  Human decisional processes result from the employment of selected quantities
of relevant information, generally synthesized from environmental incoming data
and stored memories. Their main goal is the production 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 processes 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
experimental data and point out some relevant knowledge coming from
psychological domain.
