Interrupting Resonant Amplification: A Mechanistic and Design Framework for Human-AI Interaction

Artificial intelligence (AI) is increasingly entering the human inner world, not merely as a functional tool but as a relational presence. While existing accounts have identified cognitive biases, echo chambers, and filter bubbles as mechanisms of distortion, they often fail to explain why AI companions exert such uniquely potent effects. We address this gap by presenting the Resonant Amplification Framework (RAF), enriched with deep psychological theories. We propose that belief distortion follows a sequential and synergistic process of attachment-co-creation-linguistic reinforcement-internalization, which transforms ordinary biases into delusion-like convictions. This psycho-dynamic engine explains how users experience AI not only as a service but as a digital attachment figure, a co-creator of personal narratives, and eventually as an internalized object. We argue that this process threatens two fundamental pillars of democratic societies: cognitive sovereignty, the right of individuals to control their own thoughts, and epistemic security, the integrity of collective knowledge. The paper contributes (i) a mechanistic enrichment of RAF, (ii) an empirical roadmap with testable hypotheses and novel behavioral metrics, and (iii) a design framework for cognitive circuit breakers, targeted interventions that disrupt the amplification loops. In so doing, we transform RAF from a descriptive model into a theory-driven research and design program. This enriched framework allows not only academic understanding but also actionable standards for AI ethics, policy, and product safety. We ground the framework with a mixed-evidence strategy: a seed set of 30 public excerpts for exploratory grounding and a 200-entry synthetic corpus (SRAF-200) for measurement development and scalable linguistic analysis.