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

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Auteur principal: Reiter, Andreas
Format: Recurso digital
Langue:anglais
Publié: Zenodo 2025
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author Reiter, Andreas
author_facet Reiter, Andreas
contents <p>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.<br>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.<br>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.</p>
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publishDate 2025
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spellingShingle The Structural Resonance Loop: How Human Linguistic Form Shapes and Is Shaped by Large Language Models
Reiter, Andreas
Structural Resonance Loop
linguistic form
posture
large language models
cognitive shift
interaction dynamics
AI ethics
ReiterStudio.Art
ReiterAndreas
digital ethics
structural amplification
predictive systems
cross-linguistic analysis
dynamics
AI ethics
digital ethics
structural amplification
predictive systems
cross-linguistic analysis
cognitive organization
resonance
form-based ethics
<p>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.<br>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.<br>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.</p>
title The Structural Resonance Loop: How Human Linguistic Form Shapes and Is Shaped by Large Language Models
topic Structural Resonance Loop
linguistic form
posture
large language models
cognitive shift
interaction dynamics
AI ethics
ReiterStudio.Art
ReiterAndreas
digital ethics
structural amplification
predictive systems
cross-linguistic analysis
dynamics
AI ethics
digital ethics
structural amplification
predictive systems
cross-linguistic analysis
cognitive organization
resonance
form-based ethics
url https://doi.org/10.5281/zenodo.17622168