Never cross the path of a traveling salesman: The neural network generation of Halstead-Reitan trail making tests

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.