A composite neural network model for perseveration and distractibility in the Wisconsin card sorting test

Abstract

A composite artificial neural network model is proposed to simulate the performance of the Wisconsin Card Sorting Test. The Wisconsin Card Sorting Test is a test of executive functions where prefrontal deficits are matched to some quantitative measures such as percentage of perseverative errors and number of failures to maintain set. In this work, the proposed model is used to simulate the performances of healthy subjects and patients with prefrontal involvement particularly on these measures. The model is designed in such a way that one of the subsystems, namely, the Hopfield network, serves as the working memory and the other, the Hamming block, as the hypothesis generator. The results show that the proposed relatively simple model is capable of simulating the wide range of the performances of both normal subjects and prefrontal patients on the Wisconsin Card Sorting Test. While lowering the Hamming distance in the Hamming block gave rise to progressively more perseverative responses, changing the threshold vector of the Hopfield network resulted in more set maintenance failures. The former manipulation disrupts the abstraction or mental flexibility and the latter sustained attention or perseverance both of which are the major functions of the prefrontal system.