Article Instance

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{
    "pk": 31808,
    "title": "Artificial Evolution of Syntactic Aptitude",
    "subtitle": null,
    "abstract": "Populations of simple recurrent neural networks were subject to simulations of evolution where the selection criterion was the ability of a network to learn to recognize strings from context free grammars. After a number of generations, networks emerged that use the activation values of the units feeding their recurrent connections to represent the depth of embedding in a string. Networks inherited innate biases to accurately learn members of a class of related context-free grammars, and, while learning, passed through periods during which exposure to spurious input interfered with their subsequent ability to learn a grammar.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Refereed Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/9919k906",
    "frozenauthors": [
        {
            "first_name": "John",
            "middle_name": "",
            "last_name": "Batali",
            "name_suffix": "",
            "institution": "University of California at San Diego",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1994-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/31808/galley/22876/download/"
        }
    ]
}