Forschungsprojekt ::

The influence of top-down control on feedback processing and reinforcement learning in decision tasks

Projektbeschreibung

Evaluation of external feedback is an important prerequisite for optimal decision-making. Feedback allows for improving future decisions by taking the consequences of past decisions into account. In recent years, learning from feedback has been considered within the theoretical framework of reinforcement learning. That is, feedback has been viewed as a reinforcer that leads to the adjustment of stimulus-response associations involved in a decision. Evidence for this comes from studies that examined feedback using event-related potentials (ERPs). The feedback-related negativity (FRN), a negative deflection elicited by negative feedback, has been shown to reflect a prediction error that is used as a learning signal for the adjustment of behavior. The present project aims to investigate whether and how the FRN - and thus reinforcement learning due to feedback - is influenced by top-down control. In the planned experiments, participants are instructed about the validity of feedback. This creates a situation in which learning from feedback is more or less beneficial, and hence, in which reinforcement learning should be more or less modulated by top-down control. By applying formal models of reinforcement learning to empirical data, we aim to describe the interaction between these top-down effects and reinforcement learning. Further experiments will be conducted to reveal how mechanisms like proactive control or selective attention contribute to top-down control. On the one hand, our studies aim to provide an answer to the classical question whether reinforcement learning can be influenced by cognitive processes. On the other hand, we hope to validate and improve current models of reinforcement learning by taking cognitive processes into account.