Researchers exploring machine-augmented cognition face the challenge of maintaining identity coherence while expanding cognitive reach. This methodological framework treats human-AI interfaces as sites of conscious co-authorship, providing transparent processes for designing cognitive extensions that strengthen rather than fragment core identity.
AuthorJohn Deacon
Independent AI research and systems practitioner focused on semantic models of cognition and strategic logic. He developed the Core Alignment Model (CAM) and XEMATIX, a cognitive software framework designed to translate strategic reasoning into executable logic and structure. His work explores the intersection of language, design, and decision systems to support scalable alignment between human intent and digital execution.
This article was composed with Cognitive Publishing
More info at bio.johndeacon.co.za