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Emotion-motivation models propose that behaviors, including health behaviors, should be predicted by the same variables that also predict negative affect since emotional reactions should induce a motivation to avoid threatening situations. In contrast, social cognitive models propose that safety behaviors are predicted by a different set of variables that mainly reflect cognitive and socio-structural aspects. Here, we directly tested these opposing hypotheses in young adults (N = 4134) in the context of COVID-19-related safety behaviors to prevent infections. In each participant, we collected measures of negative affect as well as cognitive and socio-structural variables during the lockdown in the first infection wave in Germany. We found a negative effect of the pandemic on emotional responses. However, this was not the main predictor for young adults’ willingness to comply with COVID-19-related safety measures. Instead, individual differences in compliance were mainly predicted by cognitive and socio-structural variables. These results were confirmed in an independent data set. This study shows that individuals scoring high on negative affect during the pandemic are not necessarily more likely to comply with safety regulations. Instead, political measures should focus on cognitive interventions and the societal relevance of the health issue. These findings provide important insights into the basis of health-related concerns and feelings as well as behavioral adaptations.
Motives motivate human behavior. Most behaviors are driven by more than one motive, yet it is unclear how different motives interact and how such motive combinations affect the neural computation of the behaviors they drive. To answer this question, we induced two prosocial motives simultaneously (multi-motive condition) and separately (single motive conditions). After the different motive inductions, participants performed the same choice task in which they allocated points in favor of the other person (prosocial choice) or in favor of themselves (egoistic choice). We used fMRI to assess prosocial choice-related brain responses and drift diffusion modeling to specify how motive combinations affect individual components of the choice process. Our results showed that the combination of the two motives in the multi-motive condition increased participants' choice biases prior to the behavior itself. On the neural level, these changes in initial prosocial bias were associated with neural responses in the bilateral dorsal striatum. In contrast, the efficiency of the prosocial decision process was comparable between the multi-motive and the single-motive conditions. These findings provide insights into the computation of prosocial choices in complex motivational states, the motivational setting that drives most human behaviors .
The presence of a partner can attenuate physiological fear responses, a phenomenon known as social buffering. However, not all individuals are equally sociable. Here we investigated whether social buffering of fear is shaped by sensitivity to social anxiety (social concern) and whether these effects are different in females and males. We collected skin conductance responses (SCRs) and affect ratings of female and male participants when they experienced aversive and neutral sounds alone (alone treatment) or in the presence of an unknown person of the same gender (social treatment). Individual differences in social concern were assessed based on a well-established questionnaire. Our results showed that social concern had a stronger effect on social buffering in females than in males. The lower females scored on social concern, the stronger the SCRs reduction in the social compared to the alone treatment. The effect of social concern on social buffering of fear in females disappeared if participants were paired with a virtual agent instead of a real person. Together, these results showed that social buffering of human fear is shaped by gender and social concern. In females, the presence of virtual agents can buffer fear, irrespective of individual differences in social concern. These findings specify factors that shape the social modulation of human fear, and thus might be relevant for the treatment of anxiety disorders.
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
In human interactions, the facial expression of a bargaining partner may contain relevant information that affects prosocial decisions. We were interested in whether facial expressions of the recipient in the dictator game influence dictators´ ehavior. To test this, we conducted an online study (n = 106) based on a modified version of a dictator game. The dictators allocated money between themselves and another person (recipient), who had no possibility to respond to the dictator.
Importantly, before the allocation decision, the dictator was presented with the facial expression of the recipient (angry, disgusted, sad, smiling, or neutral). The results showed that dictators sent more money to recipients with sad or smiling facial expressions and less to recipients with angry or disgusted facial expressions compared with a neutral facial expression. Moreover, based on the sequential analysis of the decision and the interaction partner in the preceding trial, we found that decision-making depends upon previous interactions.
Background
The onset of mental illness such as depression and anxiety disorders in pregnancy and postpartum period is common. The coronavirus induced disease 2019 (COVID-19) pandemic and the resulting public policy responses represent an exceptional situation worldwide and there are hints for adverse psychosocial impact, hence, the study of psychological effects of the pandemic in women during hospitalization for delivery and in the postpartum period is highly relevant.
