Topic: Markov decision task

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