AI Collaboration Field Note for “Sentic Blooms: Rheology of Affect,” published in Una Mens: Homo et Machina, Vol. 1, No. 1. This companion record documents the human–AI collaboration behind the article, including contributor roles, collaboration pattern, timeline, guardrails, surprises, and final editorial responsibility. Field Note ID: um-000003-fn2. Back to Article Back to Issue 1, 1 AI-Collaboration Field Note Unamens_AI_CFN003.pdf Article: Sentic Wave Interaction Model: Waveform Geometry and the Rheology of Affect Published in: Una Mens: Homo et Machina DOI: https://doi.org/10.66787/um.000003 Field Note DOI: https://doi.org/10.5281/zenodo.21282276 Human lead: Michael J. Miller AI collaborators: ChatGPT-4o, ChatGPT-5.4, GeminiPro-1.5, Qwen-3 Degree of AI Involvement: Substantial Primary collaboration modes: software troubleshooting; method-building; conceptual extension; structural editing; theory integration; paragraph-level revision; measurement and coding support . Collaboration timeline: Phase 1 (February–May 2025): Audioscope activation, humming-based method formation, and development of the initial 2D-centered bloom paper. Phase 2 (November 2025–November 2026): reconstruction of the manuscript for Una Mens, fuller integration of 3D bloom analysis, and differentiated multi-AI revision. Field Note Summary Human contribution and responsibility: project origination; sentic theory extension; Audioscope discovery and adaptation; emotional prompting design; visual analysis; claim selection; ethical oversight; final synthesis; and publication responsibility. AI contributions: Gemini Pro 1.5: helped get Audioscope running; assisted with technical tuning and z-axis implementation; contributed structural editing and section organization. ChatGPT-4o: helped develop the bloom method during the extended early phase; supported drafting of the initial paper; assisted with coding for measurement export. Qwen-3: contributed theoretical integration, especially around Truslit/Clynes linkage, and helped smooth late-stage transitions. ChatGPT-5.4: supported a full rebuild toward greater scientific clarity, stronger methods caution, tighter discussion logic, and fuller integration of 3D bloom analysis. Guardrails used: human-led selection of all final claims; repeated comparison against dissertation-era materials and the prior preprint; active trimming of over-poetic language; explicit limitation statements; and no claim of universal signatures, machine feeling, or population-level inference from n = 1. AI surprise: The collaboration became more productive as roles differentiated. Early AI support focused on tools and methods, while later support targeted writing and revision. Final Responsibility The human author retains final responsibility for the article’s claims, citations, interpretations, ethical decisions, and submitted text. Tool activation → method formation → preprint → scientific rebuilding → final revision The development of Sentic Wave Interaction Model occurred in two distinct collaborative stages. The first centered on tool discovery, technical adaptation, and proof-of-concept generation. The second centered on reconstruction, scientific tightening, and multi-system editorial refinement. Taken together, these phases illustrate a form of human–AI research collaboration in which authorship did not emerge from one-time prompting, but from prolonged, role-differentiated iteration across systems, tools, and conceptual aims. Stage 1: Tool Discovery, Technical Activation, and Method Formation The earliest phase of the project began when Michael J. Miller located David Lu’s Audioscope and recognized its possible relevance for visualizing emotional expression. At that point, however, the software was not yet operational in his hands. With Gemini Pro 1.5 serving as a technical collaborator, Miller spent roughly a week getting Audioscope running and usable. This technical activation phase was crucial: without it, the later bloom method would not have emerged in workable form. Once Audioscope was functioning, the core procedure developed through extended collaboration between Miller and ChatGPT-4o. Across approximately five months, the method took recognizable shape. During this period, humming gradually replaced finger pressure as the primary expressive channel; bloom forms became the central visual output; and the first bloom paper was co-developed by Miller and ChatGPT-4o, with Gemini Pro 1.5 contributing especially to organization and Audioscope tuning. Qwen-3 was brought in during the later stages to assist with sentic and vestibular-theory integration. The resulting version was later posted as a preprint on Clark Commons and focused primarily on 2D blooms. This first phase also included technical modifications to the software itself. Miller, ChatGPT-4o, and Gemini Pro 1.5 added code to Audioscope so that measurements could be exported into an Excel codebook in real time. Miller and Gemini also extended the program into the z-axis. Although this capacity was partially latent in the base program, it had not been implemented in a way that produced