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Individuals exhibit a systematic valence bias—a specific form of interpretation bias—in response to emo- tional ambiguity. Accumulating evidence suggests most people initially respond to emotional ambiguity negatively and differ only in subsequent responses. We hypothesized that trait-level cognitive reap- praisal—an emotion regulation strategy involving the reinterpretation of affective meaning of stimuli— might explain individual differences in valence bias. To answer this question, we conducted a random- effects meta-analysis of 14 effect sizes from 13 prior studies (n = 2,086), identified via Google Scholar searches. We excluded studies (a) in languages other than English, (b) from non-peer-reviewed sources, or (c) nonempirical sources. We included studies with (a) the Emotion Regulation Questionnaire, (b) a puta- tive measure of valence bias prior to any study-specific manipulations, and (c) adult human participants (i.e., 17+). Supporting our prediction, we found individuals with higher trait reappraisal exhibited a less negative bias (r = −.18, z = −4.04, p , .001), whereas there was a smaller, opposite effect for trait expressive sup- pression (r = .10, z = 2.14, p = .03). The effects did, however, vary across tasks with stronger effects observed among studies using the scrambled sentences task compared to the valence bias task. Although trait reappraisal accounted for only a small amount of variance, reappraisal may be one mechanism contrib- uting to variability in response to ambiguity. 

Exposure to adversity (e.g., poverty, bereavement) is a robust predictor of disruptions in psychological functioning. However, people vary greatly in their responses to adversity; some experience severe long-term disruptions, others experience minimal disruptions or even improvements. We refer to the latter outcomes—faring better than expected given adversity—as psychological resilience. Understanding what processes explain resilience has critical theoretical and practical implications. Yet, psychology's understanding of resilience is incomplete, for two reasons: ( a) We lack conceptual clarity, and ( b) two major approaches to resilience—the stress and coping approach and the emotion and emotion-regulation approach—have limitations and are relatively isolated from one another. To address these two obstacles,we first discuss conceptual questions about resilience. Next, we offer an integrative affect-regulation framework that capitalizes on complementary strengths of both approaches. This framework advances our understanding of resilience by integrating existing findings, highlighting gaps in knowledge, and guiding future research.

 

Abstract Social-evaluative stressors—experiences in which people feel they could be judged negatively—pose a major threat to adolescent mental health 1–3 and can cause young people to disengage from stressful pursuits, resulting in missed opportunities to acquire valuable skills. Here we show that replicable benefits for the stress responses of adolescents can be achieved with a short (around 30-min), scalable 'synergistic mindsets' intervention. This intervention, which is a self-administered online training module, synergistically targets both growth mindsets 4 (the idea that intelligence can be developed) and stress-can-be-enhancing mindsets 5 (the idea that one’s physiological stress response can fuel optimal performance). In six double-blind, randomized, controlled experiments that were conducted with secondary and post-secondary students in the United States, the synergistic mindsets intervention improved stress-related cognitions (study 1, n  = 2,717; study 2, n  = 755), cardiovascular reactivity (study 3, n  = 160; study 4, n  = 200), daily cortisol levels (study 5, n  = 118 students, n  = 1,213 observations), psychological well-being (studies 4 and 5), academic success (study 5) and anxiety symptoms during the 2020 COVID-19 lockdowns (study 6, n  = 341). Heterogeneity analyses (studies 3, 5 and 6) and a four-cell experiment (study 4) showed that the benefits of the intervention depended on addressing both mindsets—growth and stress—synergistically. Confidence in these conclusions comes from a conservative, Bayesian machine-learning statistical method for detecting heterogeneous effects 6 . Thus, our research has identified a treatment for adolescent stress that could, in principle, be scaled nationally at low cost. 

How do people go about reading a room or taking the temperature of a crowd? When people catch a brief glimpse of an array of faces, they can focus their attention on only some of the faces. We propose that perceivers preferentially attend to faces exhibiting strong emotions and that this generates a crowd-emotion-amplification effect—estimating a crowd’s average emotional response as more extreme than it actually is. Study 1 ( N = 50) documented the crowd-emotion-amplification effect. Study 2 ( N = 50) replicated the effect even when we increased exposure time. Study 3 ( N = 50) used eye tracking to show that attentional bias to emotional faces drives amplification. These findings have important implications for many domains in which individuals must make snap judgments regarding a crowd’s emotionality, from public speaking to controlling crowds. 

Given the prevalence and adverse impact of anxiety, there is considerable interest in using technology to regulate anxiety. Evaluating the efficacy of such technology in terms of both the average effect (the intervention success) and the heterogeneous effect (for whom and in what context the intervention was effect) is of paramount importance. In this paper, we demonstrate the efficacy of PIV, a personalized breathing pacer, in reducing anxiety in the presence of a cognitive stressor. This is the first mixed-design study of a vibrotactile affect regulation technology which accounts for individual differences and user-technology engagement in relation to the technology's efficacy in the presence of a specific stressor. Guidelines in this paper can be applied for designing and evaluating other affect regulation technologies. 

Goals are widely understood to be central to the initiation, maintenance, and cessation of emotion regulation (ER). Recent studies have shown that there are profound individual differences in the types of ER goals people pursue and the extent to which they pursue them. Here, we highlight the importance of taking an individual difference approach to studying ER goals. First, we use the extended process model of ER to provide conceptual clarity on what ER goals are and describe the crucial role of goals in each stage of ER. We then identify five promising directions for future research using an individual difference approach to ER goals.

 

Emotion regulation in the wild (ER-in-the-wild) is an important grand challenge problem of increasing focus, and is hard to approach effectively with point solutions. We provide HCI researchers and designers thinking about ER- in-the-wild with an ER-in-the-wild system architecture derived from mHealth, the Emotion Regulation Process Model (PM), and a circular biofeedback model that can be used when designing an ER system. Our work is based on literature reviews of and collaborations with experts from the domains of wearables, emotion regulation, haptics and biofeedback (WEHAB) as well as systems. In addition to providing a generic model for ER-in-the-Wild, the system architecture presented in this paper explains different kinds of emotion regulatory interventions and their characteristics.