The Entangled Brain Needs a Control Parameter: Belonging, Energy, and Regime Selection in Predictive Neuroscience

This preprint introduces the BEP framework as a minimal extension of entanglement-based and predictive processing accounts of cognition. While distributed neural network models successfully explain how affective, perceptual, and control processes interact continuously, they do not specify why one constraint comes to dominate inference when constraints conflict — a gap that leaves regime-level phenomena such as sudden obedience under authority, rigidity under social evaluation, and abrupt conformity under exclusion threat unexplained. The BEP framework addresses this gap by formalizing Belonging (B) as a dominance gate operating upstream of precision assignment, and Energy (E) as a stability bound on inference dynamics. Together these control parameters generate discrete inferential regimes — flexible integration, group-locked rigidity, passive conformity, and fragmentation — from the same entangled neural architecture without invoking changes in network structure. A minimal toy simulation demonstrates that the BEP update rule is sufficient to produce all four predicted regime trajectories and the framework's flagship empirical prediction: asymmetric hysteresis during recovery, in which restoring belonging after threat is insufficient to return the system to flexible inference when regulatory energy remains depleted. This path-dependency distinguishes BEP from precision-weighting accounts and is directly testable using repeated-measures designs combining belonging threat manipulation, HRV measurement, and trial-by-trial belief updating tracking. The framework integrates Terror Management Theory, Social Identity Theory, and motivated reasoning accounts within a single predictive neuroscience mechanism, and generates five falsifiable predictions with clear experimental paradigms.