Modeling error detection in human brain: A preliminary unification of reinforcement learning and conflict monitoring theories

The error detection concept plays a critical role in theories of performance monitoring. In this study, we have proposed a model that somehow unifies two main theories of performance monitoring: reinforcement learning and conflict monitoring. The proposed model, which is a modified and extended version of computational model of reinforcement learning theory, is used to simulate behavioral and event-related brain potential data in a modified version of Eriksen flanker task. This model captures the idea of conflict monitoring theory. Therefore, it can generate a component of event-related brain potential (N200) that reinforcement learning theory is not capable of producing it.