Topic: task controller

Value-based decision-making in a context-changing environment is known to be guided by the two distinctive reinforcement learning (RL) strategies: goal-directed and habitual learning. Despite decades-long efforts to reveal that different environmental variables influence these RL processes, it remains unknown exactly how we create a situation by manipulating a set of environmental variables to guide these processes separately. This is mostly because of the complex interactions between environmental variables and RL, often beyond the experimenter’s comprehension. Here we show that an RL algorit

While simple value-based learning efficiently predicts an outcome in a stable environment, goal-directed learning can deal with dynamic and uncertain environments1. It is this complex nature that poses a challenge for experimental design to investigate such learning processes. For example, it is hard to predict how task manipulations affect the latent process of goal-directed learning. Here we present a computational framework of goal-directed task control to guide goal-directed learning. The proposed framework is based on an asymmetric two-player game setting: while a computational model of h