{
  "abstract": "Larceny imposes profound societal and economic burdens; however, punitive judicial measures frequently fail to deter recidivism. The neurobehavioral mechanisms driving habitual offending, whether instrumental or kleptomanic, in theft recidivists remain poorly understood. In this study, we investigated explore-exploit decision-making and underlying reinforcement learning architectures in theft recidivists with a 4-arm bandit task while prefrontal cortex (PFC) hemodynamics were continuously monitored using functional near-infrared spectroscopy (fNIRS). Model-free behavioral analyses revealed that non-kleptomanic (TR-K) but not kleptomanic (TR+K) theft recidivists accumulated significantly higher cumulative regret and made fewer optimal choices compared to control individuals without criminal records (CT). Model-based analyses through Variational Bayesian Analysis identified Q-learning with decay model as the optimal computational fit. Parameter extraction using the model demonstrated that the TR-K group exhibited a lower learning rate than CT and TR+K groups, indicating a learning deficit in updating action values following environmental feedback. fNIRS tracking of trial-by-trial latent reinforcement variables revealed that while PFC activity was modulated by these variables, group differences were characterized by static baseline hemodynamic shifts rather than rewirings of value-tracking neural circuits. These findings suggest that non-kleptomanic recurrent thefts may be associated with impaired reinforcement learning mechanisms, challenging current punitive deterrence models.",
  "abstract_shown": true,
  "affiliations": [
    "Kyoto University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Y. Goto"
    },
    {
      "name": "S. Yoshino"
    },
    {
      "name": "C. Kita"
    },
    {
      "name": "M. Won"
    },
    {
      "name": "Y.-A. Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9qy3qbyss4x8g.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.11.750836",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.11.750836v1.full",
  "id": "9qy3qbyss4x8g",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.11.750836v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Neural and Behavioral Psychology Studies"
    ]
  ],
  "openalex": "W7213456309",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.11.750836v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9qy3qbyss4x8g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:43Z",
      "native_id": "10.64898/2026.09.11.750836",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.11.750836v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Impaired Reinforcement Learning Underlying Explore-Exploit Decision Making in Theft Recidivists",
  "title_text": "Impaired Reinforcement Learning Underlying Explore-Exploit Decision Making in Theft Recidivists",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:43Z",
  "url": "https://research.draconitic.com/paper/9qy3qbyss4x8g",
  "venue": "bioRxiv",
  "versions": []
}