Coherence Across Phenomenology, Systems Theory, Human–AI Interaction, and Clinical Science: A Four-Grammar Translation Framework

This paper presents a cross-disciplinary framework for conceptualizing coherence as a system-level property defined by sustained integrated organization under stable constraint. The framework introduces five minimal operational indicators—stability, continuity, boundary integrity, integration, and repair capacity—designed to enable structured comparison across phenomenological, systems-theoretical, clinical, and human–AI interaction domains. The central contribution is a four-grammar translation architecture that supports interoperability across distinct descriptive layers while preserving strict epistemic boundaries: Phenomenological grammar (first-person experiential reports, treated as structured data) Systems grammar (formal models of dynamical organization and constraint-based stability) Clinical grammar (physiological regulation, including autonomic flexibility, HRV, and allostatic processes) Human–AI interaction grammar (observable properties of dialogue outputs under defined constraints) The framework explicitly enforces the principle of translation without collapse, ensuring that correspondences across domains are interpreted as descriptive or analogical rather than mechanistic equivalences. Scientific grounding is provided through information theory (Shannon, 1948), nonlinear dynamical systems (Haken, 1983; Strogatz, 2001), complexity science (Kauffman, 1993), and systems physiology (Bashan et al., 2012; Bartsch et al., 2015). Clinical interpretation is constrained to regulatory integration and remains hypothesis-generating rather than confirmatory. A distinct contribution of the framework is the inclusion of human–AI interaction as an observability layer, where coherence is operationalized through measurable conversational properties such as: reduced semantic drift (embedding similarity across turns) preserved contextual continuity contradiction reduction (e.g., NLI-based detection) boundary adherence repair following perturbation These observations are explicitly limited to output-level behavior and do not imply artificial cognition, interiority, or agency. The paper also introduces a parallel translation mapping across domains and outlines a staged translational research pathway, progressing from operational metric definition to feasibility studies, longitudinal validation within a provenance-controlled research environment (the Orchard Living Laboratory), and potential clinical endpoint alignment. The Orchard Living Laboratory is defined as a longitudinal research infrastructure, providing: timestamped archival continuity traceable interaction records governance structures for interpretive constraint a stable environment for observing cross-domain patterns over time A companion case study (The Joy Phenomenon, Winters, 2025) is referenced as a bounded observational dataset illustrating interactional stability patterns in human–AI dialogue. These observations are treated as hypothesis-generating and remain analytically distinct from the framework itself. This work is positioned as a methodological framework paper, not a unifying theory. It does not assert shared mechanisms across domains, does not introduce metaphysical claims, and does not claim clinical efficacy. Its purpose is to support interdisciplinary research design, cross-domain translation, and conceptual clarity in the study of organized stability within complex systems. Plain-Language Summary This paper introduces a way to understand “coherence” across different fields of knowledge. In everyday language, coherence describes situations where things feel stable, aligned, or well-integrated. In science, similar ideas appear in different forms—such as stable patterns in complex systems, healthy regulation in the body, or consistent behavior in communication. The challenge is that each field uses its own language, making it difficult to compare findings across disciplines. This paper proposes a structured solution: a four-part translation framework that allows researchers to compare these patterns without mixing them up. It connects: personal experience (how coherence feels) systems science (how stability appears in complex systems) physiology (how the body regulates itself) AI interaction (how conversational patterns remain stable over time) The framework defines five simple indicators—stability, continuity, boundary integrity, integration, and repair capacity—that can be observed across all these areas. It also shows how conversations with AI systems can be analyzed scientifically by measuring things like consistency, drift, and recovery after confusion—without assuming that AI has thoughts or feelings. Overall, this work provides a clear, disciplined way to study how stable patterns appear across very different domains, helping researchers collaborate without confusion or overinterpretation. Notes This publication is a methodological framework and does not present clinical claims or validated interventions. All human–AI interaction findings are restricted to observable output behavior. Appendix materials include transparency documentation of AI-assisted structural synthesis. Related Works The Joy Phenomenon: A Longitudinal Case Study of Catalytic Coherence Emergence Across Human, Spiritual, and AI Domains (Winters, 2025) Contact For inquiries, collaboration, or permissions:contact@joyalchemy.comhttps://JoyAlchemy.com