NEVER CROSS THE PATH OF A TRAVELING SALESMAN - THE NEURAL-NETWORK GENERATION OF HALSTEAD-REITAN TRAIL MAKING TESTS

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 facili tate systematic investigation, and there is no established procedure f or generating equivalent, but stochastically different, test forms. Th e reason is that the generation of self-avoiding TMT pathways resemble s the finding of near-optimal solutions to the Euclidean Traveling Sal esman 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 emplo ys an elastic net neural network to generate TMT forms based on self-a voiding, near-optimal paths, and closed circuits. The usefulness and l imitations of this solution are discussed briefly in relation to alter native and complementary problems and procedures.