{
  "abstract": "To discover computational mechanisms of cognition, models typically specify a fixed psychological architecture, treating individual differences as variations within a predefined parameterization. Here, we take a different approach by using the population structure of individual differences to directly drive model discovery. We develop a low-rank neural network that uses this `population geometry' to learn shared computational components and participant-specific weights from trial-level choices, allowing cross-subject variation to constrain the model's architecture. Applied to perceptual decision-making in the Bernoulli Clicks Task across primary and replication datasets, out-of-sample prediction reveals a six-dimensional representation for individual differences: two continuous dimensions of temporal evidence weighting and two of choice-outcome history dependence, alongside participant-specific side bias and signal-to-noise ratio. This low-dimensional organization replicated in an independent dataset. Together, these findings reveal that seemingly disparate decision heuristics reflect different weightings over a small set of shared computational components, and establish a principled methodology for using behavioral variability to discover the mechanistic architecture of cognition.",
  "abstract_shown": true,
  "affiliations": [
    "Georgia Institute of Technology"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. Xue"
    },
    {
      "name": "S. F. Feng"
    },
    {
      "name": "H.-D. Xiong"
    },
    {
      "name": "S. Wang"
    },
    {
      "name": "R. C. Wilson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7t48t5k1vscs0.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.04.764343",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764343v1.full",
  "id": "7t48t5k1vscs0",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764343v1",
  "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": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764343v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7t48t5k1vscs0.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:21:04Z",
      "native_id": "10.64898/2026.11.04.764343",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764343v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Individual differences reveal shared computational structure in perceptual decision-making",
  "title_text": "Individual differences reveal shared computational structure in perceptual decision-making",
  "type": "preprint",
  "updated_at": "2026-11-08T00:21:02Z",
  "url": "https://research.draconitic.com/paper/7t48t5k1vscs0",
  "venue": "bioRxiv",
  "versions": []
}