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.
201_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data from subject 201
203_Cannon_FILT_altLow_STIM.mat
Cleaned EEG data from participant 203
204_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for subject 204
205_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for subject 205
206_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for subject 206
207_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for subject 207
210_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for subject 210
211_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for subject 211
212_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 212
213_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 213
214_Cannon_FILT_altLow_STIM.mat
215_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 215
216_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 216
229_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 229
233_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 233
234_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 234
235_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 235
236_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 236
237_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 237
238_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 238
247_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 247
249_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 249
251_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 251
262_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 262
264_Cannon_FILT_altLow_STIM
preprocessed EEG data for participant 264
269_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 269
273_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 273
274_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 274
276_Cannon_FILT_altLow_STIM.mat
preprocessed data for participant 276
277_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 277
278_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 278
333_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 333
335_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 335
337_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 337
338_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 338
339_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 339
341_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 341
342_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 342
343_Cannon_FILT_altLow_STIM.mat
preprocessed EEG data for participant 343
Behavioral data
Behavioral 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.git
cannonBehavData_forDryad.zip
Analysis code for: Nassar, Bruckner, Frank (2019) eLife
Analysis code for: Nassar, Bruckner, Frank (2019) eLife