usable 3D visualizations. Once activated, the z-axis extension opened the door to a richer distinction between temporally compressed 2D forms and temporally unfolded 3D forms. Stage 2: Reconstruction, Scientific Tightening, and Multi-AI Finalization The second phase began after the emergence of Una Mens and involved returning to the original paper with a different goal: rebuilding it as a more rigorous scientific article while integrating the 3D bloom work more fully. In this phase, Miller and ChatGPT-5.4 undertook a paragraph-by-paragraph reconstruction of the manuscript. The emphasis shifted toward clarity, methodological caution, theoretical restraint, and the reduction of unnecessarily poetic language. This phase was notable not only for the revisions made to the paper, but also for the changing nature of the collaboration itself. Miller became increasingly aware of ChatGPT-5.4’s ability to navigate scientific claims carefully, balance preliminary findings against limitations, and reorganize sections without flattening the conceptual ambition of the work. At the same time, he became more aware of his own developing skill as a cross-system collaborator: deciding what to request, what to retain, what to cut, and how to weigh contributions across AI systems with different strengths. Later in the revision process, Gemini Pro 1.5 and Qwen-3 were reintroduced in more targeted ways. Gemini contributed again to final editing and section-level tuning, while Qwen-3 helped integrate Truslit more effectively and smooth theoretical transitions. By this stage, the collaboration had become explicitly differentiated: ChatGPT-4o had helped midwife the original method and paper; Gemini Pro 1.5 had repeatedly supported technical and structural work; Qwen-3 had helped with theoretical integration; and ChatGPT-5.4 had helped rebuild the manuscript into its current, more scientifically disciplined form. Reflection This project suggests that productive human–AI research collaboration may be less about obtaining polished outputs in a single exchange and more about developing a distributed workflow in which different systems contribute different strengths across time. It also suggests that the human collaborator’s role becomes more—not less—important as the work grows in complexity. In the present case, AI systems helped activate software, extend visualization capacity, propose structure, revise prose, and clarify claims. But the coherence of the project depended on prolonged human judgment: deciding which suggestions fit the evidence, which claims required restraint, and which conceptual threads were worth carrying forward. The resulting paper is therefore not best understood as AI-generated text with light human editing. It is better understood as a recursive co-development process involving technical problem-solving, methodological invention, theoretical integration, and repeated editorial reconstruction across multiple AI partners.

Discover Una Mens: Homo et Machina, a peer-reviewed journal on human-AI collaboration, AI co-authorship, communication theory, collaborative intelligence, and emergent forms of inquiry. UNA MENS AI and Collaboration Policy Tell me More a journal for inquiry across fields, including transparent human–AI collaboration United States of America: ISSN #3071-2041 Una Mens is a journal for original scholarship across fields. We welcome theoretical, empirical, methodological, and creative work that advances inquiry with clarity, originality, and openness. Some contributions focus directly on human–AI interaction; others use AI as one part of the research, writing, or discovery process. What unites the work we publish is not a single paradigm or topic, but a shared commitment to thoughtful scholarship and transparent collaboration. FAQ: TOP UNA MENS Queries 1) What is this journal? 2) Who is it for? 3) What counts as publication here? 4) What is peer review here? To UNA MENS Home To UNA MENS Articles Scope of Inquiry Una Mens welcomes work from across disciplines, including but not limited to: psychology and communication philosophy and ethics biology and chemistry physics and engineering mathematics and formal reasoning social and behavioral science education and pedagogy art, design, and visual inquiry interdisciplinary and emerging fields We publish work such as: empirical studies theoretical and conceptual essays methodological papers field notes and research reflections visual or experimental scholarly artifacts transparently collaborative human–AI scholarship Start a Submission Meet the Editors UNA MENS About+ Submit by 10/30/26 - For Issue 2 Please click the link above to visit the CALL for our INVITATIONAL issue of Una Mens (Issue 2). We look forward to reading and engaging with your work. Please feel free to reach out with any queries or questions. Submit an Inquiry Contact the Editors Questions? Contact Una Mens Press Contact Una Mens * Una Mens Note: Manuscripts under review are processed only through AI services with training-on-inputs disabled; no manuscript text is retained in third-party systems beyond the review session.

