Appraisal-driven facial actions as building blocks for emotion inference

Although research on facial emotion recognition abounds, there has been little attention on the nature of the underlying mechanisms. In this article, using a "reverse engineering" approach, we suggest that emotion inference from facial expression mirrors the expression process. As a strong case can be made for an appraisal theory account of emotional expression, which holds that appraisal results directly determine the nature of facial muscle actions, we claim that observers first detect specific appraisals from different facial muscle actions and then use implicit inference rules to categorize and name specific emotions. We report three experiments in which, guided by theoretical predictions and past empirical evidence, we systematically manipulated specific facial action units individually and in different configurations via synthesized avatar expressions. Large, diverse groups of participants judged the resulting videos for the underlying appraisals and/or the ensuing emotions. The results confirm that participants can infer targeted appraisals and emotions from synthesized facial actions based on appraisal predictions. We also report evidence that the ability to correctly interpret the synthesized stimuli is highly correlated with emotion recognition ability as part of emotional competence. We conclude by highlighting the importance of adopting a theory-based experimental approach in future research, focusing on the dynamic unfolding of facial expressions of emotion. (PsycINFO Database Record