A social network is a web that integrates multiple levels of interindividual social relationships and has direct associations with an individual’s health and well-being. Previous research has mainly focused on how brain and social network structures act on each other and on how the brain supports the spread of ideas and behaviors within social networks. The structure of the social network is correlated with activity in the amygdala, which links decoding and interpreting social signals and social values. The structure also (...) relies on the mentalizing network, which is central to an individual’s ability to infer the mental states of others. Network functional properties depend on multilayer brain-social networks, indicating that information transmission is supported by the default mode system, the valuation system, and the mentalizing system. From the perspective of neuroendocrinology, overwhelming evidence shows that variations in oxytocin, β-endorphin and dopamine receptor genes, including oxytocin receptor, mu opioid receptor 1 and dopamine receptor 2, predict an individual’s social network structure, whereas oxytocin also contributes to improved transmission of emotional and behavioral information from person to person. Overall, previous studies have comprehensively revealed the effects of the brain, endocrine system, and genes on social networks. Future studies are required to determine the effects of cognitive abilities, such as memory, on social networks, the characteristics and neural mechanism of social networks in mental illness and how social networks change over time through the use of longitudinal methods. (shrink)

Decision-making competence refers to the ability to make better decisions, as defined by decision-making principles posited by models of rational choice. The adult decision-making competence (A-DMC) scale is a relatively mature evaluation tool used for decision-making competence. However, the A-DMC is yet far from other mature psychological evaluation tools, and especially the structure of A-DMC remains unclear. In the current study, we estimated a regularized partial correlation network of decision-making competence in a Chinese sample consisting of 339 adults who were (...) evaluated by the A-DMC, and then the centrality indicators were calculated. The results revealed that all nodes of the decision-making competence networks are positively associated, except for the association of resistance to framing (RF) and resistance to sunk costs (SC). The strongest edge was between RF and applying decision rules (DR; regularized partial correlation = 0.37). The centrality indicators of RF and applying DR were highest, revealing that these two variables may play important roles in the decision-making competence network. Our study conceptualizes the decision-making competence from network perspectives, so as to provide some insights for future researches. (shrink)