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    • 2016 Dissertations (MU)
    • 2016 MU dissertations - Freely available online
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    Testing the Bayesian brain hypothesis in visual perception

    Swagman, April
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    [PDF] research.pdf (2.803Mb)
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    Date
    2016
    Format
    Thesis
    Metadata
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    Abstract
    Bayesian ideal observer theory describes perception as an ideal integration of sensory information with prior knowledge to produce optimal responses. Bayesian ideal perception models are popular in sensory domains; however, these models often prove unfalsifiable because of excessive distributional assumptions and post-hoc estimation of participant beliefs. Three visual perception tasks were designed to test Bayesian ideal observer theory under minimal assumptions using the Bayesian Decision Theory framework. Prior distributions of stimuli were specified, likelihoods were manipulated across four stimulus reliability levels, and loss functions were established so that participants could choose posterior point estimates which minimize loss. In each experiment, a Bayesian ideal observer model was fit against a Bayesian posterior matching model (adapted from the probability matching phenomenon) and one or more non-Bayesian mixture models. Results from two experiments in which participants were making location-based judgments overwhelmingly supported the posterior matching models. Data from a third experiment in which participants made estimations about the number of items in an array were fit best by a non-Bayesian mixture model. Overall, Bayesian ideal observer theory was not supported in three experiments of visual perception.
    URI
    https://hdl.handle.net/10355/57274
    https://doi.org/10.32469/10355/57274
    Degree
    Ph. D.
    Thesis Department
    Psychology (MU)
    Rights
    OpenAccess
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • 2016 MU dissertations - Freely available online
    • Psychological Sciences electronic theses and dissertations (MU)

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