Quantifying Facial Expression Synchrony in Face-To-Face Dyadic Interactions: Temporal Dynamics of Simultaneously Recorded Facial EMG Signals
Human social interaction is enriched with synchronous movement which is said to be essential to establish interactional flow. One commonly investigated phenomenon in this regard is facial mimicry, the tendency of humans to mirror facial expressions. Because studies investigating facial mimicry in face-to-face interactions are lacking, the temporal dynamics of facial mimicry remain unclear. We therefore developed and tested the suitability of a novel approach to quantifying facial expression synchrony in face-to-face interactions: windowed cross-lagged correlation analysis (WCLC) for electromyography signals. We recorded muscle activations related to smiling (Zygomaticus Major) and frowning (Corrugator Supercilii) of two interaction partners simultaneously in 30 dyadic affiliative interactions. We expected WCLC to reliably detect facial expression synchrony above chance level and, based on previous research, expected the occurrence of rapid synchronization of smiles within 200 ms. WCLC significantly detected synchrony of smiling but not frowning compared to a control condition of chance level synchrony in six different interactional phases (smiling: d z s = .85–1.11; frowning: d z s = .01–.30). Synchronizations of smiles between interaction partners predominantly occurred within 1000 ms, with a significant amount occurring within 200 ms. This rapid synchronization of smiles supports the notion of the existence of an anticipated mimicry response for smiles. We conclude that WCLC is suited to quantify the temporal dynamics of facial expression synchrony in dyadic interactions and discuss implications for different psychological research areas.
KeywordsSocial interaction Assessment Time series analysis Nonverbal synchrony Ecological validity Facial mimicry
We wish to thank all participants for their participation and Rukiye Köysürenbars, Mathias Osterried, Beatrice Salewski, Olga Schulz, and Katrin Zilliken, who helped with the data collection.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures were approved by a local ethics committee (Psychotherapeutenkammer Hamburg) and performed in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Boker, S. M., Cohn, J. F., Theobald, B.-J., Matthews, I., Brick, T. R., & Spies, J. R. (2009). Effects of damping head movement and facial expression in dyadic conversation using real-time facial expression tracking and synthesized avatars. Philosophical Transactions of the Royal Society of London. Series B, Biological sciences, 364(1535), 3485–3495. doi: 10.1098/rstb.2009.0152.CrossRefPubMedPubMedCentralGoogle Scholar
- Cohen, A. S., Morrison, S. C., & Callaway, D. A. (2013). Computerized facial analysis for understanding constricted/blunted affect: Initial feasibility, reliability, and validity data. Schizophrenia Research, 148(1–3), 111–116. doi: 10.1016/j.schres.2013.05.003.CrossRefPubMedPubMedCentralGoogle Scholar
- Lehrl, S. (1999). Mehrfachwahl-Wortschatz-Intelligenztest: MWT-B. Balingen: Spitta.Google Scholar
- McIntosh, D. N., Reichmann-Decker, A., Winkielman, P., & Wilbarger, J. L. (2006). When the social mirror breaks: Deficits in automatic, but not voluntary, mimicry of emotional facial expressions in autism. Developmental Science, 9(3), 295–302. doi: 10.1111/j.1467-7687.2006.00492.x.CrossRefPubMedGoogle Scholar
- Wittchen, H.-U., Fydrich, T., & Zaudig, M. (1997). SKID: Strukturiertes Klinisches Interview für DSM-IV; Achse I und II. Achse I: psychische Störungen. SKID-I. Göttingen: Hogrefe.Google Scholar