Canonical Definition of Co-Cognition: Distributed Human-AI Cognition

Co-Cognition describes the distributed process through which human cognition and external systems, particularly AI systems, operate in a shared feedback loop, jointly generating, refining, and applying representations. Rather than treating AI systems as passive tools, this paper examines how iterative interaction changes the structure, direction, and mediation of thought itself. The document introduces Co-Cognition as a core concept within the Reality Drift framework and explores how cognition becomes distributed across human and system layers through recursive interaction. It outlines the mechanisms that shape shared reasoning loops, including semantic fidelity, recursive compression, cognitive grounding, and constraint preservation. The paper examines observable patterns of AI-mediated cognition across work, education, creativity, and knowledge systems, while also addressing the risks introduced when representations become increasingly coherent without remaining grounded in reality. It positions Co-Cognition as both a capability amplifier and a pathway through which drift can emerge when feedback loops lose constraint and human direction. Topics include distributed cognition, AI-assisted reasoning, cognitive scaffolding, recursive feedback loops, semantic fidelity, cognitive drift, externalized thinking, human-in-the-loop reasoning, and the evolving relationship between human thought and intelligent systems.