Canonical Co-Cognition: Human-AI Feedback Loops and Distributed Thought
Year: 2026
Type: article
Abstract: This document presents a canonical definition and concept paper for Co-Cognition, part of the Reality Drift framework by A. Jacobs. Co-Cognition describes the structural process in which human cognition and external systems, especially AI systems, operate in a shared feedback loop, jointly generating, refining, and applying representations.The paper distinguishes Co-Cognition from ordinary tool use, information retrieval, or simple automation. It focuses on what happens when AI systems begin shaping the structure, sequence, and direction of thought itself. In this process, ideas often emerge through interaction rather than prior formulation, and cognition becomes distributed across internal reasoning and external system mediation.The document outlines the mechanism of Co-Cognition as a recursive interaction loop: human thought is externalized through language or partial representation, the system transforms that input into structured output, and the user interprets and reintegrates the output into the next stage of thought.It also identifies observable patterns, cross-domain effects, theoretical context, and risks. Co-Cognitive loops can support insight, cognitive scaffolding, and faster conceptual refinement, but they can also amplify drift when semantic fidelity, grounding, user intent, and stop conditions weaken.
Source: Figshare
Author: Cognitive Drift Papers
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7167677598