A new computational account of cognitive control over reinforcement-based decision-making: Modeling of a probabilistic learning task

• Modeling decision-making from the perspectives of dual-system and cognitive control.
• The model simulates human performance on a variant of probabilistic learning task.
• The model addresses existing theories about the ERN and FRN components of ERP.
• The results show that the ERN is best described by the RL-ERN theory.
• The FRN is best described by a hypothetical cost-conflict signal.

Recent work on decision-making field offers an account of dual-system theory for decision-making process. This theory holds that this process is conducted by two main controllers: a goal-directed system and a habitual system. In the reinforcement learning (RL) domain, the habitual behaviors are connected with model-free methods, in which appropriate actions are learned through trial-and-error experiences. However, goal-directed behaviors are associated with model-based methods of RL, in which actions are selected using a model of the environment. Studies on cognitive control also suggest that during processes like decision-making, some cortical and subcortical structures work in concert to monitor the consequences of decisions and to adjust control according to current task demands. Here a computational model is presented based on dual system theory and cognitive control perspective of decision-making. The proposed model is used to simulate human performance on a variant of probabilistic learning task. The basic proposal is that the brain implements a dual controller, while an accompanying monitoring system detects some kinds of conflict including a hypothetical cost-conflict one. The simulation results address existing theories about two event-related potentials, namely error related negativity (ERN) and feedback related negativity (FRN), and explore the best account of them. Based on the results, some testable predictions are also presented.