which leads up to model construction, a formulation of provisional conclusions, a revision procedure that verifies if other models are **possible. Errors**
occur because of working memory limitedness: the bigger is the number of models that we have to menage, the harder the problem becomes. So, errors are conclusions not rigorously verified.

5) Heuristic behavior: modus operandi **typical of situations in which**
there is either a lack of information, or different mental schemes run in conflict, or there is no time to reasoning, or the task is too difficult. In these cases, individuals adopt strategies more similar to an attempt rather than to the formal solution. Note that, in some cases, the use **of heuristics is**
mandatory and constitutes a cognitive bias1**. Most important heuristics in**
psychology are anchoring/adjustment, availability, and representativeness [10].

While strategies 1-4 belong to a hierarchy of use and exploitation of cognitive resources, heuristics take place only after strategy 3, and if there are no conditions to apply the other ones or their application fails. Finally, it is possible to bring back the aforementioned considerations into the simple cognitive model reported in Fig. 1. Neglecting earlier input systems and concept stages, our main purpose is to formalize into a neural model the cognitive constructs of mental scheme and heuristic, showing how these can modify the production of a response. Besides we will propose **a simplified** version of the IGT [11, 12] in order to test our model.

The paper is organized as follow. In the next section we introduce the neural model, fourth section is devoted to model fitting, and **in the final** sections we show the main results of our simulations and discuss about future perspectives.

## 2. The Model

The model assumes that cognitive activities during a task resolution can be represented as a Boolean neural network, whose nodes do not necessarily correspond to single biological neurons but rather to organized sets of neurons, named functional areas. We choose this formalization in order to remain within the framework of neural domain.

The basic computational entities, namely the formal neurons, are described by the following parameters:
1. σ**: the internal and external state of activation** ∈ {**-1, +1**} 2. b: the threshold ∈ **[0, 1]**