Abstract
Currently neural networks are used in many different domains. But are neural networks also suitable for modeling problem solving, a domain which is traditionally reserved for the symbolic approach? This central question of cognitive science is answered in this paper. It is affirmed by a corresponding neural network model. The model has the same behavior as a symbolic model. However, also additional properties resulting from the distributed representation emerge. It is shown by comparison of those additional abilities with the basic behavior of the model, that the additional properties lead to a significant algorithmic improvement. This is verified by statistical hypothesis testing.
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References
Andersoll, J. R. (1995). Cognitive Psyhology and its Implications. W. H. Freeman and Company, fourth edition.
Newell, A. (1990). Unified Theories of Cognition. Harvard University Press.
Newell, A. Simon, H. (1972). Human Problem Solving. Prentice-Hall.
Steinbuch, K. (1961). Die Lernmatrix. Kybernetik, 1:36–45.
Steinbuch, K. (1971). Automat und Mensch. Springer-Verlag, fourth edition.
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© 2001 Springer-Verlag Wien
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Wichert, A., Lonsinger-Miller, B. (2001). Principles of Associative Computation. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_15
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_15
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
Online ISBN: 978-3-7091-6230-9
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