{
  "abstract": "Effort-based decision making evaluates rewards relative to the effort required to obtain it, an important process of healthy goal-directed motivation and behavior. Computational models provide mechanistic insights underlying choice behavior and potential alterations in neuropsychiatric disorders, including substance use disorders. We applied computational models to effort-based choice behavior to characterize underlying decision processes and if these mechanisms differ by substance use status. Participants completed the Effort Expenditure for Rewards Task, choosing between low- and high-effort options for monetary rewards varying in magnitude and probability. Participants met criteria for no tobacco use (n = 23), current tobacco use disorder (n = 26), former tobacco use disorder (n = 22), and tobacco and opioid use disorder (n = 29). Computational models from two families, Subjective Value and Reinforcement Learning, were fit and compared. Parameters from the best-fitting model underwent principal components analysis and linear discriminant analysis. Temporal difference reinforcement learning model demonstrated greater model evidence and predictive accuracy, indicating better fit to effort-based choice behavior. Principal components analysis revealed meaningful multivariate distinctions: PC1 differentiated all groups except individuals without tobacco use versus individuals with current tobacco use disorder; PC3 distinguished tobacco and opioid use disorder from all other groups. Linear discriminant analysis demonstrated group separation with 84% classification accuracy. A reinforcement learning framework better explained participants’ effort-based choice behavior. Substance use status relates to dynamic behavioral changes (i.e. learning) as measured by the multivariate combination of learning rate, future discounting, and choice temperature.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10827-026-00953-6.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Translational Neuroscience, Wake Forest University School of Medicine, Winston-Salem, NC USA",
    "Neuroscience Graduate Program, Wake Forest University Graduate School of Arts and Sciences, Winston-Salem, NC USA",
    "Department of Psychiatry, University of Arkansas for Medical Sciences, Little Rock, AR USA",
    "Department of Biology, Wake Forest University, Winston-Salem, NC USA",
    "Department of Neurosurgery, Wake Forest University School of Medicine, Winston-Salem, NC USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Kasey P. Spry",
      "orcid": "0000-0002-0822-3108"
    },
    {
      "affs": [
        0
      ],
      "name": "Jazmyne James"
    },
    {
      "affs": [
        2
      ],
      "name": "Alison H. Oliveto"
    },
    {
      "affs": [
        2
      ],
      "name": "Michael Mancino"
    },
    {
      "affs": [
        0,
        3,
        4
      ],
      "name": "Kenneth T. Kishida"
    },
    {
      "affs": [
        0
      ],
      "name": "Merideth A. Addicott"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8f3q8dk2aph93.bib",
  "cited_by": 0,
  "doi": "10.1007/s10827-026-00953-6",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13710678/",
  "id": "8f3q8dk2aph93",
  "issns": [
    "0929-5313",
    "1573-6873"
  ],
  "landing_url": "https://doi.org/10.1007/s10827-026-00953-6",
  "language": "en",
  "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": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2805|Neural and Behavioral Psychology Studies"
    ]
  ],
  "openalex": "W7137873797",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s10827-026-00953-6.pdf",
  "pmcid": "PMC13710678",
  "pmid": "42663917",
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8f3q8dk2aph93.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC13710678",
      "source": "pmc",
      "url": "https://doi.org/10.1007/s10827-026-00953-6"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Neural and Behavioral Psychology Studies",
      "subfield": "neuroscience"
    }
  ],
  "title": "Effort and substance use: differentiating tobacco use through reinforcement learning of effort based decision making",
  "title_text": "Effort and substance use: differentiating tobacco use through reinforcement learning of effort based decision making",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:36Z",
  "url": "https://research.draconitic.com/paper/8f3q8dk2aph93",
  "venue": "Journal of Computational Neuroscience",
  "versions": []
}