Acceso usuarios
Courtland S. Hyatt, Brinkley M. Sharpe, Max M. Owens, Benjamin S. Listyg, Nathan T. Carter, Donald R. Lynam, Joshua D. Miller
Recent personality neuroscience research in large samples suggests that personality traits tend to bear null-to-small relations to morphometric (i.e., brain structure) regions of interest (ROIs). In this preregistered, two-part study using Human Connectome Project data (N = 1,105), we address the possibility that these null-to-small relations are due, in part, to the “level” (i.e., hierarchical placement) of personality and/or morphometry examined. We used a Five-Factor Model framework and operationalized personality in terms of meta-traits, domains, facets, and items; we operationalized morphometry in terms of omnibus measures (e.g., total brain volume), and cortical thickness and area in the ROIs of the Desikan and Destrieux atlases. First, we compared the patterns of effect sizes observed between these levels using mixed effects modeling. Second, we used a machine learning framework for estimating out-of-sample predictability. Results highlight that personality–morphometry relations are generally null-to-small no matter how they are operationalized. Relatively, the largest mean effect sizes were observed at the domain level of personality, but the largest individual effect sizes were observed at the facet and item level, particularly for the Ideas facet of Openness and its constituent items. The largest effect sizes observed were at the omnibus level of morphometry, and predictive models containing only omnibus variables were comparably predictive to models including both omnibus variable and ROIs. We conclude by encouraging researchers to search across levels of analysis when investigating relations between personality and morphometry and consider prioritizing omnibus measures, which appear to yield the largest and most consistent effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
