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{
    "pk": 49258,
    "title": "AI-enhanced semantic feature norms for 786 concepts",
    "subtitle": null,
    "abstract": "Semantic feature norms have been foundational in the study\nof human conceptual knowledge, yet traditional methods face\ntrade-offs between concept/feature coverage and verifiability\nof quality due to the labor-intensive nature of norming studies.\nHere, we introduce a novel approach that augments a dataset\nof human-generated feature norms with responses from large\nlanguage models (LLMs) while verifying the quality of norms\nagainst reliable human judgments. We find that our AI-enhanced\nfeature norm dataset, NOVA: Norms Optimized\nVia AI, shows much higher feature density and overlap\namong concepts while outperforming a comparable human-only\nnorm dataset and word-embedding models in predicting\npeople's semantic similarity judgments. Taken together, we\ndemonstrate that human conceptual knowledge is richer than\ncaptured in previous norm datasets and show that, with proper\nvalidation, LLMs can serve as powerful tools for cognitive\nscience research.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Artificial Intelligence; Concepts and categories; Representation; Semantic memory; Knowledge representation"
        }
    ],
    "section": "Papers with Oral Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/83q779kc",
    "frozenauthors": [
        {
            "first_name": "Siddharth",
            "middle_name": "",
            "last_name": "Suresh",
            "name_suffix": "",
            "institution": "University of Wisconsin-Madison",
            "department": ""
        },
        {
            "first_name": "Kushin",
            "middle_name": "",
            "last_name": "Mukherjee",
            "name_suffix": "",
            "institution": "Stanford University",
            "department": ""
        },
        {
            "first_name": "Tyler",
            "middle_name": "",
            "last_name": "Giallanza",
            "name_suffix": "",
            "institution": "Princeton Neuroscience Institute",
            "department": ""
        },
        {
            "first_name": "Mia",
            "middle_name": "",
            "last_name": "Patil",
            "name_suffix": "",
            "institution": "University of Wisconsin Madison",
            "department": ""
        },
        {
            "first_name": "Xizheng",
            "middle_name": "",
            "last_name": "Yu",
            "name_suffix": "",
            "institution": "Brown University",
            "department": ""
        },
        {
            "first_name": "Jonathan",
            "middle_name": "",
            "last_name": "Cohen",
            "name_suffix": "",
            "institution": "Princeton University",
            "department": ""
        },
        {
            "first_name": "Timothy",
            "middle_name": "T",
            "last_name": "Rogers",
            "name_suffix": "",
            "institution": "University of Wisconsin- Madison",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2025-01-01T10:00:00-08:00",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/49258/galley/37219/download/"
        }
    ]
}