Mapping social knowledge structures: A large-scale, data-driven approach to uncovering clinically relevant person profiles that shape social learning

Clinical symptomatology is reflected in social dimensions, but how these dimensions are structured and vary across individuals remains unclear. Here, we set out to examine core social dimensions underlying self-reported personality traits and item preferences. We leveraged these dimensions to derive latent person-level profiles, which explained meaningful variability in clinical symptomatology. Moreover, we show that generalizations of item- and person-level social information and associated clinical symptoms can be used as social knowledge structures across various social learning and decision-making contexts. In Study 1, a final sample of 1,529 participants completed extensive self-report measures of personality traits and item preferences, which were organized into six trait and four food preference dimensions based on Exploratory Graph Analysis (EGA). These EGA dimensions were used to specify person-level profiles with Latent Profile Analysis (LPA). The resulting profiles demonstrated external validity, generalizing to independent measures of personality and clinical symptoms. Notably, we identified a “Maladaptive Trait profile” and a “Raw-Produce-Aversion/Texture-Sensitive preference profile,” both associated with external measures such as elevated autistic traits and lower emotional stability, greater behavioral rigidity, and lower emotional stability. In Study 2, we assessed the robustness and generalizability of these dimensions and profiles in an independent sample of German-speaking participants (N=649). The dimensional structure showed partial replication across samples with preservation of core dimensions. The “Maladaptive Trait profile” demonstrated the strongest cross-sample correspondence, whereas broader food preference profiles replicated more consistently than narrower ones. Study 3 demonstrates how item dimensions and people profiles can be used to probe social learning. Social learning performance differed depending on the person profile in question. Together, this work establishes a unified, data-driven framework of item-level structure and person-level groupings that capture the samples’ considerable variability in clinical symptoms. This framework provides a way to investigate social learning and decision-making–key processes that are highly relevant to psychopathology across a wide range of clinical disorders.