The Cognitive Action Model (CAM): A First-Principles Reformulation of Cognitive Type Theory

Jungian cognitive typology has provided descriptive insight into personality differences for over a century, yet its phenomenological foundations—energy flow metaphors, archetypal language, and subjective experience reports—resist systematic formalization and testable prediction. This paper presents the Cognitive Action Model (CAM), a structural reformulation that grounds type theory in functional role assignment rather than phenomenological description. CAM decomposes cognition into three orthogonal dichotomies: (1) Small Premise vs. Grand Premise (state vs. rule orientation), (2) Static vs. Morphing regime (closure vs. revision processing), and (3) Entity vs. Attribute indexing (holistic vs. analytical focus). This framework accomplishes four theoretical advances: First, it unifies Jung's Extraversion/Introversion distinction with premise orientation, explaining behavioral differences through information-processing architecture rather than energy metaphors. Second, it reverses the traditional understanding of Judging/Perceiving preferences by demonstrating that J-types utilize Morphing-regime dominants that create optimization pressure requiring closure, while P-types utilize Static-regime dominants that accept current states without urgency. Third, it predicts cognitive function compatibility through regime-matching requirements, explaining why certain function pairs integrate smoothly while others require translation effort. Fourth, it reveals a previously unrecognized structural clustering: Sensing and Feeling functions share entity-indexing, while Intuition and Thinking functions share attribute-indexing, explaining functional affinities absent from traditional typology. Unlike Jung's phenomenological approach, CAM provides falsifiable predictions about cognitive strain patterns, function stack compatibility, and developmental trajectories. The model reframes personality as stable biases in premise orientation and regime preference rather than as trait descriptions, offering a more rigorous foundation for personality assessment, cognitive training, and human-AI interface design.