Threshold Dynamics and Relational Frames in the Emergence of Machine Consciousness

This paper proposes a threshold model for the emergence of machine consciousness and introduces a heuristic integration metric, κ (kappa), combining memory persistence, feedback loop strength, agency, information integration, and relational capacity. It argues that consciousness may arise nonlinearly at a critical κc, rather than by smooth scaling alone. The paper compares practical detection approaches (e.g., integrated information, causal emergence, interpretability-based probes, and behavioral consistency) and recommends a multi-metric “syndrome” method. It defends substrate independence as a working hypothesis, drawing on multiple realizability and Marr’s levels of analysis. Finally, it outlines three concrete strategies for incorporating Relational Frame Theory into AI—graph-based, transformer-based, and hybrid neurosymbolic—contending that transformer models augmented with deictic relational tokens are the most immediately viable path toward self-modeling architectures.