The Heuristic Compression Model of Emotion (HCME): A Novel Information-Theoretic Approach to Emotional Cognition and AI Architecture
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The Heuristic Compression Model of Emotion (HCME) proposes a novel information-theoretic account of emotion: emotions function as adaptive lossy compression algorithms under constraints of bounded rationality. Unlike traditional theories that frame emotions primarily as evolved heuristics, affective signals, or context-dependent appraisals, HCME positions emotion as a computationally efficient mechanism for reducing cognitive and attentional complexity in environments where complete modeling is intractable. This compression enables action, attention allocation, and decision-making under resource scarcity. HCME provides a unified framework that integrates affective science, cognitive architecture, and artificial intelligence design. It is empirically testable and functionally implementable within synthetic agents, offering design principles for resource-bound AI systems that require rapid prioritization without full environmental modeling. By foregrounding emotional computation as compression rather than distortion, HCME reframes emotion not as a deviation from rationality, but as the very mechanism that makes practical reasoning—and personality—possible.
