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{
    "pk": 28469,
    "title": "Frequency Effects in Decision-Making are Predicted by Dirichlet Probability\nDistribution Models",
    "subtitle": null,
    "abstract": "Frequency of reward and average reward value are two types\nof reward information we utilize when making decisions\nbetween two alternative options. Often, these two pieces of\ninformation coincide with the highest value option, however,\nwhen a slightly less valuable option is presented more\nfrequently, standard reinforcement learning models such as the\nDelta model can make incorrect predictions. This paper\nexplores the discrepancy in these predictions by way of\nsimulating relevant behavioral tasks with the Delta model, the\nDecay model, and a novel Bayesian model based on the\nDirichlet distribution. We then compare model predictions to\nbehavioral data from some of the same tasks that were\nsimulated. The Delta model provides a poor fit to the data for\neach of the three presented tasks when compared to the Decay\nmodel and the two Bayesian learning models, because it\npredicts a bias toward options with higher average reward,\nwhile the Decay and Bayesian models predict a bias toward\nreward frequency. The Decay and Bayesian models show a\ndistinct similarity in prediction and fits to the data for most of\nthe tasks. This is because both models predict a bias toward\nreward frequency rather than average reward magnitude,\ndespite different computational formalisms. However, we also\nnote some interesting discrepancies between the Decay and\nBayesian models which will show that in some cases, the\nfrequency of reward may be more important than the reward\nvalue.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Frequency Effect; Reinforcement Learning;\nBayesian Learning"
        }
    ],
    "section": "Papers with Oral Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/3w97p2bq",
    "frozenauthors": [
        {
            "first_name": "Astin",
            "middle_name": "",
            "last_name": "Cornwall",
            "name_suffix": "",
            "institution": "Texas A&M University",
            "department": ""
        },
        {
            "first_name": "Hilary",
            "middle_name": "",
            "last_name": "Don",
            "name_suffix": "",
            "institution": "Texas A&M University",
            "department": ""
        },
        {
            "first_name": "Darrell",
            "middle_name": "",
            "last_name": "Worthy",
            "name_suffix": "",
            "institution": "Texas A&M 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/28469/galley/18340/download/"
        }
    ]
}