Stabilization Under Finite Cognition: A Structural Consolidation of the Framework

This paper consolidates a series of works on finite cognition and stabilization into a single structured framework. It does not propose a new physical theory or a predictive model, but clarifies the conceptual organization underlying previous formulations. The central claim is that scientific representations are produced and used by finite cognitive systems subject to constraints on memory, processing capacity, and representational precision. In addition to standard criteria such as predictive accuracy and simplicity, model viability is also affected by how representations behave under transformation during use (e.g., recombination, compression, and reinterpretation). This work refers to this constraint as stabilization: the capacity of a representation to remain internally coherent and operationally usable under such transformation processes. Stabilization is not introduced as a measurable physical quantity, but as a structural constraint describing conditions under which representations remain cognitively functional. The framework distinguishes three levels of description: conceptual assumptions about finite cognition, structural relations between representational variables, and domain-dependent operational proxies. These levels are not assumed to reduce to a single formal system, but are intended to prevent conflation between conceptual structure and empirical measurement. The aim is purely integrative: to provide a consistent interpretative map of previously introduced elements and to make explicit the assumptions governing their use.