{
  "abstract": "Probabilistic reasoning in the brain has been studied primarily using simple two-choice tasks that are experimentally and theoretically tractable but have unclear relevance to more complex problems. Here we generalize principles from these studies using information geometry, which treats probability distributions (``beliefs'') as points on statistical manifolds whose geometry captures relationships between those beliefs. We introduce an ``information compass,'' a context-dependent, one-dimensional axis along which an arbitrarily complex belief is reinforced or repudiated. Projecting high-dimensional belief updates onto this axis casts even complex decisions into familiar forms of deliberation and commitment used for simpler decisions. Our framework provides a unified account of how biological and artificial systems can use geometric forms of probabilistic reasoning to make decisions across a range of tasks and domains.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. Bosulu"
    },
    {
      "name": "V. Balasubramanian"
    },
    {
      "name": "Z. P. Kilpatrick"
    },
    {
      "name": "K. Josic"
    },
    {
      "name": "J. I. Gold"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bt78cykrdm8vp.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.07.757408",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757408v1.full",
  "id": "bt78cykrdm8vp",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757408v1",
  "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",
      "1702|Bayesian Modeling and Causal Inference"
    ]
  ],
  "openalex": "W7222022001",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757408v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bt78cykrdm8vp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "10.64898/2026.10.07.757408",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757408v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "An Information Compass for Probabilistic Reasoning in the Brain",
  "title_text": "An Information Compass for Probabilistic Reasoning in the Brain",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/bt78cykrdm8vp",
  "venue": "bioRxiv",
  "versions": []
}