Constant curvature modeling of abstract shape representation.

How abstract shape is perceived and represented poses crucial unsolved problems in human perception and cognition. Recent findings suggest that the visual system may encode contours as sets of connected constant curvature segments. Here we describe a model for how the visual system might recode a se...

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Autores principales:	Nicholas Baker, Philip J Kellman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/229ff4227ad745f2b579081f2cb96887
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spelling	oai:doaj.org-article:229ff4227ad745f2b579081f2cb968872021-12-02T20:18:53ZConstant curvature modeling of abstract shape representation.1932-620310.1371/journal.pone.0254719https://doaj.org/article/229ff4227ad745f2b579081f2cb968872021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254719https://doaj.org/toc/1932-6203How abstract shape is perceived and represented poses crucial unsolved problems in human perception and cognition. Recent findings suggest that the visual system may encode contours as sets of connected constant curvature segments. Here we describe a model for how the visual system might recode a set of boundary points into a constant curvature representation. The model includes two free parameters that relate to the degree to which the visual system encodes shapes with high fidelity vs. the importance of simplicity in shape representations. We conducted two experiments to estimate these parameters empirically. Experiment 1 tested the limits of observers' ability to discriminate a contour made up of two constant curvature segments from one made up of a single constant curvature segment. Experiment 2 tested observers' ability to discriminate contours generated from cubic splines (which, mathematically, have no constant curvature segments) from constant curvature approximations of the contours, generated at various levels of precision. Results indicated a clear transition point at which discrimination becomes possible. The results were used to fix the two parameters in our model. In Experiment 3, we tested whether outputs from our parameterized model were predictive of perceptual performance in a shape recognition task. We generated shape pairs that had matched physical similarity but differed in representational similarity (i.e., the number of segments needed to describe the shapes) as assessed by our model. We found that pairs of shapes that were more representationally dissimilar were also easier to discriminate in a forced choice, same/different task. The results of these studies provide evidence for constant curvature shape representation in human visual perception and provide a testable model for how abstract shape descriptions might be encoded.Nicholas BakerPhilip J KellmanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0254719 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nicholas Baker Philip J Kellman Constant curvature modeling of abstract shape representation.
description	How abstract shape is perceived and represented poses crucial unsolved problems in human perception and cognition. Recent findings suggest that the visual system may encode contours as sets of connected constant curvature segments. Here we describe a model for how the visual system might recode a set of boundary points into a constant curvature representation. The model includes two free parameters that relate to the degree to which the visual system encodes shapes with high fidelity vs. the importance of simplicity in shape representations. We conducted two experiments to estimate these parameters empirically. Experiment 1 tested the limits of observers' ability to discriminate a contour made up of two constant curvature segments from one made up of a single constant curvature segment. Experiment 2 tested observers' ability to discriminate contours generated from cubic splines (which, mathematically, have no constant curvature segments) from constant curvature approximations of the contours, generated at various levels of precision. Results indicated a clear transition point at which discrimination becomes possible. The results were used to fix the two parameters in our model. In Experiment 3, we tested whether outputs from our parameterized model were predictive of perceptual performance in a shape recognition task. We generated shape pairs that had matched physical similarity but differed in representational similarity (i.e., the number of segments needed to describe the shapes) as assessed by our model. We found that pairs of shapes that were more representationally dissimilar were also easier to discriminate in a forced choice, same/different task. The results of these studies provide evidence for constant curvature shape representation in human visual perception and provide a testable model for how abstract shape descriptions might be encoded.
format	article
author	Nicholas Baker Philip J Kellman
author_facet	Nicholas Baker Philip J Kellman
author_sort	Nicholas Baker
title	Constant curvature modeling of abstract shape representation.
title_short	Constant curvature modeling of abstract shape representation.
title_full	Constant curvature modeling of abstract shape representation.
title_fullStr	Constant curvature modeling of abstract shape representation.
title_full_unstemmed	Constant curvature modeling of abstract shape representation.
title_sort	constant curvature modeling of abstract shape representation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/229ff4227ad745f2b579081f2cb96887
work_keys_str_mv	AT nicholasbaker constantcurvaturemodelingofabstractshaperepresentation AT philipjkellman constantcurvaturemodelingofabstractshaperepresentation
_version_	1718374213694259200

Constant curvature modeling of abstract shape representation.

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