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

This 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 features act as the only accessible channel through which human cognitive orientation influences machine output. The paper shows how posture → form → statistical continuation → cognitive shift → new posture forms a recursive feedback loop. Drawing on linguistics, cognitive science, and empirical analysis, it demonstrates that resonance in human–AI interaction is not emotional but structural. The Structural Resonance Loop reframes digital ethics by shifting focus from content to form and establishes a new subfield: the ethics of interactional structure.