Social interaction context shapes emotion recognition through body language, not facial expressions.

The social context-seeing people emotionally interacting-is one of the most common contexts in which emotion perception occurs. Despite its importance, emotion perception of social interactions from a 3rd-person perspective is poorly understood. Here we investigated whether emotion recognition of fear and anger is facilitated by mere congruency (the contextual figure exhibits the same emotion as the target) or by functional relations (the contextual figure exhibits a complementary emotion to the target). Furthermore, we examined which expression channel, face or body, drives social context effects. In the 1st 2 experiments (Studies 1a and 1b), participants in an online survey platform (N = 146) or university students (N = 34), viewed interacting figures displaying fear or anger, presented either as faces, bodies, or both. Participants were instructed to categorize the target figure's emotions while the other figure served as context. Results showed that fear recognition was facilitated by an interacting angry figure more strongly than by an interacting fearful figure. Moreover, this effect occurred when participants viewed the figures' bodies (with or without the faces), but not when they viewed the figures' faces alone. A 3rd online experiment (Study 2) established that this context effect was stronger when participants (N = 464) watched the figures interacting (facing each other) than when figures were not interacting (facing away from each other), suggesting that social context influences emotion perception by revealing the interactants' relation. Our findings demonstrate that emotional perception is grounded in the broader process of social interaction and highlight the role of the body in interpersonal context effects. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Social Medical Capital: How Patients and Caregivers Can Benefit From Online Social Interactions.

The rapid growth of online health communities and the increasing availability of relational data from social media provide invaluable opportunities for using network science and big data analytics to better understand how patients and caregivers can benefit from online conversations. Here, we outline a new network-based theory of social medical capital that will open up new avenues for conducting large-scale network studies of online health communities and devising effective policy interventions aimed at improving patients' self-care and health.