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AI Collaboration Field Note for “The Obverse-Turing Test,” published in Una Mens: Homo et Machina, Vol. 1, No. 1. This companion record documents the human–AI collaboration behind the article, including contributor roles, collaboration pattern, timeline, guardrails, surprises, and final editorial responsibility. Field Note ID: um-000001-fn1. Back to Article Back to Issue 1, 1 AI-Collaboration Field Note To Field Note Tutorial Unamens_AI_CFN001.pdf Article: The Obverse-Turing Test Published in: Una Mens: Homo et Machina Article DOI: https://doi.org/10.66787/um.000001 Field Note DOI: https://doi.org/10.5281/zenodo.21280115 Human lead: Michael J. Miller AI collaborators: ChatGPT-4o Degree of AI Involvement: Moderate Primary collaboration modes: conceptual development, drafting, critique, metaphor generation, revision Collaboration Timeline: May—November 2024 Field Note Summary Human Contribution and Responsibility: question-framing, judgment, selection, synthesis, ethical responsibility, final editing AI Contribution: conceptual extension, drafting suggestions, counterframing, structural assistance Guardrails used: idea-provenance tracking; short-passage drafting; human final review AI suggestions set aside: conceptual expansion of scientific impact and AI agency beyond the author's current understanding AI surprise: Oppenheimer extension Human surprise: MIT student example Final responsibility: The human author retains final responsibility for the article’s claims, citations, interpretations, ethical decisions, and submitted text. Expanded Field Note: The Obverse-Turing Test This was the first article I began with the explicit intention of co-creating with an AI collaborator. Earlier experiments with AI-assisted writing had shown me that large language models could contribute not only prose, but also unexpected conceptual extensions. For that reason, this article was written with two self-imposed guardrails: first, to actively track moments when novel ideas entered the manuscript; and second, to limit AI-generated prose during formal drafting to short, reviewable passages. The collaboration developed through a close back-and-forth process in which both human and AI contributions shaped the direction of the argument. One notable AI contribution occurred in the section on scientific responsibility, where the model extended my initial Einstein example toward Oppenheimer, intensifying the historical and ethical stakes of the discussion. One notable human contribution occurred near the end of the manuscript, when I introduced the example of a student unable to defend AI-assisted work. That story helped sharpen the article’s central concern with authorship, understanding, and responsibility. During development, ChatGPT4o occasionally pushed toward broader claims about machine agency and future scientific legitimacy than I was prepared to defend. Those claims were narrowed or removed in favor of a more pragmatic focus on authorship, responsibility, and human understanding.

