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The Cognitive Science of Reorientation Open Access


Other title
Neural Networks
Cognitive Science
Cognitive Modelling
Comparative Cognition
Spatial Learning
Synthetic Psychology
Type of item
Degree grantor
University of Alberta
Author or creator
Dupuis, Brian A
Supervisor and department
Michael R. W. Dawson (Psychology)
Examining committee member and department
Brigandt, Ingo (Philosophy)
Boechler, Patricia (Education Psychology)
Spetch, Marcia (Psychology)
Department of Psychology

Date accepted
Graduation date
Master of Science
Degree level
This work stands as an example of “synthetic methodology” in psychological research. Synthetic methodology involves building a model, seeing what it can and cannot do when placed in interesting environments, comparing this behaviour to real-world subjects for parallels and discrepancies, and then examining the model for insight and theoretical advancement. This methodology is employed here in the context of a common spatial-learning “reorientation task”. Motivated by the discovery of critical flaws in a popular model for this reorientation task, we develop a synthetic neural network model as an alternative, and explore its behaviour in novel tasks, as well as the mathematical consequences of adopting such a formalism. These behaviours lead us to question assumptions underlying normal reorientation research. We devise a new method of collecting human data in spatial tasks, and use this method to compare the neural network to human subjects, in the style of comparative cognition.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Dawson, M. R. W., & Dupuis, B. (2012). The equilibria of perceptrons for simple contingency problems. IEEE Transactions in Neural Networks and Learning Systems, 23(8), 1340–1344. doi:10.1109/TNNLS.2012.2199766Dupuis, B., and Dawson, M.R.W. (under review). Resolving empirical difficulties with the Miller-Shettleworth model of geometry learning. Journal of Experimental Psychology: Animal Behaviour Processes (32 pages, submitted July 18, 2012).

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