{
  "abstract": "A longstanding goal in metacognition research is the quantification of confidence-related parameters independently from task performance. While there is now a proliferation of generative models of confidence in numerous psychological tasks, the meta-d' model remains a popular approach to estimating metacognitive sensitivity and metacognitive bias within a signal detection theoretic framework. However, as questions in metacognition research increase in complexity, it remains an ongoing challenge to develop extensions of the meta-d' model built to deal with longitudinal, repeated-measures, or other complex experimental designs. Here we introduce the open-source R package hmetad, which allows users to fit the meta-d' model for a wide range of experimental designs using a familiar formula syntax. We also review how the meta-d' model can be validated in a Bayesian workflow which investigates posterior estimates of key parameters. Overall, the hmetad package offers a comprehensive interface to fitting, evaluating, and visualizing signal detection theoretic models of metacognition.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Kevin O'Neill"
    },
    {
      "name": "Stephen Fleming"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4xk9p52ft83g6.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/e3qhz_v1",
  "fulltext_url": null,
  "id": "4xk9p52ft83g6",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/e3qhz_v1/",
  "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 Psychology"
    ],
    [
      "bepress",
      "Computational Modeling"
    ],
    [
      "bepress",
      "Mathematical Psychology"
    ],
    [
      "bepress",
      "Psychometrics"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Statistical Methods"
    ],
    [
      "openalex",
      "2805|Visual perception and processing mechanisms"
    ]
  ],
  "openalex": "W7223934556",
  "pdf_url": "https://osf.io/e3qhz/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4xk9p52ft83g6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:58Z",
      "native_id": "e3qhz",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/e3qhz_v1/"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "bepress:Statistical Methods",
      "subfield": "statistics"
    }
  ],
  "title": "Quantifying metacognition with the hmetad package in R",
  "title_text": "Quantifying metacognition with the hmetad package in R",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:58Z",
  "url": "https://research.draconitic.com/paper/4xk9p52ft83g6",
  "venue": "PsyArXiv",
  "versions": []
}