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Modelling User Navigation
Date Issued
2003
Date Available
2013-07-09T12:00:05Z
Abstract
This paper proposes the use of neural networks as a tool for studying navigation within
virtual worlds. Results indicate that the network learned to predict the next step for a given
trajectory. The analysis of hidden layer shows that the network was able to differentiate between
two groups of users identified on the basis of their performance for a spatial task. Time series
analysis of hidden node activation values and input vectors suggested that certain hidden units
become specialised for place and heading, respectively. The benefits of this approach and the
possibility of extending the methodology to the study of navigation in Human Computer
Interaction applications are discussed.
virtual worlds. Results indicate that the network learned to predict the next step for a given
trajectory. The analysis of hidden layer shows that the network was able to differentiate between
two groups of users identified on the basis of their performance for a spatial task. Time series
analysis of hidden node activation values and input vectors suggested that certain hidden units
become specialised for place and heading, respectively. The benefits of this approach and the
possibility of extending the methodology to the study of navigation in Human Computer
Interaction applications are discussed.
Type of Material
Journal Article
Publisher
Springer Berlin Heidelberg
Journal
Wseas Transactions on Systems
Volume
3
Issue
2
Start Page
582
End Page
588
Copyright (Published Version)
2003, Springer-Verlag Berlin Heidelberg
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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P18-Sas,Reilly,O'Hare,Marian,Mangina-03.pdf
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Format
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