Measuring Candidate Machine-Consciousness Mechanisms in Self-Aware Networks

This corrected preprint presents its research question, method, bounded result, and principal limitations in a standardized reader-facing format. Artificial systems can imitate reports, confidence, memory, personality, and self-description without thereby establishing subjective experience. This makes behavior-only tests vulnerable to a gaming problem: an indicator can be optimized while the mechanism it was intended to track is absent. This paper converts a 2021 Self Aware Networks (SAN) proposal for a guidebook on recognizing possible machine consciousness into a preregisterable machine-assessment protocol. The protocol is a companion to Tomographic Coherence-based Neural Rendering (TCNR), not a replacement for it. TCNR supplies a relational reconstruction hypothesis and a historical Tomographic Consciousness Index (TCI). It belongs to the Self-Aware Networks neuroscience and consciousness research program. This is a corrected preprint edition released under the Creative Commons Attribution 4.0 license.