Methods
Patients who gave birth during the first wave of the COVID-19 pandemic in Germany (March to June 2020) at the Department of Obstetrics and Gynecology, University of Würzburg, Germany, were recruited at hospital admission for delivery. Biosamples were collected for analysis of SARS-CoV-2 infection and various stress hormones and interleukin-6 (IL-6). In addition to sociodemographic and medical obstetric data, survey questionnaires in relation to concerns about and fear of COVID-19, depression, stress, anxiety, loneliness, maternal self-efficacy and the mother–child bonding were administered at T1 (delivery stay) and T2 (3–6 months postpartum).
Results
In total, all 94 recruited patients had a moderate concern of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at T1 with a significant rise at T2. This concern correlated with low to low-medium general psychosocial stress levels and stress symptoms, and the women showed a significant increase of active coping from T1 to T2. Anxiety levels were low and the Edinburgh Postnatal Depression Scale showed a medium score of 5 with a significant (T1), but only week correlation with the concerns about SARS-CoV-2. In contrast to the overall good maternal bonding without correlation to SARS-CoV-2 concern, the maternal self-efficiency correlated negatively with the obstetric impairment caused by the COVID-19 pandemic.
Conclusion
Obstetric patients` concerns regarding SARS-CoV-2 and the accompanying pandemic increased during the course of the pandemic correlating positively with stress and depression. Of note is the increase in active coping over time and the overall good mother–child-bonding. Maternal self-efficacy was affected in part by the restrictions of the pandemic.
Despite its negative reputation, egoism – the excessive concern for one’s own welfare – can incite prosocial behavior. So far, however, egoism-based prosociality has received little attention. Here, we first provide an overview of the conditions under which egoism turns into a prosocial motive, review the benefits and limitations of egoism-based prosociality, and compare them with empathy-driven prosocial behavior. Second, we summarize studies investigating the neural processing of egoism-based prosocial decisions, studies investigating the neural processing of empathy-based prosocial decisions, and the small number of studies that compared the neural processing of prosocial decisions elicited by the different motives. We conclude that there is evidence for differential neural networks involved in egoism and empathy-based prosocial decisions. However, this evidence is not yet conclusive, because it is mainly based on the comparison of different experimental paradigms which may exaggerate or overshadow the effect of the different motivational states. Finally, we propose paradigms and research questions that should be tackled in future research that could help to specify how egoism can be used to enhance other prosocial behavior and motivation, and the how it could be tamed.
A variety of factors contribute to the degree to which a person feels lonely and socially isolated. These factors may be particularly relevant in contexts requiring social distancing, e.g., during the COVID-19 pandemic or in states of immunodeficiency. We present the Loneliness and Isolation during Social Distancing (LISD) Scale. Extending existing measures, the LISD scale measures both state and trait aspects of loneliness and isolation, including indicators of social connectedness and support. In addition, it reliably predicts individual differences in anxiety and depression. Data were collected online from two independent samples in a social distancing context (the COVID-19 pandemic). Factorial validation was based on exploratory factor analysis (EFA; Sample 1, N = 244) and confirmatory factor analysis (CFA; Sample 2, N = 304). Multiple regression analyses were used to assess how the LISD scale predicts state anxiety and depression. The LISD scale showed satisfactory fit in both samples. Its two state factors indicate being lonely and isolated as well as connected and supported, while its three trait factors reflect general loneliness and isolation, sociability and sense of belonging, and social closeness and support. Our results imply strong predictive power of the LISD scale for state anxiety and depression, explaining 33 and 51% of variance, respectively. Anxiety and depression scores were particularly predicted by low dispositional sociability and sense of belonging and by currently being more lonely and isolated. In turn, being lonely and isolated was related to being less connected and supported (state) as well as having lower social closeness and support in general (trait). We provide a novel scale which distinguishes between acute and general dimensions of loneliness and social isolation while also predicting mental health. The LISD scale could be a valuable and economic addition to the assessment of mental health factors impacted by social distancing.