Investigating the Relationship between Facial Mimicry and Empathy

Abstract

Facial expressions play a key role in interpersonal communication when it comes to negotiating our emotions and intentions, as well as interpreting those of others. Research has shown that we can connect to other people better when we exhibit signs of empathy and facial mimicry. However, the relationship between empathy and facial mimicry is still debated. Among the factors contributing to the difference in results across existing studies is the use of different instruments for measuring both empathy and facial mimicry, as well as often ignoring the differences across various demographic groups. This study first looks at the differences in the empathetic abilities of people across different demographic groups based on gender, ethnicity and age. The empathetic ability is measured based on the Empathy Quotient, capturing a balanced representation of both emotional and cognitive empathy. Using statistical and machine learning methods, this study then investigates the correlation between the empathetic ability and facial mimicry of subjects in response to images portraying different emotions displayed on a computer screen. Unlike the existing studies measuring facial mimicry using electromyography, this study employs a technology detecting facial expressions based on video capture and deep learning. This choice was made in the context of increased online communication during and after the COVID-19 pandemic. The results of this study confirm the previously reported difference in the empathetic ability between females and males. However, no significant difference in empathetic ability was found across different age and ethnic groups. Furthermore, no strong correlation was found between empathy and facial reactions to faces portraying different emotions shown on a computer screen. Overall, the results of this study can be used to inform the design of online communication technologies and tools for training empathy team leaders, educators, social and healthcare providers.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

“…A scoping review of virtual environments (VE) for clinical communication skills ( Sanders et al, 2021 ) suggests multiple uses for enhancing clinician’s communication and empathy skills, as well as utility for communication research purposes. Evidence on effective doctor-patient communication has been applied as principles to robot-patient communication ( Broadbent et al, 2018 ) and empathy display/facial expression ( Milcent et al, 2021 ; Kovalchuk et al, 2022 ), to increase human engagement and attention in research contexts ( Johanson et al, 2019 ).…”

“…← Virtual worlds (VW) for communication and teamworking skills development (Mitchell et al, 2011;Wu et al, 2019) ← VR environments for communication skills and research (Sanders et al, 2021) ← Robot facial expression research (Broadbent et al, 2018;Milcent et al, 2021;Kovalchuk et al, 2022), human engagement and attention in research contexts (Johanson et al, 2019) Therapeutic bond and therapeutic alliance (5 articles)…”

Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding

“…Therefore, it was expected that the automated FACS based on facial recordings could not be as sensitive as facial EMG. However, given the increasing number of researchers using automated FACS to detect spontaneous facial mimicry [35][36][37][38], it is first essential to evaluate whether the software performs as well as the researchers' expectation. Third, the present study only evaluated the pairs of CS-AU4 and ZM-AU12.…”

“…Although the validity of spontaneous facial mimicry detection has not been statistically examined, several studies have already used the automated FACS to quantitatively estimate spontaneous facial mimicry in both clinical and nonclinical populations [35][36][37][38]. The present study used the live image relay system established in a previous study [39,40].…”

Deteksi Antusiasme Siswa dengan Algoritma Yolov8 pada Proses Pembelajaran Daring

“…Dalam klasifikasi ekspresi wajah, Ekman dan Friesen mengembangkan Facial Action Coding System (FACS) yang mencakup tujuh kategori, seperti marah, jijik, takut, bahagia, netral, sedih, dan terkejut (P. Ekmanand W. V. Friesen, 1971). Ekspresi wajah memainkan peran penting dalam komunikasi antarpribadi ketika kita berurusan dengan emosi dan niat kita, serta menginterpretasikan yang orang lain rasakan (Kovalchuk et al, 2022). Dalam konteks pembelajaran, pemahaman terhadap emosi siswa dapat memberikan wawasan berharga bagi pengajar untuk menyesuaikan metode pengajaran dan menciptakan lingkungan pembelajaran yang lebih mendukung.…”