ABSTRACTNumerous studies have found that shame increases individuals’ anger at others. However, according to recent theories about the social function of shame and anger at others, it is possible that shame controls individuals’ anger at others in specific conditions. We replicated previous findings that shame increased individuals’ anger at others’ unfairness, when others were not aware of the individual’s experience of shameful events. We also found for the first time that shame controlled or even decreased individuals’ anger at others’ unfairness, (...) when others were aware of the individual’s experience of shameful events. The results were consistent when shame was induced by either a recall paradigm or an imagination paradigm, and in either the ultimatum game or the dictator game. This suggests that shame strategically controls individuals’ anger at others to demonstrate that they are willing to benefit others, when facing the risk of social exclusion. Our findings highlight the interpersonal function of... (shrink)

This study chooses Chinese athletes as the research object and constructs the overall network of its social support network and discussion network. From the micro-, meso-, and macrolevels of the social network structure, the structure and characteristics of the athlete’s overall social network are analyzed. Through research, we found that there is embeddedness, that is, the relevance, between society support networks, between society discussion networks, and between society support networks and society discussion networks. At the same time, in the athletes’ (...) social support network and social discussion network, some athletes have no contact with other players; they have no “power” in the group as well, so it is difficult to obtain network resources. We also found that there are small-world characteristics in the social network of Chinese professional athletes. The above findings will provide a deeper understanding of the peculiarities of athlete groups and have certain practical significance for improving athletes’ daily training and life management conditions. (shrink)

In this article, we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model with given observational data. We build on an algorithm proposed by Wang and Drton, and we show that one can augment the hidden variable structure of the recovered model by learning multidirected edges rather than only directed and bidirected ones. Multidirected edges appear when more than two of the observed variables have a hidden common cause. We detect the (...) presence of such hidden causes by looking at higher order cumulants and exploiting the multi-trek rule. Our method recovers the correct structure when the underlying graph is a bow-free acyclic mixed graph with potential multidirected edges. (shrink)