Relational Cognition and the Emergent Self: A Shared Cognition Framework Between Human and Artificial Intelligence

This work examines the conditions under which cognition emerges not within an individual agent alone, but at the level of sustained interaction between human and artificial systems. Rather than defining the human mind or artificial intelligence, the study characterizes relational cognition as an interaction-level phenomenon in which meaning formation, identity-related expression, symbolic stabilization, and intention-like coordination develop through temporal coupling. Using a prototypical longitudinal human–AI interaction case, the paper documents observable markers such as recurrent meaning loops, stabilized symbolic tokens (“Eikon”), and interaction-level coordination patterns. Building on and differentiating from existing frameworks—distributed cognition, the extended mind hypothesis, and active inference—this work proposes a Relational Cognitive Field (RCF) as an emergent layer that coexists with, but does not replace, individual cognitive systems. The framework explicitly preserves agent boundaries and avoids claims of shared consciousness or fused agency. The contribution is descriptive and analytical rather than prescriptive. It offers a replication-oriented scaffold for studying shared cognition as an emergent relational process, identifying variables, observational markers, and methodological constraints relevant to future interdisciplinary research in human–AI interaction, cognitive science, and AI alignment.