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Reading: Multiplication Number Facts: Modeling Human Performance with Connections Networks

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Research Article

Multiplication Number Facts: Modeling Human Performance with Connections Networks

Authors:

Betty Edelman ,

The University of Texas at Dallas, US
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Hervé Abdi,

The University of Texas at Dallas, US; Université de Bourgogne, FR
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Dominique Valentin

The University of Texas at Dallas, US
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Abstract

Three connectionist models of human performance on simple multiplication number facts, commonly called “times tables,” are reviewed. Also, human data from normal subjects and brain damaged patients, which constrain these models, are presented. These human data include the problem size effect, error effects, priming effects, use of strategies and rules, and number representation. The connectionist models presented are: a simple auto-associator (J. A. Anderson’s Brain-State-in-a-Box), a standard back-propagation model, and McCloskey and Lindemann’s MATHNET. The review of human data and connectionist models of memory retrieval provides some insight into the strengths of, differences between, and challenges for, this approach to computational modeling. Particular attention is paid to the representation of number used by these models, and a related ability to generalize learning.

How to Cite: Edelman, B., Abdi, H. and Valentin, D., 1996. Multiplication Number Facts: Modeling Human Performance with Connections Networks. Psychologica Belgica, 36(1-2), pp.31–63. DOI: http://doi.org/10.5334/pb.893
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Published on 01 Jan 1996.
Peer Reviewed

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