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{
    "pk": 27742,
    "title": "Can Generic Neural Networks Estimate Numerosity Like Humans?",
    "subtitle": null,
    "abstract": "Researchers exploring mathematical abilities have proposed\nthat humans and animals possess an approximate number\nsystem (ANS) that enables them to estimate numerosities in\nvisual displays. Experimental data shows that estimation\nresponses exhibit a constant coefficient of variation (CV: ratio\nof variability of the estimates to their mean) for numerosities\nlarger than four, and a constant CV has been taken as a\nsignature characteristic of the innate ANS. For numerosities up\nto four, however, humans often produce error-free responses,\nsuggesting the presence of estimation mechanisms distinct\nfrom the ANS specialized for this ‘subitizing range’. We\nexplored whether a constant CV might arise from learning in\ngeneric neural networks using widely-used neural network\nlearning procedures. We find that our networks exhibit a flat\nCV for numerosities larger than 4, but do not do so robustly for\nsmaller numerosities. Our findings are consistent with the idea\nthat estimation for numbers larger than 4 may not require innate\nspecialization for number, while also supporting the view that\na process different from the one we model may underlie\nestimation responses for the smallest numbers.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Mathematical cognition"
        },
        {
            "word": "numerical cognition"
        },
        {
            "word": "Neural Networks"
        },
        {
            "word": "development"
        },
        {
            "word": "learning"
        }
    ],
    "section": "Publication-based-Talks",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/1fm93016",
    "frozenauthors": [
        {
            "first_name": "Sharon",
            "middle_name": "Y",
            "last_name": "Chen",
            "name_suffix": "",
            "institution": "Columbia University",
            "department": ""
        },
        {
            "first_name": "Zhenglong",
            "middle_name": "",
            "last_name": "Zhou",
            "name_suffix": "",
            "institution": "John Hopkins University",
            "department": ""
        },
        {
            "first_name": "Mengting",
            "middle_name": "",
            "last_name": "Fang",
            "name_suffix": "",
            "institution": "Beijing Normal University",
            "department": ""
        },
        {
            "first_name": "James",
            "middle_name": "L",
            "last_name": "McClelland",
            "name_suffix": "",
            "institution": "Stanford University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2018-01-01T10:00:00-08:00",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27742/galley/17382/download/"
        }
    ]
}