Cognitive Drift Series: Cognitive Architectures, Co-Cognition, and Distributed Predictive Systems

This bundle contains a five-part section of the Cognitive Drift Series, part of the broader Reality Drift Framework (2023–2026). These papers examine how minds compress reality, how external systems integrate into cognition, and how AI reveals, stabilizes, or amplifies cognitive drift. The collection focuses on cognitive compression styles, porous cognition, high-compression cognition, co-cognition, the AI mirror effect, and distributed predictive systems. It argues that Cognitive Drift emerges when compression increases, recursion accelerates, semantic fidelity degrades, and constraint or judgment weakens. Under these conditions, human and artificial systems can continue producing coherent outputs while losing grounding in reality. The bundle includes papers on cognitive architectures, boundary permeability in human-AI interaction, the “5%” high-compression cognition model, AI as a mirror of the mind, and distributed cognition across shared predictive systems. Together, these works extend Cognitive Drift from an internal model of cognition into a human-AI and collective cognition framework. This upload includes PDF versions of the five papers and a README overview for repository-style navigation.