{
  "abstract": "Learning novel behaviors requires balancing previously learned actions with the ability to flexibly adapt to changing reward contingencies. This trade-off is well documented in the division of labor between dorsolateral striatum (DLS), which promotes selection of cached, history-dependent actions, and dorsomedial striatum (DMS), which supports flexible learning as reward contingencies change. Here, we propose policy regularization as a general computational principle for understanding this division. Capacity-limited agents face a fundamental trade-off between maximizing reward and minimizing the cost of deviating from a default policy that caches frequently used action transitions. We formalize this trade-off as a KL-regularized reward objective in which a flexible controller (DMS) incurs a cost for diverging from a history-dependent default (DLS). The resulting optimal policy is a combination of a reward-driven action value, continuously updated by DMS, and a default policy conditioned on action history, cached by DLS. In practice, DLS consolidates the action transitions shaped by DMS's reward-driven value learning, so DLS preserves behaviors that were once optimal even when reward contingencies change, producing robust but inflexible action selection and effectively \"regularizing\" DMS-driven learning. We show that a single model with one shared parameter set and consistent lesion rules provides a unifying explanation for the functional organization of the striatum, reproducing canonical DLS-DMS dissociations in outcome devaluation, serial spatial reversal, skilled action sequencing, and motor sequence execution tasks.",
  "abstract_shown": true,
  "affiliations": [
    "Harvard University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "C. Bhatia"
    },
    {
      "name": "S. J. Gershman"
    },
    {
      "name": "L. Lai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9kfmx2b5cef1n.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.10.750654",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750654v1.full",
  "id": "9kfmx2b5cef1n",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750654v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "2805|Neural and Behavioral Psychology Studies"
    ]
  ],
  "openalex": "W7212429333",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750654v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9kfmx2b5cef1n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:47Z",
      "native_id": "10.64898/2026.09.10.750654",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750654v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Policy regularization as a unifying theory of the striatal division of labor in learning",
  "title_text": "Policy regularization as a unifying theory of the striatal division of labor in learning",
  "type": "preprint",
  "updated_at": "2026-11-03T00:20:31Z",
  "url": "https://research.draconitic.com/paper/9kfmx2b5cef1n",
  "venue": "bioRxiv",
  "versions": []
}