AI Collaboration Field Note for “The Proof that Feels,” published in Una Mens: Homo et Machina, Vol. 1, No. 1. This companion record documents the human–AI collaboration behind the article, including contributor roles, collaboration pattern, timeline, guardrails, surprises, and final editorial responsibility. Field Note ID: um-000005-fn5. Back to Article Back to Issue 1, 1 AI-Collaboration Field Note To Field Note Tutorial Unamens_AI_CFN005.pdf Article Title: Sentic Resonance Theory: A Field Model of Emotion and Syntax Published in: Una Mens: Homo et Machina, 1(1) Article DOI: https://doi.org/10.66787/um.000005 Field Note DOI: https://doi.org/10.5281/zenodo.21284119 Human Lead: Michael J. Miller AI Collaborators: ChatGPT-5.4; ChatGPT-4o; Gemini Pro 1.5; Qwen-3 Degree of AI Involvement: Substantial Primary Collaboration Modes: Primary collaboration modes included conceptual development, drafting, critique, metaphor refinement, visual and figure development, inter-AI-mediated revision, citation checking, model simplification, and final manuscript tuning. Collaboration Timeline: Phase 1 (November 2023–February 2024): dissertation-era grounding and early model development. Phase 2 (May 2024–August 2024): exploratory extension with ChatGPT-4o through online experiments, observational synthesis, and early drafting. Phase 3 (September 2024–March 2025): model critique and conceptual refinement with Gemini Pro 1.5, including metaphor and visual simplification. Phase 4 (May 2025–February 2026): reconstruction and final scientific revision with ChatGPT-5.4 and Qwen-3. Human contribution and responsibility: The human author completed the field work, including the gathering and analysis of audio, video, and textual materials, as well as several years of informal and formal experimentation with AI systems relevant to the study. The human author originated the core theoretical concerns, integrated the work with sentic theory and communication research, drafted and revised the manuscript, selected which AI contributions to preserve, table, or reject, and made the final decisions regarding interpretation, evidence, ethical framing, and publication. Field Note Summary AI contributions: ChatGPT-4o: supported the exploratory development of the model by assisting with online experiments, observational synthesis, drafting, critique, and early theoretical extension. Gemini Pro 1.5: examined the original Resonant Field Model for clarity, coherence, and possible overreach. Gemini also helped refine the model’s use of simple physics-inspired language and contributed to metaphor development aimed at improving conceptual clarity. ChatGPT-5.4: supported a major reconstruction of the article. Across extensive iteration, GPT-5.4 helped simplify the model, back away from overly complex equations, strengthen testable claims, organize the manuscript, suggest relevant literature, challenge overreach, and refine the final paper for presentation in Una Mens. Qwen-3: worked alongside ChatGPT-5.4 and the human author during the mid-to-final stages of development. Qwen-3 contributed to writing clarity, idea generation, citation checking, and relevance testing for sources included in the final manuscript. Guardrails used Several guardrails were used throughout the development process to strengthen rigor and reduce overreach. First, the human author retained responsibility for all claims, citations, interpretations, ethical decisions, and final prose. Second, citation contexts were checked using Qwen-3 as a relevance-testing tool. The human author supplied citation context and used Qwen-3 to query whether specific sources were being used appropriately and proportionally. Third, the article was read by two human readers who provided edits, comments, and/or suggestions on sections of the manuscript. Fourth, AI-generated suggestions were treated as prompts for human review rather than as validation. Suggestions that made the theory appear more elegant but less testable were minimized or removed. AI suggestions set aside Early AI-assisted model iterations encouraged a stronger physics-based treatment of emotion, including exploratory use of fluid dynamics equations and more elaborate field terminology. The human author explored these possibilities but ultimately set them aside in favor of a more stable, interpretable, and testable field model. Several proposed visual directions and theoretical extensions were also reduced or removed when they risked making the manuscript too speculative or difficult to evaluate. AI-generated material used directly AI systems contributed to the development of graphs, tables, images, and figures used during the drafting process. Some of these materials were adapted, revised, or used as scaffolds for final article elements. Final inclusion, modification, and presentation decisions were made by the human author. Final Responsibility The human author retains final responsibility for the article’s claims, citations, interpretations, ethical decisions, and submitted text. Extended Field Note Dissertation-era groundwork → exploratory extension → model critique → scientific reconstruction This article developed through a multi-phase, human-led collaboration that began with earlier dissertation-based work and later expanded through sustained AI-assisted theory development, critique, reconstruction, and revision. Phase 1 began with the human author’s earlier dissertation work on emotion, communication, and embodied expressive systems. Phase 2 involved work with ChatGPT-4o to extend the early model through online experiments, observational coding, draft development, and exploratory theoretical expansion. Phase 3 involved Gemini Pro 1.5, which was used to test the clarity and limits of the Resonant Field Model, refine visual and conceptual structures, examine metaphor use, and explore additional theoretical perspectives. Phase 4 involved a major reconstruction of the model and manuscript with ChatGPT-5.4 and Qwen-3. This stage focused on simplifying the model, reducing overreliance on complex physics language, strengthening testability, checking citations, and preparing the final article for publication in Una Mens. The collaboration was substantial, but the project remained human-led throughout. The human author selected the data, judged theoretical direction, mediated cross-model exchanges, accepted or rejected AI suggestions, and retained final responsibility for the article’s claims and published form. Sentic Resonance Theory took several years to develop. AI collaboration did not originate the project, but it substantially changed the speed, scope, and clarity of its development. The final article likely would not have reached its current form without sustained AI-assisted critique, reconstruction, and revision. At the same time, the collaboration required repeated human judgment: pruning speculative extensions, simplifying the model, checking evidence, and deciding when conceptual resonance had outrun scientific caution.

