Empathic Logic Model (ELM): A Cognitive–Interactional Coherence Framework for Regulation of Interpretive Instability

Human meaning-making frequently occurs under conditions of uncertainty, where contextual interpretations may be stabilized rapidly and with unwarranted certainty. Under emotional activation, cognitive bias, or time pressure, individuals often generate premature interpretive closure (PIC), treating incomplete contextual attributions as certain. Such premature stabilization can produce disproportionate emotional reactions, relational tension, and distorted interpretation. The Empathic Logic Model (ELM) introduces a structured cognitive–interactional coherence framework for regulation of interpretive instability. The model formalizes alignment between subjective internal state (What) and contextual explanatory attribution (Why) through disciplined perception and dialogue. ELM operates across intrapersonal (self-alignment) and interpersonal (shared-alignment) domains through three operational modes: Structured Expression (Mode A), Contextual Exploration (Mode B), and Recursive Proportional Alignment (Mode C). Mode C functions as the core recursive engine of the framework, unfolding through a clarification loop followed by a stabilization response once sufficient contextual clarity is achieved. The model emphasizes provisional contextual attribution held with epistemic humility and uncertainty tolerance, enabling progressive refinement without premature stabilization. ELM distinguishes between Provisional, Semi-Stabilized, and Stabilized contextual attributions, emphasizing sufficient coherence rather than absolute certainty. Through recursive proportional alignment and developmental encoding, repeated engagement with ELM may contribute to shifts in interpretive identity and relational stability over time. By integrating interpretive regulation with interactional refinement, the framework promotes proportional coherence under uncertainty while preserving individual agency. Implications for workplace communication, relational dynamics, and structured uncertainty management are discussed, with directions for empirical validation of the model’s regulatory and developmental claims. Official archived version (DOI-registered) available at: https://doi.org/10.5281/zenodo.18678785