Solving Valid Syllogistic Problems using a Bidirectional Heteroassociative Memory

Marie-France H├ębertUniversity of Ottawa, Ottawa, Ontario, Canada
Sylvain ChartierUniversity of Ottawa, Ottawa, Ontario, Canada
Christophe TremblayUniversity of Ottawa, Ottawa, Ontario, Canada

Abstract

Classical syllogistic reasoning, also known as Aristotelian reasoning, is of particular interest in cognition. Such reasoning, which can seem simple at first, is known to be associated with high error rates. Although some research has been done on this topic, the underlying mechanisms used by human beings remain largely unknown. To understand the underlying cognitive properties associated with solving syllogistic problems, this study uses a connectionist approach composed of three steps inspired from Laird and Bara (1984): spatial representation, associative memory, and alternative searching conclusion. Results show that the network produces similar performances as humans. Keywords: Syllogistic reasoning; artificial neural network; neurodynamic modeling.

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Solving Valid Syllogistic Problems using a Bidirectional Heteroassociative Memory (1.6 MB)



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