Neurocognitive Adaptation in Symbiotic Intelligence

This paper proposes an architecture-based model of neurocognitive adaptation in sustained human–LLM interaction. Building on the epistemic framework of Symbiotic Intelligence, it introduces the concept of delegation gradients and differentiates adaptive from erosive functional reorganization patterns. The contribution is conceptual and programmatic. No empirical data are presented. Instead, the paper formulates testable hypotheses and outlines a longitudinal research framework for investigating executive control, metacognition, cognitive offloading, and automation bias in symbiotic human–AI systems. The central thesis is that long-term cognitive outcomes depend not on AI use per se, but on interaction architecture—specifically the distribution of self-generated activity, friction intensity, delegation gradients, and epistemic responsibility within the dyad.