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{
    "pk": 26797,
    "title": "A Bayesian Model of Memory for Text",
    "subtitle": null,
    "abstract": "The study of memory for texts has had an long tradition of re-search in psychology. According to most general accounts oftext memory, the recognition or recall of items in a text is basedon querying a memory representation that is built up on the ba-sis of background knowledge. The objective of this paper is todescribe and thoroughly test a Bayesian model of this generalaccount. In particular, we develop a model that describes howwe use our background knowledge to form memories as a pro-cess of Bayesian inference of the statistical patterns that areinherent in a text, followed by posterior predictive inference ofthe words that are typical of those inferred patterns. This pro-vides us with precise predictions about what words will be re-membered, whether veridically or erroneously, from any giventext. We then test these predictions using data from a memoryexperiment using a relatively large sample of randomly chosentexts from a representative corpus of British English.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Bayesian models; Memory; Reconstructive mem-ory; Text memory;"
        }
    ],
    "section": "Talks: Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/2pp181n0",
    "frozenauthors": [
        {
            "first_name": "Mark",
            "middle_name": " ",
            "last_name": "Andrews",
            "name_suffix": "",
            "institution": "Nottingham Trent University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2017-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/26797/galley/16433/download/"
        }
    ]
}