Neural Network and Tree Search Algorithms for the Generation of Path-Following (Trail-Making) Tests

Due to their sensitivity to cognitive impairment, simplicity of administration and low cost, path-following tests, such as the HalsteadReitan Trail Making Test (TMT) and the Zahlen-Verbindungs-Test (ZVT), are among the most frequently used neuropsychological tests in the assessment of brain damage. Despite their usefulness, however, such tests have been constructed in an unprincipled manner which does not facilitate repeated testing, systematic investigation or theoretical interpretation. The reason for their unprincipled development is that the selective generation of the required pathways constitutes a computational problem that is NPcomplete. This paper outlines two practical solutions to the problem of generating ZVT matrices. The first employs a Hopfield-style neural network and the second employs a heuristic-driven tree search. The characteristics of the pathways generated by these two solutions are compared, and ways of manipulating the relative difficulty of alternative test forms are discussed.

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