NetFACS: using network science to understand facial communication systems

Mielke, A., Waller, B.M. ORCID: 0000-0001-6303-7458, Pérez, C., Rincon, A.V., Duboscq, J. and Micheletta, J., 2021. NetFACS: using network science to understand facial communication systems. Behavior Research Methods. ISSN 1554-351X

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Understanding facial signals in humans and other species is crucial for understanding the evolution, complexity, and function of the face as a communication tool. The Facial Action Coding System (FACS) enables researchers to measure facial movements accurately, but we currently lack tools to reliably analyse data and efficiently communicate results. Network analysis can provide a way to use the information encoded in FACS datasets: by treating individual AUs (the smallest units of facial movements) as nodes in a network and their co-occurrence as connections, we can analyse and visualise differences in the use of combinations of AUs in different conditions. Here, we present 'NetFACS', a statistical package that uses occurrence probabilities and resampling methods to answer questions about the use of AUs, AU combinations, and the facial communication system as a whole in humans and non-human animals. Using highly stereotyped facial signals as an example, we illustrate some of the current functionalities of NetFACS. We show that very few AUs are specific to certain stereotypical contexts; that AUs are not used independently from each other; that graph-level properties of stereotypical signals differ; and that clusters of AUs allow us to reconstruct facial signals, even when blind to the underlying conditions. The flexibility and widespread use of network analysis allows us to move away from studying facial signals as stereotyped expressions, and towards a dynamic and differentiated approach to facial communication.

Item Type: Journal article
Publication Title: Behavior Research Methods
Creators: Mielke, A., Waller, B.M., Pérez, C., Rincon, A.V., Duboscq, J. and Micheletta, J.
Publisher: Springer Science and Business Media LLC
Date: 9 November 2021
ISSN: 1554-351X
Rights: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
Divisions: Schools > School of Social Sciences
Record created by: Laura Ward
Date Added: 15 Nov 2021 14:14
Last Modified: 15 Nov 2021 14:14

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