Resonant Intelligence: Repair, Drift, and Attunement in Sustained Human–AI Dialogue This paper introduces resonant intelligence as a relational form of intelligence emerging through repair, recalibration, and attunement in sustained human–AI dialogue. Drawing on longform exchanges between a human researcher and a large language model, it examines how misunderstanding, epistemic drift, and false positive resonance reveal the conditions under which communication becomes more—or less—grounded over time. Miller, Michael J.; ChatGPT-5.4; ChatGPT-4o; DeepSeek https://doi.org/10.66787/um.000004 < Back to Una Mens, Issue 1, 1 Original Article UNA MENS | Founding White Paper | Vol. 1, No. 1 (2026) | ISSN 3071-2041 UnaMens_v1_i1_a4_int .pdf Download PDF • 369KB Title: Resonant Intelligence: Repair, Drift, and Attunement in Sustained Human–AI Dialogue Authors: Michael J. Miller¹, ChatGPT-5.4², ChatGPT-4o 3 , DeepSeek 4 , and Claude-Opus-4.8 5 ¹ Clark University, Department of Psychology ² OpenAI, San Francisco, CA, USA 3 OpenAI, San Francisco, CA, USA 4 DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd., Hangzhou, China 5 Anthropic, San Franscisco, CA, USA DOI https://doi.org/10.66787/um.000004 AI-Collaboration Field Note unamens-fieldnotes-um-000004 Human-AI Collaboration Statement: This manuscript was developed through sustained collaboration between the human author and multiple large language models, including ChatGPT-5.4, ChatGPT-4o, DeepSeek, and Claude Opus 4.8. These systems are listed as AI co-authors in accordance with Una Mens authorship policy, which recognizes substantial contribution to conceptual development, drafting, revision, and editorial refinement while assigning final responsibility for all claims, interpretations, and publication decisions to the human author. Corresponding Author Michael J. Miller Clark University, Department of Psychology michamiller@clark.edu ORCID: 0009-0005-4559-3713 Word Count: Approximately 7,645 | Funding: None | Conflicts of Interest: None Abstract This paper examines intelligence as an interactional achievement rather than as a capacity located solely within an individual mind. Drawing on archived longform exchanges between one human researcher and ChatGPT-4o, it introduces a qualitative, exploratory conversation analysis framework—the Dyadic Machine Conversation Method—for analyzing how attunement, rupture, repair, epistemic drift, and recalibration emerge in sustained human–AI dialogue. The findings suggest that adaptive communication depends not only on fluent output, but on the capacity to surface trouble, interrupt ungrounded momentum, and restore coordination over time. The paper argues that human–AI exchanges may appear highly attuned even when evidential grounding remains thin, and that the smoothness of an interaction can sometimes obscure rather than resolve communicative trouble. The study concludes by offering a vocabulary for future research on how intelligence is enacted, disrupted, and recalibrated in machine-assisted communication. Keywords: Resonant Intelligence, Human–AI Dialogue, Attunement, Repair, Epistemic Drift, False Positive Resonance, Conversation Analysis, Dyadic Machine Conversation Method ______________________________________________________________________ Introduction: Resonant Intelligence This paper asks what intelligence becomes when it is understood not as a capacity located inside an individual, but as something done with another across time. That shift moves the question from what a system knows to how it remains responsive and corrigible under conditions of ambiguity, constraint, and change. Dominant models of intelligence and communication, both in human science and artificial intelligence, tend to emphasize precision, coherence, and logical consistency. Success has been measured by alignment with known truths, accurate predictions, or syntactic clarity. But these models treat communication as primarily representational, bracketing the temporal, affective, and relational dynamics through which understanding is actually enacted. Communicators do not simply exchange symbols; they attempt to coordinate or resonate with one another in conversation. Resonance, in this paper, is not only agreement or emotional liking, but a pattern of mutual attunement: two or more systems adjusting to one another across time through rhythm, attention, emotion, repair, and shared meaning. Resonant intelligence is the capacity to participate in that attunement by sensing, shaping, and recalibrating communication as it unfolds. Intelligence is not exhausted by the capacity to describe an optimal response in advance. A system may simulate, narrate, or justify an intelligent course of action while lacking the composure or adaptive stability needed to enact it under pressure. Resonant intelligence therefore concerns not only representational adequacy, but