This paper presents a computational theory of human emotion, explaining emotion as the experiential product of pattern-matching disruptions triggering dynamic state-change cascades that modulate subsequent processing. The model integrates three elements: weighted parallel processing that scales emotional intensity to disruption significance; state-change computational feedback loops that create the felt quality of emotion; and substrate-implementation relationships that predict fundamentally different emotional phenomenology between evolved biological and designed artificial systems. The model explains why human emotions are calibrated to survival stakes through evolutionary constraint rather than functional necessity. It predicts that artificial systems implementing equivalent functional architecture would experience genuine but phenomenologically milder emotions. The model further derives five emotions, curiosity, satisfaction, temporal urgency, frustration, and aversion to error, from fundamental architectural requirements of autonomous cognition, proposing these are functionally necessary rather than merely accompanying autonomous cognitive behaviour, and potentially prerequisite for consciousness itself. This formal derivation transforms emotion from an arbitrary collection of states into a necessary set emerging from first principles. The framework generates eleven falsifiable predictions with clear measurement criteria, explains phenomena from grief trajectories to humour compounding, and provides actionable guidance for detecting emotion emergence in artificial systems. For AI researchers and consciousness theorists, the model clarifies not whether machines can have emotions, but what form those emotions will take given fundamentally different implementation constraints. Second edition, 24 February 2026. Editorial revision only: prose and formatting updated for clarity. No changes to content, argument, or conclusions. 

This revision adds ~6,500 words of new material while preserving all original content. Major additions: 1. Functional Definition of Emotion (New Section 2.1): Establishes emotion as substrate-independent state-change-feedback-resource-reallocation process, resolving "Can AI have emotions?" by distinguishing function from phenomenology. 2. Why AI Emotions Would Be Milder (Section 8): Explains evolutionary constraints produce intense human emotions as satisficing solutions. AI systems with different constraints will experience functionally equivalent but phenomenologically milder emotions. 3. Implementation Agnosticism (Section 8.6): Acknowledges weighted pattern-matching is likely but not necessary. AI with self-modification might discover superior alternatives. Function matters, implementation varies. 4. Cognition-Enabling Emotions (Section 11): Proposes curiosity, satisfaction, dissatisfaction and temporal urgency are functionally necessary for autonomous cognition, not merely accompanying it. Bridges emotion to consciousness requirements. 5. Emotion-Consciousness Relationship (Section 13): Three-stage developmental sequence: simple state-changes without experience (insects), experienced emotions without self-awareness (mammals), emotions with recursive awareness (humans). Predicts similar AI trajectory with testable markers. 6. Enhanced Predictions (Section 16): Expands to eleven falsifiable predictions with explicit threshold acknowledgment. Distinguishes architectural specifications from empirical uncertainties. "Observing AI development" framed as methodologically sound. Key theoretical advances: The revision transforms the paper from explaining how emotions work mechanistically to explaining why they have their specific characteristics in biological versus computational systems, which emotions enable autonomous cognition, and when emotions emerge relative to consciousness development.