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A positive view of oneself is important for a healthy lifestyle. Self-protection mechanisms such as suppressing negative self-related information help us to maintain a positive view of ourselves. This is of special relevance when, for instance, a negative test result threatens our positive self-view. To date, it is not clear which brain areas support self-protective mechanisms under self-threat. In the present functional magnetic resonance imaging (fMRI) study the participants (N = 46) received a (negative vs. positive) performance test feedback before entering the scanner. In the scanner, the participants were instructed to ascribe personality traits either to themselves or to a famous other. Our results showed that participants responded slower to negative self-related traits compared to positive self-related traits. High self-esteem individuals responded slower to negative traits compared to low self-esteem individuals following a self-threat. This indicates that high self-esteem individuals engage more in self-enhancing strategies after a threat by inhibiting negative self-related information more successfully than low self-esteem individuals. This behavioral pattern was mirrored in the fMRI data as dACC correlated positively with trait self-esteem. Generally, ACC activation was attenuated under threat when participants evaluated self-relevant traits and even more for negative self-related traits. We also found that activation in the ACC was negatively correlated with response times, indicating that greater activation of the ACC is linked to better access (faster response) to positive self-related traits and to impaired access (slower response) to negative self-related traits. These results confirm the ACC function as important in managing threatened self-worth but indicate differences in trait self-esteem levels. The fMRI analyses also revealed a decrease in activation within the left Hippocampus and the right thalamus under threat. This indicates that a down-regulation of activation in these regions might also serve as coping mechanism in dealing with self-threat.

Restrictive measures are indispensable to achieving sustainable and low-carbon mobility. At the same time, these are often not implemented due to concerns that public support will not materialize; therefore, they are relegated to the background in the debate on policy measures that can be applied to change mobility behavior. In this national study ( N = 1,083), we used structural equation modeling (SEM) to examine the drivers of and differences between regulatory and economic transport policies. We find that policy-specific beliefs and, in particular, perceived fairness are key drivers of public acceptance. Our results indicate that policies must be perceived as fair, effective, and minimally intrusive for them to be accepted by the public and thus implementable. No major differences were found between the two types of policies examined, namely, regulatory and economic policies. Overall, public acceptance of the proposed measures is low. We discuss these results in terms of the study content and methodology and conclude by describing their implications for transport policy design.

Social scientists and psychologists who study environmental issues need to improve their measures to capture relevant pro-environmental behaviors to reduce greenhouse gas emissions – the main driver of climate change. They also need to identify meaningful predictors for these behaviors, which go beyond mere statistical significance. In this large representative study of the Austrian population ( N  = 1,083), we aim at addressing both issues. We focus on relevant and specific energy-related behavioral intentions (traveling, electricity consumption and heating) and test a set of preregistered social-psychological predictors in path models, followed by an exploratory machine-learning approach. We show that a combination of some prominent predictors – perceived behavior control, consideration of future and immediate consequences, and willingness to sacrifice – accounts for only 20 to 30% of variance in behavioral intentions. We suggest that future studies confirm our results in other cultures and set even higher qualitative benchmarks for measures and predictors.