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{
    "pk": 28717,
    "title": "Extracting and Utilizing Abstract, Structured Representations for Analogy",
    "subtitle": null,
    "abstract": "Human analogical ability involves the re-use of abstract, struc-tured representations within and across domains. Here, wepresent a generative neural network that completes analogiesin a 1D metric space, without explicit training on analogy.Our model integrates two key ideas. First, it operates overrepresentations inspired by properties of the mammalian En-torhinal Cortex (EC), believed to extract low-dimensional rep-resentations of the environment from the transition probabil-ities between states. Second, we show that a neural networkequipped with a simple predictive objective and highly generalinductive bias can learn to utilize these EC-like codes to com-pute explicit, abstract relations between pairs of objects. Theproposed inductive bias favors a latent code that consists ofanti-correlated representations. The relational representationslearned by the model can then be used to complete analogiesinvolving the signed distance between novel input pairs (1:3:: 5:? (7)), and extrapolate outside of the network’s trainingdomain. As a proof of principle, we extend the same architec-ture to more richly structured tree representations. We suggestthat this combination of predictive, error-driven learning andsimple inductive biases offers promise for deriving and utiliz-ing the representations necessary for high-level cognitive func-tions, such as analogy.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "abstract structured representations; analogy; neu-ral networks; predictive learning; relational reasoning;"
        }
    ],
    "section": "Papers with Poster Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/2c79n8v2",
    "frozenauthors": [
        {
            "first_name": "Steven",
            "middle_name": "M.",
            "last_name": "Frankland",
            "name_suffix": "",
            "institution": "Princeton University",
            "department": ""
        },
        {
            "first_name": "Taylor",
            "middle_name": "W.",
            "last_name": "Webb",
            "name_suffix": "",
            "institution": "Princeton University",
            "department": ""
        },
        {
            "first_name": "Alexander",
            "middle_name": "A.",
            "last_name": "Petrov",
            "name_suffix": "",
            "institution": "The Ohio State University",
            "department": ""
        },
        {
            "first_name": "Randall",
            "middle_name": "C.",
            "last_name": "O’Reilly",
            "name_suffix": "",
            "institution": "University of California, Davis",
            "department": ""
        },
        {
            "first_name": "Jonathan",
            "middle_name": "D.",
            "last_name": "Cohen",
            "name_suffix": "",
            "institution": "Princeton University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2019-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/28717/galley/18588/download/"
        }
    ]
}