Research suggests that perceived social support bolsters emotional well-being. We tested whether perceived support from friends, family, and spouses/partners was associated with reduced negative and greater positive affectivity (i.e., everyday affective baseline), and whether perceived strain in these relationships had the opposite effects. Using data from the third waves of the Midlife in the United States survey and National Study of Daily Experience (n = 1,124), we found negative affectivity decreased with more support from friends, and increased with more strain among family. Positive affectivity increased with more support from friends and family and decreased with more strain among friends and partners. We replicated analyses using second-wave Midlife in Japan survey data (n = 657) and found friends’ support and familial tension had the same impacts on positive and negative affect cross-culturally. Some relationship dynamics may vary, but perceived support—especially in friendships—might cross-culturally enhance everyday emotional well-being.

Whether it is the first day of school or a new job, individuals often find themselves in situations where they must quickly and accurately learn novel social networks. Prior work has shown that success in learning social networks predicts more positive individual and social outcomes, but the mechanisms through which social network learning occurs are unclear. We posit that individuals use linguistic features of observed conversations to identify the valence and strength of social relationships. To investigate this, 57 adults completed a naturalistic behavioral task wherein they watched an episode of the reality television show, Survivor, and indicated which contestants they thought were friends or rivals throughout the episode. In Study 1, we take a person-focused approach using 34,735 participant observations to understand how well individuals learned the social network structure. In Study 2, we take a novel stimulus-focused approach that employs natural language processing to quantify 486 sentences of episode dialogue and examine how distinct linguistic features (similarity, emotional tone, self-assurance) predict network learning. In Study 3, we analyze the time course of the entire preceding season of Survivor using 296 dyadic similarity assessments to explore the association between semantic similarity and relationship formation. Across three studies, we found that participants successfully learned the social network structure, linguistic features predicted network learning, and greater semantic similarity was associated with friendship formation. These findings suggest that naturalistic conversational content is both a potential mechanism of social network learning and a promising avenue for research on social relational inference.

Across three studies, we used a combination of quasi-naturalistic contexts and a strategy choice paradigm to explore the influence emotional intensity has on emotion regulation strategy usage. These results suggest that emotional intensity predicts regulation choices in the abstract, but may be less predictive in real-world contexts.