Abstract
The perception and interpretation of faces provides individuals with a wealth of knowledge that enables them to navigate their social environments more successfully. Yet, neural information regarding the temporal dynamics of valence-based information from emotional facial expressions remains limited. The present study tasked participants with viewing and categorizing 210 images of facial expressions belonging to seven different emotional categories (i.e., anger, disgust, fear, happy, neutral, sad, and surprised) across 30 unique identities for an equal number of male and female faces. As participants viewed and categorized these images, we recorded electroencephalogram (EEG) data from posterior electrode sites, then in conjunction with multi-variate pattern analysis (MVPA) attempted to decode for the temporal dynamics of valence-based facial expression information. In multiple different classifying conditions, it was demonstrated that when decoding for a positively- vs. a negatively- vs. a neutrally-valenced expression (e.g., happy vs. sad vs. neutral) and for a negatively- vs. a negatively- vs. a neutrally valenced expression (e.g., anger vs. sad vs. neutral), statistically significant above chance level decoding accuracy (i.e., chance level set at 33.33%) occurred, and that classification accuracy was higher in classification conditions with positively-valenced expressions versus classifications conditions with two-negatively valenced expressions. Additionally, above chance level decoding occurred sooner when decoding for a positively- vs. a negatively- vs. a neutrally valenced expression (i.e., around 73 ms on average) when compared to instances of decoding for a negatively- vs. a negatively- vs. a neutrally-valenced expression (i.e., 129 ms on average). Together, these finding suggest that neural processing of facial expressions may occur in a hierarchical manner, in that categorization of between-valence (positive vs. negative) facial expressions precedes categorization among within-valence (negative vs. negative) facial expressions.