THE DUAL-FUNCTION REGULATORY REVERSAL PRINCIPLE (DFRRP): A Generalized Theoretical Framework for Competing Functional Effects, State-Dependent Action Reversal, and Persistent Goal-Experience Incompatibility (Integrated Cyclic Dynamics Extension: DFRRP-CFRA Version 1.1

This paper proposes the Dual-Function Regulatory Reversal Principle (DFRRP), a domain-general theoretical framework for situations in which one control element produces competing functional effects across multiple outcome dimensions. The framework is developed around the Music-Engagement-Attention Reversal Case (MEARC). In this primary case, a person listening to music while working may experience increased engagement or improved affect alongside attentional interference. Turning the music off may reduce interference while also reducing stimulation or increasing boredom. These changed conditions may subsequently motivate the person to turn the music on again, potentially creating a recurring sequence of regulatory actions.The proposed Cyclic Feedback-Recurrence Architecture (CFRA) extends the framework by distinguishing switching events, completed cycles, state recurrence, periodic orbits, and behavioral termination. It specifies two complementary branches: Finite-Horizon Cycle Dynamics (FHCD), which quantifies completed cycles over a defined observation window, and Infinite-Horizon Regulatory Recurrence (IHRR), which characterizes conditions under which cycle counts may remain unbounded as the horizon tends to infinity.The formal architecture includes a state-transition model, a multi-criteria utility function, a hysteresis-based switching policy, a conditional relay-oscillator model, cycle-period equations, recurrence definitions, closure conditions, and a proposed empirical validation strategy. It also distinguishes behavioral cycling from exact periodicity and separates mathematical possibility from claims about actual human behavior.DFRRP-CFRA is presented as a testable theoretical proposal, not as an established universal law. Its scientific value depends on operational definitions, parameter estimation, comparison with competing explanations, out-of-sample prediction, and independent replication. The framework's proposed contribution is the integration of competing functional effects, state-dependent reversal, recurrent-event measurement, and conditional closure within one explicit architecture.