On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.

The current study assessed main effects and moderators (including emotional expressiveness, emotional processing and ambivalence over emotional expression) of the effects of expressive writing in a sample of healthy adults. Young adult participants (N = 116) were randomly assigned to write for 20 minutes on four occasions about deepest thoughts and feelings regarding their most stressful/traumatic event in the past five years (expressive writing) or about a control topic (control). Dependent variables were indicators of anxiety, depression, and physical symptoms. No significant effects of writing condition were evident on anxiety, depressive symptoms, or physical symptoms. Emotional expressiveness emerged as a significant moderator of anxiety outcomes, however. Within the expressive writing group, participants high in expressiveness evidenced a significant reduction in anxiety at three-month follow-up, and participants low in expressiveness showed a significant increase in anxiety. Expressiveness did not predict change in anxiety in the control group. These findings on anxiety are consistent with the matching hypothesis, which suggests that matching a person’s naturally elected coping approach with an assigned intervention is beneficial. These findings also suggest that expressive writing about a stressful event may be contraindicated for individuals who do not typically express emotions.

Expressive disclosure regarding a stressful event improves psychological and physical health, yet predictors of these effects are not well established. The current study assessed exposure, narrative structure, affect word use, self-affirmation and discovery of meaning as predictors of anxiety, depressive and physical symptoms following expressive writing. Participants (N = 50) wrote on four occasions about a stressful event and completed self-report measures before writing and three months later. Essays were coded for stressor exposure (level of detail and whether participants remained on topic), narrative structure, self-affirmation and discovery of meaning. Linguistic Inquiry and Word Count software was used to quantify positive and negative affect word use. Controlling for baseline anxiety, more self-affirmation and detail about the event predicted lower anxiety symptoms, and more negative affect words (very high use) and more discovery of meaning predicted higher anxiety symptoms three months after writing. Findings highlight the importance of self-affirmation and exposure as predictors of benefit from expressive writing.

Although there has been much interest in understanding the effect of gratitude on health-related outcomes, this remains an understudied area of research, particularly regarding mechanisms and measurement of biological outcomes. The present study explored whether a gratitude intervention could reduce inflammatory outcomes and whether this occurred through increased support-giving. Healthy women (n ϭ 76) were randomly assigned to a 6-week gratitude intervention (i.e., writing on topics intended to induce gratitude) or a control condition (i.e., neutral writing). Support-giving and markers of inflammation were measured pre-and postintervention. Those in the gratitude intervention (vs. control) reported higher postintervention levels of support-giving. Moreover, those with lower levels of psychological distress gave more support as a function of the gratitude intervention. Regarding inflammatory outcomes, although there was no effect of the gratitude intervention on postintervention inflammatory markers, increases in support-giving across the entire sample were related to decreases in inflammatory markers. These results, along with a scarcity of work in this area, suggest that further work is needed to more fully understand the relationships between gratitude and biological markers relevant to health. Finally, these novel findings linking support-giving and decreases in inflammation also indicate that the mammalian caregiving system, associated with enhanced support-giving and reduced physiological stress responding, is a mechanism worth further examination to elucidate the links between social support and health.

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience.