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Statistical models of learning and using semantic representations.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Statistical models of learning and using semantic representations./
Author:	Abbott, Joshua Thomas.
Description:	1 online resource (110 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
Subject:	Cognitive psychology. -
Online resource:	click for full text (PQDT)
ISBN:	9781369559293

Statistical models of learning and using semantic representations.
Abbott, Joshua Thomas.

Statistical models of learning and using semantic representations. - 1 online resource (110 pages)

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2016.

Includes bibliographical references

How does cognition organize sparse and ambiguous input from the environment into useful representations and concepts for understanding the world? The work in this dissertation explores how people learn and reason with abstract knowledge, focusing on the kinds of processes and representations used in semantic memory. In particular, I present three case studies, each investigating different assumptions for the semantic representations and algorithms used to model cognition. The first chapter introduces this work situated in the framework of probabilistic models of cognition and outlines the goals of each case study. The second chapter focuses on the distinction between process and representation in semantic memory search. The simulations and analyses in this chapter show that behavioral results on a semantic fluency task previously explained as a strategic search process can also be produced by a non-strategic search process, depending on the structure of representation used for semantic memory. The third chapter investigates semantic representations as a means to explore universals and variation in cognition across cultures. In this chapter, I present a simple computational model operating over an irregularly shaped perceptual color space which accounts both for universal tendencies and for variation in focal colors, or best examples of color terms, across the world's languages. The fourth chapter explores the challenges of develop- ing models that can learn to appropriately apply new labels to concepts from only a few example observations, like people. Building upon a successful Bayesian word learning model, I propose adapting large-scale knowledge representations, typically used in machine learning and computer vision, to automatically construct hypothesis spaces for generalization models that account for these challenges. Finally, in the fifth chapter, I discuss the theoretical and practical implications for this body of work as a whole, and suggest a variety of future directions. Taken together, this research suggests general principles of computation over structured knowledge representations illuminates how people make sense of the world around them, and may lead to developing machines that think more like people do.

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

Mode of access: World Wide Web

ISBN: 9781369559293Subjects--Topical Terms:

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

554714
Electronic books.

Statistical models of learning and using semantic representations.
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245 1 0 $a Statistical models of learning and using semantic representations.
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300 $a 1 online resource (110 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-07(E), Section: B.
500 $a Adviser: Thomas L. Griffiths.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2016.
504 $a Includes bibliographical references
520 $a How does cognition organize sparse and ambiguous input from the environment into useful representations and concepts for understanding the world? The work in this dissertation explores how people learn and reason with abstract knowledge, focusing on the kinds of processes and representations used in semantic memory. In particular, I present three case studies, each investigating different assumptions for the semantic representations and algorithms used to model cognition. The first chapter introduces this work situated in the framework of probabilistic models of cognition and outlines the goals of each case study. The second chapter focuses on the distinction between process and representation in semantic memory search. The simulations and analyses in this chapter show that behavioral results on a semantic fluency task previously explained as a strategic search process can also be produced by a non-strategic search process, depending on the structure of representation used for semantic memory. The third chapter investigates semantic representations as a means to explore universals and variation in cognition across cultures. In this chapter, I present a simple computational model operating over an irregularly shaped perceptual color space which accounts both for universal tendencies and for variation in focal colors, or best examples of color terms, across the world's languages. The fourth chapter explores the challenges of develop- ing models that can learn to appropriately apply new labels to concepts from only a few example observations, like people. Building upon a successful Bayesian word learning model, I propose adapting large-scale knowledge representations, typically used in machine learning and computer vision, to automatically construct hypothesis spaces for generalization models that account for these challenges. Finally, in the fifth chapter, I discuss the theoretical and practical implications for this body of work as a whole, and suggest a variety of future directions. Taken together, this research suggests general principles of computation over structured knowledge representations illuminates how people make sense of the world around them, and may lead to developing machines that think more like people do.
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 Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a University of California, Berkeley. $b Psychology. $3 1180842
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