The Network That Learns It Is a Network: From Predictive Selfhood to Explicit Network Self-Knowledge

This theory-and-methods paper distinguishes self-registration, learned self-modeling, embodied first-person perspective, and explicit network self-knowledge. It traces a source-bounded Self Aware Networks genealogy from 2011 through 2025 and proposes a controlled educational experiment that tests mechanistic graph learning, counterfactual reasoning, novel transfer, calibration, updating, and retention. The central claim is intentionally limited: the experiment tests whether people can acquire a usable and revisable account of the distributed mechanism that constructs their self-model; it does not assume that reading a theory increases consciousness itself. This is version 1.0 of the preprint.