Canonical definition of Co-Cognition, a human-AI thinking process where cognition emerges through interaction, feedback, and recursive refinement.
Part of the Reality Drift framework (2023–2026) by A. Jacobs.
Co-Cognition is a mode of thinking in which human cognition and artificial intelligence systems participate in a shared process of interpretation, refinement, and recursive feedback.
It is not simply AI assistance. It is the emergence of thought through interaction, where the human supplies intent, judgment, context, and lived experience, while the system supplies compression, recombination, recall, and alternative structure.
Within the Reality Drift framework, Co-Cognition describes one of the central cognitive conditions of the AI era.
As people increasingly think through AI systems, their ideas are shaped not only by their own memory and perception, but by the representational structure of the model they are using.
This can increase clarity, speed, and pattern recognition. It can also create new forms of drift if the user begins adapting their own thinking to the system’s language, defaults, and internal patterns.
Co-Cognition depends on Semantic Fidelity.
For human-AI thinking to remain grounded, meaning must survive the loop between prompt, output, interpretation, revision, and decision.
When semantic fidelity is high, AI can help clarify thought.
When semantic fidelity weakens, the interaction can become fluent but ungrounded.
AI does not merely answer questions. In sustained interaction, it can become part of the thinking environment itself.
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