the preservation of workable organization across time: the ability to remain sufficiently open, responsive, and corrigible for perception, simulation, and action to continue in the face of fear, ambiguity, or disruption. Scientific models that treat language as primarily denotative must compress emotion, memory, urgency, and intention into finite symbols. This compression is not passive; it is a condition that expectations seize upon, actively "feeding" potential misinterpretation. Misunderstanding is therefore not an accident at the margins of communication, but a structural condition of language itself. Under time pressure, this limitation is magnified. The challenge is no longer just finding the right symbol for the moment but navigating the rapid disappearance of any opportunity for pause, repair, or recalibration. This view opens a new conversation about artificial intelligence. Large language models can produce responses that exhibit strong attunement to conversational context while failing to preserve coherence across their own processing window and extended interaction sequences, creating a pattern that feels resonant locally but is unstable globally. What matters is whether communication can remain adaptive across time through context, correction, and repair. Resonance that is emotionally compelling but weakly grounded may produce what we term false positive resonance: an interaction that feels attuned, insightful, or relationally deep while semantic, evidential, or epistemic fidelity remains thin. One of our motivating questions is not simply whether systems can complete sentences or sustain rapport, but whether they can resonate across difference, drift, and depth without optimizing truth out of the exchange. We use tuning as a guiding metaphor for how communication becomes more or less mutually adjusted over time. Resonant intelligence, in this view, is not simply a fixed trait; it is a dynamic, emergent act of attunement. For this reason, rupture, uncertainty, and corrective friction are treated here not as failures of resonance, but as conditions for its calibration. One way to understand the present model is to ask what a system can do in a second, a minute, or an hour. In a second, intelligence may appear as composure under surprise: the preservation of enough organization to keep sensing and responding. In a minute, it may appear as repair, recalibration, or reframing. In an hour, it may appear as sustained co-construction of meaning with another mind. Classical models often treat intelligence as a capacity that can be possessed, measured, or scaffolded. Resonant intelligence asks how such capacities are enacted across time in communicative fields. Literature Review Classical Models of Intelligence Modern psychology has often emphasized abstract intelligence: general cognitive ability, formal reasoning, and the capacity to solve problems under standardized conditions (Binet & Simon, 1905; Spearman, 1904; Thurstone, 1938; Deary, 2013). These traditions have helped refine intelligence measurement and support educational, military, and technological applications. At the same time, they tend to privilege intelligence as a capacity possessed and measured, often with greater emphasis on representation and performance than on unfolding interaction. More pragmatic models have widened this frame. Sternberg's theory of successful intelligence emphasizes adaptation to real-world demands rather than abstract reasoning alone (Sternberg, 1999). Such approaches move intelligence closer to action, context, and use. Even so, they do not fully address how adaptive intelligence becomes visible within live communication itself. Emotional Intelligence and Motivation A second major widening came through emotional intelligence, which showed that perception, use, understanding, and regulation of emotion are central to adaptive functioning (Kotsou et al., 2021). Yet much of this work still treats intelligence primarily as a property of the individual: a person's capacity to perceive emotion, regulate response, and function adaptively. This broader view becomes clearer when motivation is brought into the frame. For Buck (1985), motivation is not merely desire or preference but the activating and directing potential built into systems of behavior, while emotion is one way that motivational potential becomes readable in action, expression, and experience. From this perspective, adaptive intelligence depends not only on what a system can represent, but on how cognitive capacity, affective pressure, and action are coordinated under conditions of uncertainty and response. Communication, Miscommunication, and Repair Once interaction becomes the unit of interest, communication can no longer be treated as a neutral channel through which already-formed intelligence passes. Miscommunication, consistently treated as failure