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Shape Perception as Bayesian Inference of Modality-Independent Part-Based 3D Object-Centered Shape Representations.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Shape Perception as Bayesian Inference of Modality-Independent Part-Based 3D Object-Centered Shape Representations./
Author:	Erdogan, Goker.
Description:	1 online resource (234 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
Subject:	Cognitive psychology. -
Online resource:	click for full text (PQDT)
ISBN:	9781369822991

Shape Perception as Bayesian Inference of Modality-Independent Part-Based 3D Object-Centered Shape Representations.
Erdogan, Goker.

Shape Perception as Bayesian Inference of Modality-Independent Part-Based 3D Object-Centered Shape Representations. - 1 online resource (234 pages)

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--University of Rochester, 2017.

Includes bibliographical references

Shape is a fundamental property of physical objects. It provides crucial information for various critical behaviors from object recognition to motor planning. The fundamental question here for cognitive science is to understand object shape perception, i.e., how our brains extract shape information from sensory stimuli and make use of it. In other words, we want to understand the representations and algorithms our brains use to achieve successful shape perception. This thesis reports a computational theory of shape perception that uses modality-independent, part-based, 3D, object-centered shape representations and frames shape perception as Bayesian inference over such representations. In a series of behavioral, neuroimaging and computational studies reported in the following chapters, we test various aspects of this proposed theory and show that it provides a promising approach to understanding shape perception.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369822991Subjects--Topical Terms:

556029
Cognitive psychology.
Index Terms--Genre/Form:

554714
Electronic books.

Shape Perception as Bayesian Inference of Modality-Independent Part-Based 3D Object-Centered Shape Representations.
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300 $a 1 online resource (234 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Advisers: Robert A. Jacobs; Jiebo Luo.
502 $a Thesis (Ph.D.)--University of Rochester, 2017.
504 $a Includes bibliographical references
520 $a Shape is a fundamental property of physical objects. It provides crucial information for various critical behaviors from object recognition to motor planning. The fundamental question here for cognitive science is to understand object shape perception, i.e., how our brains extract shape information from sensory stimuli and make use of it. In other words, we want to understand the representations and algorithms our brains use to achieve successful shape perception. This thesis reports a computational theory of shape perception that uses modality-independent, part-based, 3D, object-centered shape representations and frames shape perception as Bayesian inference over such representations. In a series of behavioral, neuroimaging and computational studies reported in the following chapters, we test various aspects of this proposed theory and show that it provides a promising approach to understanding shape perception.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Cognitive psychology. $3 556029
650 4 $a Artificial intelligence. $3 559380
650 4 $a Neurosciences. $3 593561
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