Theoretical Convergence and Empirical Divergence: A Comparative Evaluation of Two Dimensional Models of Affect and Jamel Bulgaria’s Core Emotion Framework

This manuscript reevaluates the two dimensional valence–arousal paradigm and situates Jamel Bulgaria’s Core Emotion Framework (CEF) as a structurally richer alternative capable of resolving the limitations inherent in low dimensional affect models. Beginning with the historical divide between essentialist and constructivist theories (Ekman, 1992; Panksepp, 1998; Barrett, 2006, 2017), the analysis shows that low dimensional affect spaces—despite their conceptual elegance (Russell, 1980)—systematically obscure critical components of emotional meaning due to methodological compression, stimulus sampling artifacts, and the ecological fallacy. The CEF does not treat the circumplex as a mechanistic theory to be falsified, but as a descriptive projection that compresses higher dimensional structure; the framework therefore extends the circumplex by specifying the generative operators that produce the observed valence–arousal patterns. A synthesis of multilevel modeling across eight large datasets reveals that the canonical V shaped relation between valence and arousal is weak, probabilistic, culturally modulated, and highly variable at the individual level (Kuppens et al., 2006; Yik et al., 2023; Koval et al., 2023). Building on the Grid Study’s multidimensional findings—which empirically recover four semantic dimensions beyond valence and arousal (Fontaine et al., 2007)—the CEF reframes emotional life as a dynamic, high dimensional architecture of ten functional operators distributed across the Head, Heart, and Gut centers of the Human Operating System (Bulgaria, 2023, 2024a, 2024b, 2024c). Within this constructivist landscape, the CEF treats operators not as innate emotion modules but as recurrent computational motifs—functional constraints on construction that emerge across appraisal, somatic, and relational systems. Psychometric analyses show excellent absolute model fit but poor incremental fit under traditional CFA, a divergence that reflects the framework’s predicted cross activation and fusion dynamics rather than structural inadequacy. The CEF further provides a mechanistic account of clinical psychopathology as maladaptive operator fusions, modeled using quantum probability theory to capture contextuality and non separability in severe affective states where classical probability fails (Busemeyer & Bruza, 2012). The framework has direct implications for computational affective modeling, clinical intervention, and the development of high dimensional AI systems capable of recognizing operator level emotional structure. Valence–arousal models remain valuable as low dimensional summaries of population level affective structure; the CEF clarifies the operator level processes that generate these summaries and explains why they vary across individuals, cultures, and contexts.