Analysis of emotion regulation using posture, voice, and attention: A qualitative case study

Abstract

The ability to detect and regulate emotions is an important aspect of emotional intelligence that can benefit individuals in their personal well-being and social interactions (Mayer, Caruso, Salovey, 2016). This study examined emotion regulation (ER) in medical students as they practiced learning how best to communicate undesired news to patients in an international technology-rich learning environment (TRLE; Lajoie et al., 2012). Gross’ (2015) process model of ER served as the theoretical model that guided the analysis of regulatory strategies, in a case study (Yin, 2011) of four medical students. A qualitative approach was used to determine how multichannels of emotion representation (vocal characteristics, motor expressions, attention tendencies) indicate instances of conscious or unconscious ER. Analyses revealed four major findings as evidences of ER: (a) dissociation between emotion channels (e.g. a calm face accompanied by a nervous voice); (b) sudden changes in emotion expression without external triggers (e.g. from smile to a rapidly serious face); (c) unexpected emotions (e.g. smiling when expected to demonstrate empathy); and, (d) use of multiple emotion channels to demonstrate emotion regulatory responses. The findings demonstrate the power of multimodal emotion analysis towards accurate detection of ER, and how it may inform the relationship between experienced and expressed emotions. Multimodal ER detection approaches, as illustrated in this study, may have important implications for assisting learners in face to face and computer-supported collaborative settings in becoming more conscious of their ER, helping them regulate undesired self and peer emotions in challenging learning situations.