DOI: 10.5061/dryad.570pf8n
title: Data from: Statistical context dictates the relationship between feedback-related EEG signals and learning

description: 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.
description: 201_Cannon_FILT_altLow_STIM.matpreprocessed EEG data from subject 201203_Cannon_FILT_altLow_STIM.matCleaned EEG data from participant 203204_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for subject 204205_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for subject 205206_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for subject 206207_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for subject 207210_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for subject 210211_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for subject 211212_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 212213_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 213214_Cannon_FILT_altLow_STIM.mat215_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 215216_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 216229_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 229233_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 233234_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 234235_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 235236_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 236237_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 237238_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 238247_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 247249_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 249251_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 251262_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 262264_Cannon_FILT_altLow_STIMpreprocessed EEG data for participant 264269_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 269273_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 273274_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 274276_Cannon_FILT_altLow_STIM.matpreprocessed data for participant 276277_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 277278_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 278333_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 333335_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 335337_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 337338_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 338339_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 339341_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 341342_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 342343_Cannon_FILT_altLow_STIM.matpreprocessed EEG data for participant 343Behavioral dataBehavioral data for all participants. For code to extract and analyze data, along with comments that provide information about particular data fields, see the analysis code provided on github: https://github.com/learning-memory-and-decision-lab/NassarBrucknerFrank_eLife_2019.gitcannonBehavData_forDryad.zipAnalysis code for: Nassar, Bruckner, Frank (2019) eLifeAnalysis code for: Nassar, Bruckner, Frank (2019) eLife