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{
    "pk": 27910,
    "title": "A Case of Divergent Predictions Made by Delta and Decay Rule Learning Models",
    "subtitle": null,
    "abstract": "The Delta and Decay rules are two learning rules used to update\nexpected values in reinforcement learning (RL) models. The\ndelta rule learns average rewards, whereas the decay rule learns\ncumulative rewards for each option. Participants learned to\nselect between pairs of options that had reward probabilities of\n.65 (option A) versus .35 (option B) or .75 (option C) versus\n.25 (option D) on separate trials in a binary-outcome choice\ntask. Crucially, during training there were twice as AB trials as\nCD trials, therefore participants experienced more cumulative\nreward from option A even though option C had a higher\naverage reward rate (.75 versus .65). Participants then decided\nbetween novel combinations of options (e.g, A versus C). The\nDecay model predicted more A choices, but the Delta model\npredicted more C choices, because those respective options had\nhigher cumulative versus average reward values. Results were\nmore in line with the Decay model’s predictions. This suggests\nthat people may retrieve memories of cumulative reward to\ncompute expected value instead of learning average rewards\nfor each option.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Reinforcement Learning"
        },
        {
            "word": "delta rule"
        },
        {
            "word": "decay rule"
        },
        {
            "word": "prediction error"
        },
        {
            "word": "Base rates"
        },
        {
            "word": "probability learning"
        }
    ],
    "section": "Publication-based-Talks",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/3x42k7ks",
    "frozenauthors": [
        {
            "first_name": "Darrell",
            "middle_name": "A",
            "last_name": "Worthy",
            "name_suffix": "",
            "institution": "Texas A&M",
            "department": ""
        },
        {
            "first_name": "A",
            "middle_name": "Ross",
            "last_name": "Otto",
            "name_suffix": "",
            "institution": "McGill University",
            "department": ""
        },
        {
            "first_name": "Astin",
            "middle_name": "C",
            "last_name": "Cornwall",
            "name_suffix": "",
            "institution": "Texas A&M",
            "department": ""
        },
        {
            "first_name": "Hilary",
            "middle_name": "J",
            "last_name": "Don",
            "name_suffix": "",
            "institution": "U of Sydney",
            "department": ""
        },
        {
            "first_name": "Tyler",
            "middle_name": "",
            "last_name": "Davis",
            "name_suffix": "",
            "institution": "Texas Tech University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2018-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/27910/galley/17548/download/"
        }
    ]
}