Decisional Processes with Boolean Neural Network: the Emergence of Mental Schemes
Graziano Barnabei, Franco Bagnoli, Ciro Conversano, Elena Lensi

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…
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Taxonomy
TopicsNeural Networks and Applications · Time Series Analysis and Forecasting · Data Visualization and Analytics
