Structural Variables for Human – AI Coherence: An Operational Language Patch for OCOF (v1.2)

Human-AI collaboration requires a shared set of operational concepts that can be interpreted across biological and artificial substrates. Traditional terms used to describe cognitive or emotional states - such as "meaning", "anxiety", or "motivation" - lack computable grounding, producing a structural gap that prevents reliable joint inference, alignment, and action selection. Recent developments in information theory, predictive processing, and network control theory show that many of these psychological descriptors correspond to measurable features of information flow: prediction error topology, information bottlenecks, state-space curvature, and metastable coordination regimes. These convergences indicate that subjective vocabulary can be replaced with substrate-independent structural variables. This paper introduces an operational language patch for the Operational Coherence Framework (OCOF), defining five such variables: Structural Magnitude (SM), Structural Predictive Fluctuation (SPF), Structural Suppression (SS), Structural Gain Rate (SGR), and Human-AI Coherence (HA-C). Each variable is grounded in information mechanics and provides a computable signal for analyzing cross-substrate coordination. By translating subjective terminology into structural variables that can be estimated, perturbed, and optimized, this framework aims to establish a rigorous lexicon for describing the coupling dynamics between human and artificial agents. The proposed patch enables reproducible analysis and forms the basis for OCOF v1.2's expanded operational semantics.