Topic: state prediction error

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

Neuromodulatory systems play a crucial role in the brain, driving rapid, global changes in neuronal computation and learning. While the dopaminergic system is thought to broadcast a scalar reward prediction error, the serotonin system’s function has been difficult to elucidate, partly due to the heterogeneity of its individual neuronal responses [1]. Although serotonergic neurons are known to respond to various salient events [2] and rewards [3], which can be accounted for by ad-hoc extensions to a reinforcement learning framework, our study presents the first comprehensive, normative framewor