A Neural Network Model of Rule-guided Behavior

摘要

The flexibility of our behavior is mainly caused by our ability to abstract rules from a circumstance and apply them to other situations. To examine the system for such rule-guided behavior, we proposed a neural network model of rule-guided behavior and simulated the physiological experiments of a rule-guided delayed matching-to-sample task (Wallis et al., 2001). Our model was constructed through neural system identification (Zipser, 1993) and a fully recurrent neural network model was optimized to perform a rule-guided delayed task. In the model's hidden layer, rule-selective units as in Wallis et al.(2001) were found, and an examination of connection weights substantiated that rule-selective neurons maintain encoded rule information and indirectly contributed to rule-guided responses. The simulation results predict functional interactions among neurons exhibiting various task-related activities.