Human health is complex and multifaceted. It is a product of reciprocal interactions across multi-level systems, including biological, psychological, and socio-cultural processes. Building on a biopsychosocial model of health, our research strives to answer the following question: Are negative emotional states always detrimental to health? Extant evidence strongly suggests that they are, but much of this work draws nearly exclusively on European American adults who are college-educated. Thus, it remains unclear whether the maladaptiveness of negative emotional states generalizes beyond this group of people, which sometimes is referred to as WEIRD (Western, Educated, Industrialized, Rich, and Democratic; Henrich et al., 2010). Our research strives to test the generalizability (or lack thereof) of this conclusion, built on the premise that each negative emotion is not a monolithic entity, but rather entails a complex, multifaceted experience that has different meanings and functions across socio-cultural contexts. Thus, the health correlates of negative emotions will differ depending on their meaning and function in a given social context. We address this issue using daily diary methods (e.g., Zhu et al., 2023, Emotion) as well as archival data from large-scale cross-cultural surveys, such as the Midlife in the U.S. (MIDUS) and Midlife in Japan (MIDJA) and the harmonized data from the Gateway to Global Aging.
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In addition to our research on culture and emotion, we also pursue an additional program of work on emotion regulation. Although negative emotions are at times adaptive, they can have negative consequences when they become overwhelming, impeding one’s ability to think rationally about their problems. Thus, we also examine the process through which people can adaptively regulate emotions. One of the most critical factors contributing to regulatory success is one’s ability to spontaneously engage in adaptive emotion regulatory strategies at times when they are needed. We examine self-distancing as one such intra-individual regulatory capacity—i.e., a process that allows people to transcend their egocentric viewpoint during self-reflection. This emotion regulatory strategy has been shown to facilitate adaptive meaning-making by changing the way people cognitively represent emotional experiences in ways that reduce their aversiveness (Park et al., 2014, Clin Psychol Sci; Park et al., 2016, Emotion).
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Considering applied and clinical implications of this line of work, one critical question that arises is: how can self-distancing be harnessed in the context of intervention to facilitate adjustment? In an attempt to answer this question, we are currently testing whether meditation training, which enables non-judgmental observation of emotional experiences, would promote self-distancing, thereby facilitating meaning-making about these experiences. We plan to extend this line of work by testing the effects of meditating training on other types of emotion regulatory processes (e.g., emotion differentiation) and further elucidate the underlying neural mechanisms. We will address these questions with members of the Center for Contemplative Science at KAIST by bringing the social/affective neuroscience approach to understanding the neurobiological mechanisms of meditation and its psychological, social, and health consequences.
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Another way to extend our work on emotion regulation is to examine emotion-regulatory process in virtual reality (VR). For example, we are planning to develop an innovative VR-based emotion regulation (ER) training to facilitate self-distancing as well as other adaptive regulatory processes. VR-based interventions have been increasingly used in the treatment of a wide range of medical conditions, and yet, there has been no training program that specifically targets emotion regulation skills. The cornerstone of the VR-based ER intervention is that it enables an ecologically valid approach to train emotion regulation skills by creating a personally relevant and realistic virtual environment for each individual. We plan to integrate this technology with concurrent neurophysiological assessments using mobile monitoring devices to enhance the effectiveness of the training by providing instant biofeedback based on individuals’ physiological signals (e.g., heart rate).
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