{
  "abstract": "Human adults show a number bias in magnitude perception, weighting numerical information more heavily than continuous magnitudes like area. However, developmental research suggests this bias is not innate: young children show an area bias that reverses to a number bias only later in development. What drives this shift? Here we use deep neural networks as a tool for testing computational sufficiency, asking whether learning to estimate number is by itself enough to reorganize how a visual system weights magnitude. We trained ResNeXt-50 networks on an enumeration task (labeling the number of dots in an image, an operationalization of a functional outcome of counting) and measured representational bias by linearly decoding number versus cumulative area from penultimate-layer features, with the two dimensions decorrelated by design. Untrained networks showed a robust area bias, with area more decodable than number. Critically, networks trained to estimate number showed a reversal, with number becoming substantially more decodable than area, and this shift emerged after a single epoch of training. The effect was not a trivial consequence of training on a dimension: a matched control trained to estimate area did not produce the mirror-image effect, because enumeration training actively suppressed area information rather than merely amplifying number. Principal component analysis confirmed a reorganization of representational geometry, with numerosity becoming the dominant axis of variation (r² = 0.95) and area variance strongly reduced. These findings provide a computational proof-of-concept that learning to enumerate can restructure magnitude representations, suggesting that the perceptual primacy of number in adults may be partly a consequence, rather than a precondition, of symbolic number acquisition.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Lauren S Aulet"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7z7q47ttm73ck.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/356xd_v4",
  "fulltext_url": null,
  "id": "7z7q47ttm73ck",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/356xd_v4/",
  "language": null,
  "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": [
    [
      "bepress",
      "Cognitive Neuroscience"
    ],
    [
      "bepress",
      "Cognitive Psychology"
    ],
    [
      "bepress",
      "Computational Neuroscience"
    ],
    [
      "bepress",
      "Developmental Psychology"
    ],
    [
      "bepress",
      "Neuroscience"
    ],
    [
      "bepress",
      "Perception"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "openalex",
      "2613|Cognitive and developmental aspects of mathematical skills"
    ]
  ],
  "openalex": "W7219852046",
  "pdf_url": "https://osf.io/356xd/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7z7q47ttm73ck.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:56Z",
      "native_id": "356xd",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/356xd_v4/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Cognitive Neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Learning to Enumerate Reorganizes Magnitude Representations",
  "title_text": "Learning to Enumerate Reorganizes Magnitude Representations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:56Z",
  "url": "https://research.draconitic.com/paper/7z7q47ttm73ck",
  "venue": "PsyArXiv",
  "versions": []
}