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.
Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes / Barnabei, Graziano; Bagnoli, Franco; Conversano, C.; Lensi, E.. - STAMPA. - 3:(2010), pp. 303-313. (Intervento presentato al convegno Summer Solstice 2009 International Conference on Discrete Models of Complex Systems tenutosi a Danzica (Polonia) nel June 22–24, 2009).
Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes
BARNABEI, GRAZIANO;BAGNOLI, FRANCO;
2010
Abstract
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.File | Dimensione | Formato | |
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