Data from: Statistical context dictates the relationship between feedback-related EEG signals and learning

Learning should be adjusted according to the surprise associated with
observed outcomes but calibrated according to statistical context. For
example, when occasional changepoints are expected, surprising outcomes
should be weighted heavily to speed learning. In contrast, when
uninformative outliers are expected to occur occasionally, surprising
outcomes should be less influential. Here we dissociate surprising
outcomes from the degree to which they demand learning using a predictive
inference task and computational modeling. We show that the P300, a
stimulus-locked electrophysiological response previously associated with
adjustments in learning behavior, does so conditionally on the source of
surprise. Larger P300 signals predicted greater learning in a changing
context, but less learning in a context where surprise indicated a one-off
outlier (oddball). Our results suggest that the P300 provides a surprise
signal that is interpreted by downstream learning processes differentially
according to statistical context in order to appropriately calibrate
learning across complex environments.