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    Modelling learning and decision making under information processing constraints

    Author
    Malloy, Tyler
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    180422_Malloy_rpi_0185N_11791.pdf (1.022Mb)
    Other Contributors
    Sims, Christopher Robert; Fajen, Brett R.; Van Heuveln, Bram;
    Date Issued
    2020-12
    Subject
    Cognitive science
    Degree
    MS;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/2642
    Abstract
    This thesis explores the impact of information processing constraints on models of human learning and decision making. This is achieved through altering existing methods within the fields of economic decision making and reinforcement learning, with inspiration from information theory and the rational inattention economic framework. The result is two models, one of human decision making and one of human learning, which seek to represent the way that differences in individual information processing abilities impact learning and decision making. Data from human responses in a learning task is used to compare the accuracy of this model against existing methods. Results from experimentation show that these models achieve a high degree of accuracy while accounting for the impact of differences in information processing capacity utilized by participants during the learning task. These results further the understanding of how cognitive limitations impact human learning and decision making, and suggest ways in which this type of model could potentially benefit current approaches in artificial intelligence, by incorporating more human-like learning strategies.;
    Description
    December 2020; School of Humanities, Arts, and Social Sciences
    Department
    Dept. of Cognitive Science;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.;
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    • RPI Theses Online (Complete)
    • RPI Theses Open Access

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