Testing Expectancy and Situational Meaning in Multiscale Brain–Body Dynamics: A Prospective Model-Comparison Framework

This theoretical paper develops a prospective model-comparison framework for testing whether experimentally differentiated expectancy and situational meaning are associated with condition-sensitive cross-system predictive structure in multivariate brain–body physiology. The framework distinguishes physiological differentiation from the stronger question of which predictive restrictions are required to represent it. Three competing model classes are compared: condition-sensitive local dynamics, measured shared-process representations, and additional condition-sensitive cross-system structure. All candidate classes permit condition-invariant cross-system dependence, preventing generic physiological coupling from favoring the more permissive model. A proof-of-principle expectancy paradigm combines cue–outcome learning with physically matched perturbations and simultaneous multimodal physiological recording. Prospective information boundaries, history-respecting validation, held-out prediction, and simulation-based feasibility assessment are used to constrain inference. Failure of adequate discrimination is treated as an unresolved outcome rather than forced model selection. All conclusions are explicitly conditional on the prespecified empirical representation boundary. The framework does not assume that brain–body regulation is fundamentally relational; it asks when additional condition-sensitive cross-system structure provides reproducible predictive information about future multivariate physiology beyond strong local and measured shared-process alternatives.