Emotion as Compressed Rationality: The Heuristic Compression Model of Emotion (HCME) for Cognitive, Cultural, and AI Integration
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This paper introduces the Heuristic Compression Model of Emotion (HCME), a novel framework that reconceptualizes emotions as cognitively efficient heuristic compressions—fast, layered approximations that prioritize action and simplify decision-making in complex environments. Synthesizing insights from bounded rationality, affective neuroscience, embodied cognition, and cultural learning, HCME proposes that emotional states emerge from an interplay of evolutionarily shaped instincts, somatic feedback, social encoding, and cognitive appraisal. The model provides an elegant explanation for emotional intensity, contradiction, and dysfunction as trade-offs inherent to lossy compression strategies. HCME offers a unified structure that translates modern emotion theory into a format directly applicable to affective AI architectures. Its cross-disciplinary scope makes it valuable to psychologists, philosophers of mind, and AI developers seeking scalable frameworks for emotion-informed systems.
