4fad0yx4s5pr",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/vcnxt_v3/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Clinical Psychology"
    ],
    [
      "bepress",
      "Cognitive Psychology"
    ],
    [
      "bepress",
      "Computational Modeling"
    ],
    [
      "bepress",
      "Computational Neuroscience"
    ],
    [
      "bepress",
      "Emotion"
    ],
    [
      "bepress",
      "Mathematical Psychology"
    ],
    [
      "bepress",
      "Neuroscience"
    ],
    [
      "bepress",
      "Psychiatry"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "3203|Psychoanalysis and Psychopathology Research"
    ]
  ],
  "openalex": "W7214581947",
  "pdf_url": "https://osf.io/vcnxt/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c4fad0yx4s5pr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:17Z",
      "native_id": "vcnxt",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/vcnxt_v1/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "bepress:Clinical Psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "Reinforcement learning as a foundation for psychodynamics",
  "title_text": "Reinforcement learning as a foundation for psychodynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:48Z",
  "url": "https://research.draconitic.com/paper/c4fad0yx4s5pr",
  "venue": "PsyArXiv",
  "versions": []
}