A High-Dimensional Data-Driven Modularity Analysis Reveals Five Distinct Personality Clusters with Different Psychological Profiles

Personality is a fundamental aspect of human behavior, shaping how individuals perceive, interact with, and adapt to their environments. Despite extensive research, establishing a generalizable taxonomy of personality types remains challenging. Conventional personality assessments typically categorize individuals based on aggregated trait scores, which compress item-level variability and overlook how people with identical trait levels may express those traits through different response patterns. However, the reducing rich item-level information into a single aggregated score can obscure meaningful subgroups within the population and restrict the capacity to identify distinct personality profiles. Addressing this limitation is therefore essential for advancing theoretical models of personality structure and improving psychological assessment.Here, we leveraged 60-item NEO Five-Factor Inventory (NEO-FFI) data from the HCP dataset (N=1206) to investigate personality clustering using an item-pattern-based modularity (IPBM) approach. This framework emphasizes personality categorization based on response styles rather than aggregated trait values, thereby offering a more nuanced characterization of personality structure. Our graph-theoretical clustering analysis identified five distinct personality profiles. While two clusters aligned with commonly reported resilient and under-controlled types, others reflected less frequently described configurations; to the best of our knowledge, one of these clusters has not been documented in prior work.We further incorporated NIMH Toolbox assessments to examine differences across clusters in negative affect, well-being, and self-efficacy. Significant differences emerged for negative affect, well-being, and self-efficacy, demonstrating that item-level personality patterns carry meaningful implications for psychological functioning. Together, these findings highlight the value of response-pattern-based approaches in refining personality taxonomies and underscore how item-level characteristics can yield precise insights into personality and its associations with key indicators of emotional health and adaptive functioning. This framework may enhance personalized assessment strategies and inform the development of more targeted psychological interventions.