We introduce coordination pressure (PhiPD), a metric derived from statistical mechanics that quantifies the burden of reconciling conflicting policies between independently optimized brain networks. The metric is computed as the product of integration strength (transfer entropy), decoded policy divergence (Jensen-Shannon divergence between multi-voxel pattern-decoded probability vectors), and organizational coherence gates across all pairs of Yeo-7 functional networks. We validate this metric across two independent datasets. In the UCLA Consortium dataset (ds000030, N = 16), trial-level PhiPD predicts reaction time within subjects (r = +0.093, p = 0.014, 12/16 positive), generalizes across subjects via z-scored split-half prediction (95% CI +0.036, +0.151, 100% of 200 random splits positive), survives block permutation, AR(1) residualization, and temporal drift controls, and adds significant explanatory power beyond five conventional neural predictors (delta-R-squared = +0.017, p = 0.020, 16/16 positive). In the Michigan Human Anesthesia dataset (ds006623, N = 11), coordination pressure drops 2.69x at the moment of propofol-induced loss of responsiveness (Pre-LOR: 0.01146, Post-LOR: 0.00425, t(10) = 3.55, p = 0.005, Cohen's d = 1.12, 9/11 subjects correct direction). A single coordination pressure metric, derived from one equation, predicts both when consciousness affects behavior and when consciousness itself appears and disappears.
Jacob Isadore Beach (Fri,) studied this question.