Convergent activation dynamics in continuous time networks
Neural Networks
Neural Networks - Special issue on the global brain: imaging and modelling
A Computational Model of Information Processing in the Frontal Cortex and Basal Ganglia
Journal of Cognitive Neuroscience
Common blood flow changes across visual tasks: Ii. decreases in cerebral cortex
Journal of Cognitive Neuroscience
Dynamical Systems Account for Meta-level Cognition
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Acquiring Rules for Rules: Neuro-Dynamical Systems Account for Meta-Cognition
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Hi-index | 0.00 |
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.