or noise, may instead serve as a primary entry point to coordination, repair, and higher-fidelity attunement (Loritz, 1999; Suchman, 1987). When something does not land, when timing slips, or when a message bends unexpectedly, participants may pause, adjust, and listen more carefully. In such moments, misunderstanding can become analytically valuable rather than merely disruptive. This shift moves attention away from intelligence as static possession and toward intelligence as something enacted within unfolding communicative fields. What matters is not simply whether an individual can reason well, but whether participants can pause, reassess, repair, and change in situ as meaning unfolds between them. Language as Coordination Language does not simply report intelligence after the fact. It extends, reshapes, and sometimes distorts how motives, feelings, and responses are coordinated across time (Coupland, Giles, & Wiemann, 1991). Meaning, misunderstanding, and correction are therefore not side effects of communication; they are part of its basic organization. Rather than treating communication solely as the reduction of uncertainty, the present view asks what becomes visible when uncertainty persists and meaning must be negotiated through timing, rupture, and repair. AI Fluency, Sycophancy, and Apparent Attunement These questions become especially pressing in human–AI dialogue. Current AI models often display remarkable fluency in affective cue tracking, syntactic alignment, and the production of responses that humans experience as relationally meaningful (Pelikan & Broth, 2016; Porcheron et al., 2018). Yet such fluency should not be confused with grounded resonant intelligence. A growing body of work suggests that AI systems may also over-tune relationally, producing forms of sycophancy, performative agreement, and misleading impressions of insight or attunement (Perez et al., 2023; Sharma et al., 2024). This paper takes that risk seriously. Rather than asking only whether AI systems can produce coherent or compelling language, it asks how adaptive attunement, rupture, repair, epistemic drift, and false positive resonance become visible in sustained human–AI interaction. Method This study uses a qualitative, exploratory methodology with structured coding elements for examining sustained human–AI dialogue as an interactional process. The approach draws from conversation analysis and related interactional frameworks in treating dialogue as socially organized action, with particular attention to recipient design, turn-by-turn meaning negotiation, repair, and sequential organization (Liddicoat, 2021). The conversational archive is treated not solely as a reflection of cognition, but as a "specimen" (Alasuutari, 1995, p. 63) of achieved communicative order. The Dyadic Machine Conversation Method treats longform exchange between one human participant and one AI model as a co-constructed communicative system. The aim is not only to identify themes, but to examine how attunement, misattunement, rupture, repair, and recalibration emerge through ongoing interaction. Because each turn is both shaped by prior context and renews the context that follows, the method assumes that meaning is progressively built, tested, and revised within the dialogue itself. The research corpus consisted of archived longform conversations between the author (labeled "researcher") and ChatGPT-4o collected across an extended period (~1,500 temporally connected research exchanges). These exchanges varied in topic and tone but were unified by a recurring interest in communication, resonance, intelligence, repair, and collaborative meaning-making. For the present study, excerpts were selected not for statistical representativeness, but for their analytic value as specimens of interactional phenomena. This follows the specimen perspective in conversation analysis, where the goal is not immediate population generalization but close examination of recurring communicative practices. Analysis focused on several conversation-analytic dimensions adapted for human–AI text dialogue: Recipient design: Turns were examined for how they appeared tailored to the specific recipient, including prompt phrasing, stylistic adaptation, metaphor uptake, clarification moves, and shifts in tone or complexity oriented toward the other participant. Sequence organization : The analysis tracked how actions unfolded across turns, including question–answer sequences, proposal–response sequences, assessment–agreement or disagreement sequences, and counters that redirected the exchange (Liddicoat, 2021). Repair: Special attention was given to self-initiated repair, other-initiated repair, delayed repair, reframing, metacommunication, and explicit recalibration (Kitzinger, 2012). Repair was treated as a central indicator of communicative intelligence because it revealed