This paper presents a spatial intervention protocol that integrates large language models (LLMs) as "externalized generative models" within an individual's cognitive process to non-invasively attenuate activity in the Default Mode Network (DMN), the neural substrate of self-referential thought. Grounded in the Free Energy Principle (FEP) and the framework of Active Inference, we theorize a mechanism by which delegating high-order inferential load to an external AI architecture reduces the "complexity" cost of intra-cranial computation. This reduction is proposed to induce a reallocation of attentional resources toward interoceptive awareness—a state herein termed Perceptual Liberation. The protocol is structured as a three-phase closed-loop system integrating voice-based AI interaction with biometrically responsive environmental parameters (light, acoustics, vibration). This paper presents the theoretical framework and system design rationale; quantitative validation is designated for future empirical research. The intervention is proposed as a cognitive augmentation protocol and is not intended as a substitute for clinical psychiatric treatment.
Renya Ueno (Mon,) studied this question.
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