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Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices
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 thatdoi:10.1101/2021.05.10.443374 fatcat:ksdjva2di5aazlnf3xgzmswwuq