Abstract: In real-life situations, facial expressions often change dynamically over time, and an individual's interpretation of any given facial expression may be influenced by the dynamic sequential context in which it is embedded. For instance, when a facial expression transition from sadness to happiness, how do the preceding expressions affect the perception of the current happy expression? While recent studies have indicated that simultaneously presented contextual information affect the perception of target expressions, little is known about the effect of sequentially changing context. The present study aims to investigate the influence of sequential changes in facial expressions (i.e., dynamic sequential context) on the perception of past and current expressions, as well as the underlying mechanisms, through the use of behavioral experiments and eye-tracking technology. The stimuli include both artificially synthesized and human-performed dynamic facial expressions. The research findings will contribute to our understanding of facial expression processing in ecologically valid contexts and provide valuable insights for the artificial the development of AI-based dynamic facial expression recognition systems.

Key words: facial expression, emotion perception, dynamic sequential context