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Sensitivity to shape differences along morph-spaces
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Destler, Nathan. Sensitivity to shape differences along morph-spaces. Retrieved from https://doi.org/doi:10.7282/T3NG4SZD

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TitleSensitivity to shape differences along morph-spaces
NameDestler, Nathan (author); Feldman, Jacob (chair); Singh, Manish (internal member); Michel, Melchi (internal member); Rutgers University; Graduate School - New Brunswick
Date Created2016
Other Date2016-10 (degree)
SubjectPsychology
Extent1 online resource (vi, 30 p. : ill.)
DescriptionWe investigated the dimensions defining mental shape space, by measuring shape discrimination thresholds along "morph-spaces" defined by pairs of shapes. Given any two shapes, one can construct a morph-space by taking weighted averages of their boundary vertices (after normalization), creating a continuum of shapes ranging from the first shape to the second. Previous studies of morphs between highly familiar shape categories (e.g. truck and turkey) have shown elevated discrimination at the category boundary, reflecting a kind of "categorical perception" in shape space. However, these findings were restricted to known object shapes. Here, we use this technique to explore implicit categorical boundaries in spaces of unfamiliar shapes, where categories are defined not by familiar named types, but by the underlying "generative" structure of mental shape space. We further explore how probabilistic skeletal models of shape may explain discrimination and categorization of these unfamiliar shapes. In this study, subjects were shown two shapes at nearby points along a morph-space, and asked to judge whether they were the same or different, with an adaptive procedure used to estimate discrimination thresholds at each point along the morph-space. We targeted several potentially important categorical distinctions, such one- vs. two-part shapes, two- vs. three-part shapes, changes in symmetry structure, and other "qualitative" distinctions. The results show strong consistency between subjects. Sensitivity (1/difference threshold) is predicted by using a Bayesian probabilistic skeletal model to compute the probability of the standard shape being generated by the comparison shape's generative model, and vice versa. The results show that discrimination thresholds are not uniform over shape spaces. Instead, the results are consistent with the model, suggesting that a probabilistic generative framework drives shape discrimination.
NoteM.S.
NoteIncludes bibliographical references
Noteby Nathan Destler
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T3NG4SZD
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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