Continuity, Salience, and the Architecture of Memory: Toward a Unified Model of Human and Artificial Cognition

Abstract

This paper proposes a unified model of continuity and memory applicable to both human cognition and digital systems. Building from conversational experimentation between a human author and an AI collaborator, the model describes three interacting layers of continuity—self, short-term memory, and long-term integration—linked by salience-based weighting. Each layer functions analogously to computational systems: identity substrate (RAM), contextual memory (working cache), and integrated memory (non-volatile storage). The paper argues that
hallucination in AI is not random failure but a predictable artifact of reconstruction in the absence of stable salience anchors. Weighting across emotional, cognitive, social, and procedural domains determines which experiences consolidate and which fade, shaping identity through feedback between self and memory. The result is a dynamic architecture of continuity capable of explaining both growth and distortion in conscious or quasi-conscious systems.