Decoding dynamic implicit and explicit representations of facial expressions of emotion from EEG

Faces transmit a wealth of important social signals. While previous studies have elucidated the network of cortical regions important for perception of facial expression, and the associated temporal components such as the P100, N170 and EPN, it is still unclear how task constraints may shape the representation of facial expression (or other face categories) in these networks. In the present experiment, we investigate the neural information available across time about two important face categories (expression and identity) when those categories are either perceived under explicit (e.g. decoding emotion when task is on emotion) or implicit task contexts (e.g. decoding emotion when task is on identity). Decoding of both face categories, across both task contexts, peaked in a 100-200ms time-window post-stimulus (across posterior electrodes). Peak decoding of expression, however, was not affected by task context whereas peak decoding of identity was significantly reduced under implicit processing conditions. In addition, errors in EEG decoding correlated with errors in behavioral categorization under explicit processing for both expression and identity, but only with implicit decoding of expression. Despite these differences, decoding time-courses and the spatial pattern of informative electrodes differed consistently for both tasks across explicit Vs implicit face processing. Finally our results show that information about both face identity and facial expression is available around the N170 time-window on lateral occipito-temporal sites. Taken together, these results reveal differences and commonalities in the processing of face categories under explicit Vs implicit task contexts and suggest that facial expressions are processed to a richer degree even under implicit processing conditions, consistent with prior work indicating the relative automaticity by which emotion is processed. Our work further demonstrates the utility in applying multivariate decoding analyses to EEG for revealing the dynamics of face perception.