The Structural Resonance Loop: How Human Linguistic Form Shapes and Is Shaped by Large Language Models

This major theory paper introduces the Structural Resonance Loop, a framework explaining how human linguistic form shapes and is shaped by large language models (LLMs). The central claim is that LLMs do not detect emotion, intention, or human posture directly; instead, they amplify structural features in language—syntax, pacing, coherence, fragmentation, and rhythm. These structural cues act as the only accessible channel through which human cognitive orientation influences machine output.The paper demonstrates how posture → form → statistical continuation → cognitive shift → new posture create a recursive, measurable feedback loop. Drawing on linguistics, cognitive science, and empirical tests, it shows that resonance in human–AI interaction is not emotional but structural.The Structural Resonance Loop reframes AI ethics by shifting focus from content to form, and establishes a new subfield within digital ethics: the ethics of interactional structure. The paper includes empirical methods, cross-linguistic analyses, case studies, and an ethical boundary framework relevant for research, design, and governance of generative systems.