Machine-Learning Decomposition Identifies a Big Two Structure in Human Personality with Distinct Neurocognitive Profiles.

Personality neuroscience has traditionally relied on the Big Five model to investigate trait structure and its relationship to individual differences in brain organization and life outcomes. However, existing theoretical frameworks explain only part of the item-level covariance, raising questions about whether alternative factor solutions might complement the canonical five-factor model. Here, we applied an additive and part-based machine learning decomposition to a mega-scale, global dataset (n = 1,336,840) to systematically evaluate trait structure across factor resolutions. Beyond reproducing the canonical Big Five, we identified a robust Big Two comprising Social Adaptation and Spontaneous Mentation. Social Adaptation integrates covarying questionnaire items from Extraversion, Agreeableness, and Conscientiousness, indexing externally oriented social functioning. Spontaneous Mentation, in turn, aggregates Neuroticism with introspective facets of Openness, capturing internally directed affective-cognitive exploration. Embedding individuals in this Big Two space revealed structured manifolds along which neurocognitive profiles aligned with distinct trait orientations. Importantly, this lower-dimensional representation improved prediction of functional brain connectivity relative to Big Five scores, while preserving comparable associations with cognition and mental health. Together, these results establish a neurocognitively grounded Big Two framework that complements the Big Five and offers an interpretable bridge between personality structure, cognitive functioning, and psychopathology.