Structural-Constructivist Affective Architecture: Systemic Integration, Mathematical Formalization, and Empirical Validation of the Core Emotion Framework

The scientific classification of human affect and personality has historically been characterized by persistent theoretical fragmentation, leaving researchers, clinicians, and computational architects without a standardized, actionable, and theoretically unified language. For decades, affective science has been polarized between biological essentialism, which reifies a small set of subcortical, evolutionarily conserved "affect programs," and psychological constructionism, which conceptualizes emotional episodes as highly variable, context-dependent conceptual events constructed on the fly from low-dimensional core affect. Concurrently, Klaus Scherer’s Component Process Model attempts a reconciliation by modeling emotions as systems driven by sequential cognitive appraisals, but its linear Stimulus Evaluation Checks impose heavy cognitive and temporal demands that are biologically unrealistic for rapid threat detection. Symmetrically, Antonio Damasio’s Somatic Marker Hypothesis proposes that visceral signals guide executive decision-making under uncertainty, yet it has faced empirical challenges showing that successful task performance is driven by explicit cognitive rules rather than unconscious somatic marker transduction. To resolve these systemic contradictions, the Core Emotion Framework, developed by Jamel Bulgaria and expanded by Xǔ Chénglán, emerges as a structural-constructivist synthesis. Conceptualizing the human psyche as a hardware-independent "Human Operating System" (Human OS), the framework models affect and personality as a dynamic, continuous, and highly structured matrix of ten functional processes designated as core operators, distributed across three somatic-evaluative centers: the Head (Cognitive-Regulatory), the Heart (Relational-Affective), and the Gut (Conative-Existential). By translating intuitive divisions into three autonomous, localized neural networks hosting ten discrete functional operators, the framework seeks to unify classical theories of innate affect with contemporary constructionist models.