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Discretization of continuous features by human learners
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Discretization of continuous features by human learners
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ETD_2137
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051773
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eng
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theses
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Psychology
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Learning, Psychology of
Abstract
Natural features are often continuous, but many models of human learning and categorization involve discrete-valued (e.g. Boolean) features. Discretization is well-known to be beneficial in machine learning, leading to faster and sometimes more accurate learning. Yet there has been little research on how human learners discretize continuous features. This dissertation investigates human discretization, focusing on two specific areas of inquiry. First is the hypothesis that discretization of a continuous parameter depends on the shape of the probability distribution underlying it, and principally on the presence of “modes” or separable peaks in the distribution. The second hypothesis is that humans create clear distinctions between discretized feature values, rather than probabilistic boundaries.
Subjects were presented with items that had feature values drawn from a mixture of Gaussian distributions, and a free sorting task was used to assess whether subjects spontaneously discretized the feature in a way that related to the underlying mixture. The relative locations of the two component Gaussians, their separation as measured by Cohen’s d (the ratio of the distance between the components’ means to their standard deviations), and the number of items drawn from the overall mixture were varied. Each of these factors influenced the way subjects discretized the features, while further analysis showed that the estimated mixtures were more sharply separated (higher Cohen’s d) than the original probability. This study suggests that human featural discretization involves a process akin to the estimation of mixture components in the environment, but that the separation among the components is systematically overestimated to create “cleaner” divisions than are truly present—a phenomenon that might be termed hyperdiscretization.
Subjects were presented with items that had feature values drawn from a mixture of Gaussian distributions, and a free sorting task was used to assess whether subjects spontaneously discretized the feature in a way that related to the underlying mixture. The relative locations of the two component Gaussians, their separation as measured by Cohen’s d (the ratio of the distance between the components’ means to their standard deviations), and the number of items drawn from the overall mixture were varied. Each of these factors influenced the way subjects discretized the features, while further analysis showed that the estimated mixtures were more sharply separated (higher Cohen’s d) than the original probability. This study suggests that human featural discretization involves a process akin to the estimation of mixture components in the environment, but that the separation among the components is systematically overestimated to create “cleaner” divisions than are truly present—a phenomenon that might be termed hyperdiscretization.
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electronic resource
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xi, 61 p. : ill.
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Ph.D.
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Includes bibliographical references (p. 56-60)
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by Cordelia D. Aitkin
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Aitkin
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Cordelia D.
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Cordelia D. Aitkin
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Feldman
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Jacob
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Jacob Feldman
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Gelman
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Rochel
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Rochel Gelman
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Gallistel
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Charles
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Charles R Gallistel
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Pazzani
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Michael
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Michael J Pazzani
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-10
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xx
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Rutgers University Electronic Theses and Dissertations
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T3XW4K00
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ETD doctoral
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The author owns the copyright to this work.
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Copyright protected
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Open
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Aitkin
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Cordelia
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Cordelia Aitkin
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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