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{
    "pk": 32740,
    "title": "Learning, Development, and Nativism: Connectionist Implications",
    "subtitle": null,
    "abstract": "Fedforward neural network models of cognitive development are reviewed within the framework of a functional distinction between learning and development. This analysis suggests that static architecture networks implement a learning theory, whereas generative architecture networks combine learning and development. Both types of networks are then evaluated m terms of genetic costs. Within a levels-of-innateness framework, generative architectures are viewed as more plausible than static ones. Static architecture networks appear to implement a form of nativistic elicitation.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Long Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/6n60x5td",
    "frozenauthors": [
        {
            "first_name": "Sylvain",
            "middle_name": "",
            "last_name": "Sirois",
            "name_suffix": "",
            "institution": "Department of Psychology, McGill University",
            "department": ""
        },
        {
            "first_name": "Thomas",
            "middle_name": "R.",
            "last_name": "Shultz",
            "name_suffix": "",
            "institution": "Department of Psychology, McGill University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1999-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/32740/galley/23802/download/"
        }
    ]
}