Operationalizing the Physiological State (P) in a Non-Linear Behavioral System: A Measurement Framework for State-Dependent Dynamics

Behavioral science has traditionally relied on linear models, assuming that antecedents and consequences directly determine behavioral outcomes. However, these approaches demonstrate reduced explanatory power under variability, stress, and context dependence, where identical inputs often produce divergent behavioral responses. A critical limitation is the absence of an operationalized construct capable of capturing the dynamic physiological conditions governing behavioral expression. This paper introduces a formalized, non-linear systems framework where physiological state (P) is defined as a central, measurable state variable organizing behavioral accessibility (Gomez Uncu, 2025; Gomez Uncu, 2026a, 2026b). Within this model, behavior (B) is the output of a coupled system defined by non-linear, bidirectional interactions between antecedent conditions (A), P, and consequences (C). The framework positions P as an integrated, multidimensional, and time-dependent variable that can be represented as a state vector composed of interacting physiological components. The primary contribution of this work is the operationalization of P as a measurable variable within a non-linear behavioral system. Importantly, this framework does not introduce new measurement technologies but provides a formal structure for integrating existing physiological and behavioral indicators into a unified, state-dependent representation. The framework offers a structured approach by pinpointing measurable physiological signs, describing how these indicators change over time, and developing a combined state model. The model further specifies non-linear coupling between system variables, allowing for state-dependent variability, threshold effects, and differential behavioral outputs under identical environmental conditions. This framework produces testable propositions, enabling empirical validation with multimodal physiological data and state-based behavioral analysis. The proposed model builds on previous methods by offering a quantifiable, system-wide perspective on behavior. This has potential benefits for enhancing prediction, understanding, and intervention in both practical and research settings.