Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes

Human decisional processes result from the employment of selected quantities of relevant information, generally synthesized from environmen- tal incoming data and stored memories. Their main goal is the produc- tion of an appropriate and adaptive response to a cognitive or behavioral task. Different strategies of response production can be adopted, among which haphazard trials, formation of mental schemes and heuristics. In this paper, we propose a model of Boolean neural network that incorporates these strategies by recurring to global optimization strategies during the learning session. The model characterizes as well the passage from an unstructured/chaotic attractor neural network typical of data-driven pro- cesses to a faster one, forward-only and representative of schema-driven processes. Moreover, a simplified version of the Iowa Gambling Task (IGT) is introduced in order to test the model. Our results match with experimen- tal data and point out some relevant knowledge coming from psychological domain.