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

TL;DR

This paper introduces a Boolean neural network model that simulates human decision-making processes, including mental schemes and heuristics, and demonstrates its effectiveness through a simplified Iowa Gambling Task aligned with psychological findings.

Contribution

The paper presents a novel Boolean neural network model incorporating response strategies and schema formation, bridging data-driven and schema-driven decision processes.

Findings

Model transitions from chaotic to structured attractor states

Matches experimental psychological data

Provides insights into mental scheme emergence

Abstract

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.