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SHARE: A stochastic, hierarchical a...

Feng, Gang.

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SHARE: A stochastic, hierarchical architecture for reading eye-movement.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	SHARE: A stochastic, hierarchical architecture for reading eye-movement./
作者:	Feng, Gang.
面頁冊數:	252 p.
附註:	Source: Dissertation Abstracts International, Volume: 62-08, Section: B, page: 3821.
Contained By:	Dissertation Abstracts International62-08B.
標題:	Psychology, Cognitive. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3023055
ISBN:	0493346430

SHARE: A stochastic, hierarchical architecture for reading eye-movement.
Feng, Gang.

SHARE: A stochastic, hierarchical architecture for reading eye-movement. - 252 p.

Source: Dissertation Abstracts International, Volume: 62-08, Section: B, page: 3821.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.

Advances in methods for capturing patterns of eye-movements in reading have not yet been matched by corresponding methods for turning those data into a comprehensive quantitative model that is able to account for patterns of reading eye movements.

ISBN: 0493346430Subjects--Topical Terms:

1017810
Psychology, Cognitive.

SHARE: A stochastic, hierarchical architecture for reading eye-movement.
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245 1 0 $a SHARE: A stochastic, hierarchical architecture for reading eye-movement.
300 $a 252 p.
500 $a Source: Dissertation Abstracts International, Volume: 62-08, Section: B, page: 3821.
500 $a Adviser: Kevin F. Miller.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.
520 $a Advances in methods for capturing patterns of eye-movements in reading have not yet been matched by corresponding methods for turning those data into a comprehensive quantitative model that is able to account for patterns of reading eye movements.
520 $a The primary objective of the current research is to identify a set of mathematical tools that are able to describe reading eye movements, which are complex time-series data that covary with linguistic, perceptual, and other variables. A survey of existing quantitative models of reading eye movements shows that many of the models are unable to account for distributions of empirical eye-movement. Nonetheless, the variety of modeling approaches also point to promising solutions to the problem.
520 $a Based on analysis of modeling constraints, and inspired by previous efforts, a stochastic, hierarchical architecture for reading eye-movement, SHARE, is proposed. An advanced Markov model helps to capture the temporal dependency between reading eye movements, and the hierarchical structure concisely represents the logical relationships between covariate factors, eye-movement decisions, and observable eye-movement behaviors. Model parameter estimation is based on Bayesian theory, which provides a natural way to incorporate prior knowledge and to conduct probabilistic reasoning.
520 $a A simple model based on the SHARE architecture has been developed. Although it only takes into account a limited number of covariates and only models dependency between adjacent eye movements, it nevertheless is able to capture much of the dynamics of reading eye movements. A simulation study shows that with its simple structure, the model is able to reproduce the distributions of fixation durations and saccade length, as well as composite eye-movement variables. Because each reader is modeled individually, analyses of model parameters for readers of varying age and reading proficiency also shed light on the development of reading skills.
520 $a The SHARE architecture is shown to be flexible enough to characterize both beginning and fluent reading, which is particularly attractive for the study of reading development. Its ability to capture eye-movement patterns also opens a wide range of possibilities for real-world applications of the eye-movement technology.
590 $a School code: 0090.
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650 4 $a Psychology, Developmental. $3 1017557
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790 $a 0090
791 $a Ph.D.
792 $a 2001
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