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{
    "pk": 26363,
    "title": "A Comparative Evaluation of Approximate Probabilistic Simulation and DeepNeural Networks as Accounts of Human Physical Scene Understanding",
    "subtitle": null,
    "abstract": "Humans demonstrate remarkable abilities to predict physicalevents in complex scenes. Two classes of models for physicalscene understanding have recently been proposed: “IntuitivePhysics Engines”, or IPEs, which posit that people make pre-dictions by running approximate probabilistic simulations incausal mental models similar in nature to video-game physicsengines, and memory-based models, which make judgmentsbased on analogies to stored experiences of previously en-countered scenes and physical outcomes. Versions of the lat-ter have recently been instantiated in convolutional neural net-work (CNN) architectures. Here we report four experimentsthat, to our knowledge, are the first rigorous comparisonsof simulation-based and CNN-based models, where both ap-proaches are concretely instantiated in algorithms that can runon raw image inputs and produce as outputs physical judg-ments such as whether a stack of blocks will fall. Both ap-proaches can achieve super-human accuracy levels and canquantitatively predict human judgments to a similar degree,but only the simulation-based models generalize to novel sit-uations in ways that people do, and are qualitatively consis-tent with systematic perceptual illusions and judgment asym-metries that people show.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "physical scene understanding; neural network;analysis by synthesis; simulation engine; blocks world"
        }
    ],
    "section": "Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/4bd5068b",
    "frozenauthors": [
        {
            "first_name": "Renqiao",
            "middle_name": "",
            "last_name": "Zhang",
            "name_suffix": "",
            "institution": "Massachusetts Institute of Technology",
            "department": ""
        },
        {
            "first_name": "Jiajun",
            "middle_name": "",
            "last_name": "Wu",
            "name_suffix": "",
            "institution": "Massachusetts Institute of Technology",
            "department": ""
        },
        {
            "first_name": "Chengkai",
            "middle_name": "",
            "last_name": "Zhang",
            "name_suffix": "",
            "institution": "Massachusetts Institute of Technology",
            "department": ""
        },
        {
            "first_name": "William",
            "middle_name": "T.",
            "last_name": "Freeman",
            "name_suffix": "",
            "institution": "Massachusetts Institute of Technology",
            "department": ""
        },
        {
            "first_name": "Joshua",
            "middle_name": "B.",
            "last_name": "Tenenbaum",
            "name_suffix": "",
            "institution": "Massachusetts Institute of Technology",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2016-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/26363/galley/15999/download/"
        }
    ]
}