Social Support Predicts Differential Use, But Not Differential Effectiveness, of Expressive Suppression and Social Sharing in Daily Life

While emotion regulation often happens in the presence of others, little is known about how social context shapes regulatory efforts and outcomes. One key element of the social context is social support. In two experience sampling studies (Ns = 179 and 123), we examined how the use and affective consequences of two fundamentally social emotion regulation strategies—social sharing and expressive suppression—vary as a function of perceived social support. Across both studies, we found evidence that perceived social support predicted variation in people’s use of these strategies, such that higher levels of social support predicted more sharing and less suppression. However, we found only limited and inconsistent support for context-dependent affective outcomes of suppression and sharing: suppression was associated with better affective consequences in the context of higher perceived social support in Study 1, but this effect did not replicate in Study 2. Taken together, these findings suggest that the use of social emotion regulation strategies appears to depend on contextual variability in social support, whereas their effectiveness does not. Future research is needed to better understand the circumstances in which context-dependent use of emotion regulation may have emotional benefits, accounting for personal, situational, and cultural factors.

Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks

Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training.

Factors influencing protective behavior in the post-COVID-19 period in China: a cross-sectional study

Background The study aimed to explore the factors influencing protective behavior and its association with factors during the post-COVID-19 period in China based on the risk perception emotion model and the protective action decision model (PADM). Methods A total of 2830 valid questionnaires were collected as data for empirical analysis via network sampling in China. Structural equation modeling (SEM) was performed to explore the relationships between the latent variables. Results SEM indicated that social emotion significantly positively affected protective behavior and intention. Protective behavioral intention had significant direct effects on protective behavior, and the direct effects were also the largest. Government trust did not have a significant effect on protective behavior but did have a significant indirect effect. Moreover, it was found that government trust had the greatest direct effect on social emotion. In addition, we found that excessive risk perception level may directly reduce people’s intention and frequency of engaging in protective behavior, which was not conducive to positive, protective behavior. Conclusion In the post-COVID-19 period, theoretical framework constructed in this study can be used to evaluate people’s protective behavior. The government should strengthen its information-sharing and interaction with the public, enhance people’s trust in the government, create a positive social mood, appropriately regulate people's risk perception, and, finally, maintain a positive attitude and intent of protection.

Motivation, Social Emotion, and the Acceptance of Artificial Intelligence Virtual Assistants—Trust-Based Mediating Effects

The complexity of the emotional presentation of users to Artificial Intelligence (AI) virtual assistants is mainly manifested in user motivation and social emotion, but the current research lacks an effective conversion path from emotion to acceptance. This paper innovatively cuts from the perspective of trust, establishes an AI virtual assistant acceptance model, conducts an empirical study based on the survey data from 240 questionnaires, and uses multilevel regression analysis and the bootstrap method to analyze the data. The results showed that functionality and social emotions had a significant effect on trust, where perceived humanity showed an inverted U relationship on trust, and trust mediated the relationship between both functionality and social emotions and acceptance. The findings explain the emotional complexity of users toward AI virtual assistants and extend the transformation path of technology acceptance from the trust perspective, which has implications for the development and design of AI applications.

Social Perception of Risk-Taking Willingness as a Function of Expressions of Emotions

Two studies showed that emotion expressions serve as cues to the expresser’s willingness to take risks in general, as well as in five risk domains (ethical, financial, health and safety, recreational, and social). Emotion expressions did not have a uniform effect on risk estimates across risk domains. Rather, these effects fit behavioral intentions associated with each emotion. Thus, anger expressions were related to ethical and social risks. Sadness reduced perceived willingness to take financial (Study 1 only), recreational, and social risks. Happiness reduced perceived willingness to take ethical and health/safety risks relative to neutrality. Disgust expressions increased the perceived likelihood of taking a social risk. Finally, neutrality increased the perceived willingness to engage in risky behavior in general. Overall, these results suggest that observers use their naïve understanding of the meaning of emotions to infer how likely an expresser is to engage in risky behavior.

Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis

Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students’ sentiments evolved from positive to confused/negative to insightful.