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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. 

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. 

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.