Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/3217
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Type: Journal article
Title: Never cross the path of a traveling salesman: The neural network generation of Halstead-Reitan trail making tests
Author: Vickers, Doug
Lee, Michael David
Citation: Behavior Research Methods, Instruments, and Computers, 1998; 30(3):423-431
Issue Date: 1998
Statement of
Responsibility: 
Douglas Vickers, Michael D. Lee
Abstract: The Halstead-Reitan Trail Making Test (TMT) is one of the most widely used neuropsychological instruments for the assessment of brain damage. Despite its usefulness, however, the TMT has two major disadvantages. It has not been constructed in a principled manner that would facilitate systematic investigation, and there is no established procedure for generating equivalent, but stochastically different, test forms. The reason is that the generation of self-avoiding TMT pathways resembles the finding of near-optimal solutions to the Euclidean Traveling Salesman Problem (TSP) and constitutes a computational problem that is NP-complete. This article describes a practical approach to the problem of generating stochastically different test forms. This approach employs anelastic net neural network to generate TMT forms based on self-avoiding, near-optimal paths, and closed circuits. The usefulness and limitations of this solution are discussed briefly in relation to alternative and complementary problems and procedures.
Rights: © Springer, Part of Springer Science+Business Media
DOI: 10.3758/BF03200675
Appears in Collections:Psychology publications
Environment Institute publications

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