{
  "abstract": "Base-rate neglect is usually treated as one mistake: giving the prior too little weight. Turning co-occurrences into a judgment takes two base-rate corrections, not one: weighting in the prior (how common the outcome is) and dividing out the cue's own frequency. Under-correcting the prior is classical base-rate neglect. Under-correcting the cue is the cue-density effect, long studied but not usually framed as base-rate neglect's cue-side analogue. We write both corrections as two weights in one Bayesian equation. The dual-factor heuristic, a leading model of covariation judgment, sits where both equal one-half. Response format fixes which weight a study can measure. A graded rating carries the cue-frequency weight directly. A forced choice between two outcomes cancels it, measuring a separate odds-space weight that agrees with it only at full correction. The cancellation is exact, so same-cue choice proportions carry no information about the cue-frequency weight, not even its direction. A choice between two cues keeps the term, and differing cue frequencies are necessary and sufficient for a softmax choice to depend on it. The same weight appears in six learning-and-memory models (in odds space for signal detection), some exactly, others as a limiting or constructed correspondence. When an experiment keeps only the four cells of a cue x outcome contingency table, each of the six model families projects onto one scalar coordinate, which alone cannot separate the accounts free to sit anywhere along it. On a suitable design the two weights are separately estimable: an experimenter can move either weight's regressor without moving the other's. The nested common-weight model excludes a two-coefficient separation by construction. This paper lays out the framework and the rating experiment that tests the separation. A companion paper fits the odds-space cue weight to a colour-flavour dataset.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.05658",
  "author_total": 1,
  "authors": [
    {
      "name": "Adam Y. Shavit"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d4t2c00mee258.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.05658",
  "fulltext_url": null,
  "id": "d4t2c00mee258",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.05658",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1800|Decision-Making and Behavioral Economics"
    ]
  ],
  "openalex": "W7201874266",
  "pdf_url": "https://arxiv.org/pdf/2608.05658",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/d4t2c00mee258.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:08Z",
      "native_id": "2608.05658",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.05658"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Two base rates, two weights: base-rate neglect has a second axis",
  "title_text": "Two base rates, two weights: base-rate neglect has a second axis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:13Z",
  "url": "https://research.draconitic.com/paper/d4t2c00mee258",
  "venue": "arXiv",
  "versions": []
}