Article Instance

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{
    "pk": 33035,
    "title": "Semantic and Associative Priming in a Distributed Attractor Network",
    "subtitle": null,
    "abstract": "A distributed attractor network is trained on an abstract version \nof the task of deriving the meanings of written words. When \nprocessing a word, the network starts from the final activity \npattern of the previous word. Two words are semantically related if they overlap in their semantic features, whereas they \nare associatively related if one word follows the other frequently during training. After training, the network exhibits \ntwo empirical effects that have posed problems for distributed \nnetwork theories: much stronger associative priming than semantic priming, and significant associative priming across an \nintervenmg unrelated item. It also reproduces the empirical \nfindings of greater priming for low-frequency targets, degraded \ntargets, and high-dominance category exemplars.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "17",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/9g64m08p",
    "frozenauthors": [
        {
            "first_name": "David",
            "middle_name": "C.",
            "last_name": "Plaut",
            "name_suffix": "",
            "institution": "Carnegie Mellon University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1995-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/33035/galley/24097/download/"
        }
    ]
}