Facial expressions support effective social communication by dynamically transmitting complex, multi-layered messages, such as emotion categories and their intensity. How facial expressions achieve this signalling task remains unknown. Here, we address this question by identifying the specific facial movements that convey two key components of emotion communication – emotion classification (such as ‘happy,’ ‘sad’) and intensification (such as ‘very strong’) – in the six classic emotions (happy, surprise, fear, disgust, anger and sad). Using a data-driven, reverse correlation approach and an information-theoretic analysis framework, we identified in 60 Western receivers three communicative functions of face movements: those used to classify the emotion (classifiers), to perceive emotional intensity (intensifiers), and those serving the dual role of classifier and intensifier. We then validated the communicative functions of these face movements in a broader set of 18 complex facial expressions of emotion (including excited, shame, anxious, hate). We find that the timing of emotion classifier and intensifier face movements are temporally distinct, in which intensifiers peaked earlier or later than classifiers. Together, these results reveal the complexities of facial expressions as a signalling system, in which individual face movements serve specific communicative functions with a clear temporal structure.
Spatiotemporal neural network dynamics for the processing of dynamic facial expressions
