RT Journal Article
SR Electronic
T1 Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.10.443374
DO 10.1101/2021.05.10.443374
A1 Hans Kirschner
A1 Adrian G. Fischer
A1 Markus Ullsperger
YR 2021
UL http://biorxiv.org/content/early/2021/05/10/2021.05.10.443374.abstract
AB Optimal decision making in complex environments requires dynamic learning from unexpected events. To speed up learning, we should heavily weight information that indicates state-action-outcome contingency changes and ignore uninformative fluctuations in the environment. Often, however, unrelated information is hard to ignore and can potentially bias our learning. Here we used computational modelling and EEG to investigate learning behaviour in a modified probabilistic choice task that introduced two types of unexpected events that were irrelevant for optimal task performance, but nevertheless could potentially bias learning: pay-out magnitudes were varied randomly and, occasionally, feedback presentation was enhanced by visual surprise. We found that participants’ overall good learning performance was biased by distinct effects of these non-normative factors. On the neural level, these parameters are represented in a dynamic and spatiotemporally dissociable sequence of EEG activity. Later in feedback processing the different streams converged on a central to centroparietal positivity reflecting a final pathway of adaptation that governs future behaviour.Competing Interest StatementThe authors have declared no competing interest.