{
  "abstract": "Research into cognitive control has flourished over the last three decades across many areas of the psychological and neural sciences, but problems have recently emerged that raise foundational questions about what cognitive control is, how individual differences should be measured, and the validity of cognitive control as an explanation for variation in behavioral traits and clinical conditions. Here, we outline a novel perspective that is rooted in evidence accumulation models, formal mathematical models that explain how individuals make choices across many contexts. We review model-based work that distinguishes conflict-specific processes, which are selectively engaged to address goal-conflicting information, from task-general processes that facilitate goal-relevant responding irrespective of the presence of conflict. This review reveals an emerging consensus that task-general, but not conflict-specific, processes show validity as coherent individual difference dimensions. We then highlight the computationally-derived measure we call efficiency of evidence accumulation (EEA) that reflects the amplification of goal-relevant information across many tasks and contexts. EEA shows strong validity as a trait-like individual difference dimension, explains individual differences in performance in cognitive control tasks, and shows clear relevance to behavioral traits and clinical conditions in which cognitive control, as traditionally defined, is thought to be impaired. These findings help address multiple theoretical, methodological, and empirical challenges in the study of control processes in cognition, indicate that characterizing processes that enhance goal-relevant responding across contexts is a promising way forward, and suggest important ways in which theories of control’s role in behavioral outcomes could be revised.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Alexander Samuel Weigard"
    },
    {
      "name": "Andrew Heathcote"
    },
    {
      "name": "Chandra Sripada"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cmjph3b9rsdj0.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/ymu52_v2",
  "fulltext_url": null,
  "id": "cmjph3b9rsdj0",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/ymu52_v2/",
  "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",
      "Clinical Neuroscience"
    ],
    [
      "bepress",
      "Clinical Psychology"
    ],
    [
      "bepress",
      "Cognitive Neuroscience"
    ],
    [
      "bepress",
      "Cognitive Psychology"
    ],
    [
      "bepress",
      "Neuroscience"
    ],
    [
      "bepress",
      "Psychiatry"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://osf.io/ymu52/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cmjph3b9rsdj0.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:21:46Z",
      "native_id": "ymu52",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/ymu52_v2/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Cognitive Neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "bepress:Clinical Psychology",
      "subfield": "psychiatry"
    }
  ],
  "title": "Emerging Insights into Cognitive Control from Computational Models of Choice",
  "title_text": "Emerging Insights into Cognitive Control from Computational Models of Choice",
  "type": "preprint",
  "updated_at": "2026-11-06T12:21:46Z",
  "url": "https://research.draconitic.com/paper/cmjph3b9rsdj0",
  "venue": "PsyArXiv",
  "versions": []
}