NetFACS: Using network science to understand facial communication systems


Understanding facial signals in humans and other species is crucial for understanding the evolution,complexity, and function of the face as a communication system. The Facial Action Coding System (FACS)enables researchers to measure facial movements accurately, but we currently lack tools to reliablyanalyse data and efficiently communicate results. No statistical approach in facial signal research makesfull use of the information encoded in FACS datasets. Network analysis can provide a way forward: bytreating individual Action Units (the smallest units of facial movements) as nodes in a network and theirco-occurrence as connections, we can analyse and visualise differences in the use of combinations ofAction Units in different conditions. Here, we present ‘NetFACS’, a statistical package that usesoccurrence probabilities and resampling methods to answer questions about the use of Action Units,Action Unit combinations, and the facial communication system as a whole in humans and non-humananimals. Using human emotion signals as an example, we illustrate some of the current functionalities ofNetFACS. We show that very few Action Units are specific to certain stereotypical emotion signals; thatAction Units are not used independently from each other; that graph-level properties of stereotypicalemotion signals differ; and that clusters of Action Units allow us to reconstruct facial signals, even whenblind to the underlying conditions. The flexibility and widespread use of network analysis allows us tomove away from studying facial signals as stereotyped expressions, and towards a dynamic anddifferentiated approach to facial communication. © 2020 The Author(s)

psyRxiv, 2020, 10.31234/