Mapping the Self: A network approach for understanding psychological and neural representations of self-concept structure

How people self-reflect and maintain a coherent sense of self is an important question that spans from early philosophy to modern psychology and neuroscience. Research on the self-concept has not yet developed and tested a formal model of how diverse trait self-perceptions cohere and depend upon one another. We first develop a network-based approach, which suggests normative beliefs about trait relationships contribute to how the self-concept is structured (Study 1). This model explains how people maintain positivity and coherence in self-evaluations and how trait interrelations shape activation in brain regions involved in self-referential processing and concept representation (Study 2 and Study 3). Results reveal that a network-based property theorized to preserve coherence (i.e., outdegree centrality) is associated with more favorable and consistent self-evaluations and decreased vmPFC activation. Further, participants higher in self-esteem and lower in depressive symptoms differentiate between higher and lower centrality traits more in self-evaluations, suggesting that emphasizing central, stability preserving traits may be protective for mental health. Together, our model and findings join individual differences, brain activity, and behavior to present a computational theory of how normative beliefs about trait relationships contribute to a coherent, interconnected self-concept.