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gamblers. This strange behavior can be interpreted from a psychological point of view as a persistence of the use of the heuristics of anchoring and adjustment during earlier trials after the loss, while in our network is due to the fact that the error quotas related to B' and D' becomes comparable.

## 5. Conclusion And Future Perspectives

We have presented preliminary results of application of a Boolean model of neural network to relevant cognitive strategies involved in decision making tasks. At moment only mental schemes have been studied. The choice of a such formalization is due to the possibilities that Boolean **neural networks** offers in terms of robustness, ease of simulation and easy generation of samples for data fitting.

The model appears to capture the most relevant psychological knowledge regarding the domain of application. By shifting from an unstructured slow attractor neural network to a quicker forward-only one, it hold in respect of learning studies about task complexity and the number of employed cognitive resources. As defined, mental schemes become fast **and adaptive** cognitive strategies of behavioral response.

Regarding the model fitting, the behavior of our network on the simplified version of the IGT produces results qualitatively comparable with those of humans.

Future studies will be targeted to include into the model aspects regarding probabilistic and hypothetical-deductive reasoning, while future applications will take in consideration pathological gambling.

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