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Learning via prediction : mapping continuous stimuli to discrete symbols

Author: Adam Daniel November; Michael Ramscar; Ewart A C Thomas; James L McClelland; Stanford University. Department of Psychology.
Publisher: 2010.
Dissertation: Thesis (Ph. D.)--Stanford University, 2010.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Database:WorldCat
Summary:
How do we use and represent words? How do we learn to break up the dimensions of continuous variation of the world into discrete categories? In this thesis, I explore how recasting this problem in terms of simple prediction provides insight into the computational nature of word learning. Through a series of computational simulations and human experiments that manipulate the structure of information in time, I find  Read more...
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Details

Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Adam Daniel November; Michael Ramscar; Ewart A C Thomas; James L McClelland; Stanford University. Department of Psychology.
OCLC Number: 666741063
Notes: Submitted to the Department of Psychology.
Description: 1 online resource.
Responsibility: Adam Daniel November.

Abstract:

How do we use and represent words? How do we learn to break up the dimensions of continuous variation of the world into discrete categories? In this thesis, I explore how recasting this problem in terms of simple prediction provides insight into the computational nature of word learning. Through a series of computational simulations and human experiments that manipulate the structure of information in time, I find that when using features of the world to predict words, the representations learned are likely to be more useful for discriminating the appropriate response. However, these representations are also likely to be distorted, favoring diagnostic information, and thus sacrificing general utility across contexts. At the same time, trying to use words to predict the relevant features of the world will result in less distorted representations, but will not enhance discrimination. Demonstrating these effects while controlling for various potentially confounding variables strengthens the case that prediction is central to word learning.

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