A Decisive Anomaly: Why Interfaces Rather Than Stories Become Fixed in Dream

Abstract

This paper addresses a decisive anomaly identified in the Mayer (2025) report: in AI-related nightmares, 93% of cases fixate on the AI interaction interface itself rather than on narrative content. To explain this “formal fixation,” we propose a paradigm-shifting Interaction Architecture Internalization Model, which posits that the cognitive system internalizes the abstract logic and temporal structure of goal-directed interactions through the accumulation of a Learning Time Delay Dose. When this dose exceeds a critical threshold, a cognitive phase transition occurs, solidifying the interaction architecture as an internal framework. Grounded in insights from Piaget, Chomsky, Einstein, Wiener, and Landau, the model not only provides a unified explanation for phenomena from language acquisition to personality formation but also generates specific, empirically testable predictions. It forecasts, for instance, that systemic fluctuations in interaction delays (e.g., widespread server latency) will catalyze architectural internalization, a prediction corroborated by analyzed dream reports from such periods. Methodologically, the Learning Time Delay Equivalence Principle circumvents the “Problem of Other Minds,” establishing an objective foundation, while the theory's “blinded loop” validation—stemming from an academic misunderstanding—uniquely confirms its a priori predictive power. Ultimately, we advocate for a “Statistical Mechanics of Cognition,” where time delay dose acts as an order parameter, prioritizing the dynamics of form over the semantics of content.