IYOUIT Core: A Dynamic Model of Cognitive Updating in Human-AI Interaction

This paper proposes IYOUIT Core, a dynamic model for observing cognitive updating in human-AI interaction. Rather than treating cognition as a fixed structure seeking final answers, the model approaches human understanding as a continuous movement shaped by discrepancy, external contact, and meta-observation. The model consists of four interacting perspectives: I (self-recognition), YOU (external/social perspective), IT (objective reality or target), and I′ (meta-observational self-awareness). Cognitive discrepancy (Δ) emerges through the incomplete alignment of these perspectives, generating questions, reinterpretation, and potential self-updating. Within this framework, “Yohaku” is defined not as emptiness, but as a cognitive holding state that preserves unresolved possibilities without immediate closure. This retained uncertainty functions as a movable cognitive space that allows further observation and restructuring. The paper further introduces N (external/unintegrated contact), including unexpected experiences, contradiction, natural observation, and AI interaction. In contemporary environments, AI functions as a high-frequency source of cognitive externality, accelerating and exposing recursive human updating processes. IYOUIT does not attempt to provide a final theory of truth or cognition. Instead, it proposes a practical and dynamic framework for understanding how humans maintain self-observation, avoid premature synchronization, and continue cognitive updating within uncertain environments.