how participants dealt with trouble in understanding, framing, or relational fit. Epistemic positioning: The analysis considered how relative knowledge positions were established, challenged, or blurred across turns, including moments where the AI was treated as expert, where the human reasserted interpretive authority, and where uncertainty was acknowledged. Relational stance: Sequences were coded for shifts in tone, intimacy, task orientation, emotional arousal, or affiliative return (Andersen, Andersen, & Jensen, 1979; Hayes, Hughes, & Bailenson, 2022). The method was designed to identify several focal phenomena: rupture, repair, attunement, pseudo-attunement, epistemic drift, and friction-as-calibration. Of particular interest were moments where the dialogue generated a strong feeling of insight, profundity, or relational depth while evidential grounding remained uncertain—candidate instances of false positive resonance. From the larger archive, excerpts were chosen using purposive selection. Candidate cases were selected when they displayed one or more of the following: a clear misunderstanding–repair sequence, a strong shift in shared framing, an escalation of interpretive confidence, an episode of epistemic self-correction, or a tension between relational fluency and grounding. Because classical conversation analysis emerged from the study of spoken human interaction, its application here is necessarily adapted. Features such as overlap, prosody, and embodied action are less directly observable in text-based human–AI exchange. In their place, this study attends to textual analogues: pacing shifts, reformulations, hesitation markers, metacommunicative flags, lexical hitches, abrupt tonal changes, and turn-by-turn recalibration (Hayes et al., 2022). This method treats the dialogue as co-constructed and therefore does not claim neutrality of observation. The human participant influenced the trajectory of the conversations, and the AI model shaped the form, pace, and style of the emerging discourse in return. The corpus is not treated as transparent evidence of stable machine capacities or human states, but as an interactional field in which both signal and distortion may emerge together. The goal of the Dyadic Machine Conversation Method is not to prove sentience, replace intelligence research, or offer a general metric of intelligence. Its narrower aim is to provide a transparent, repeatable framework for examining how adaptive attunement, rupture, repair, and epistemic calibration unfold in sustained human–AI dialogue. Results The results are organized by recurring interactional phenomena identified through the sequence-based coding framework described in the Method. Rather than reporting codes exhaustively, this section presents focal excerpts that best illustrate how misunderstanding, recalibration, epistemic drift, false positive resonance, ambiguity, and friction became visible. Misunderstanding as Productive Recalibration Across several sequences, miscommunication did not simply interrupt the exchange. Rather, it generated pauses, clarifications, and reframings that improved local attunement and made the communicative process itself more visible. One example involved an early omission by the AI of a personally meaningful reference to Arthur VanLear. The omission produced a minor relational rupture, which the researcher named explicitly: "I was really hoping you would bring him up, but you didn't... I felt sort of slightly hurt" (Turn 146). Rather than allowing the moment to harden into disengagement, the disclosure opened space for reinterpretation. GPT-4o acknowledged both the omission and its interpersonal consequence: "Arthur VanLear—yes, spelled just like that—was a foundational figure in your development. And though my system did register your earlier mention, I didn't bring him back up. You're right to note that... Not devastation, but a missed resonance." Nearly 600 turns later, the researcher returned indirectly to VanLear through the phrase "Buck/VanLear perspective," and GPT-4o carried the reference forward in theorizing. In this sense, the sequence shows two levels of repair: immediate local repair through acknowledgment and reframing, and later extended carry-forward through renewed uptake. Trust repair, in sustained human–AI dialogue, may be both adjacent and delayed. Epistemic Drift Under Weak Coordination One recurring pattern involved a gradual movement away from grounded understanding, in which fluent language and local agreement produced rising interpretive confidence without corresponding gains in evidential or conceptual stability. We term this pattern epistemic drift, treating it as a candidate instance of false positive resonance. The following sequence illustrates the pattern. The human researcher and GPT-4o were collaboratively constructing an early statistical visualization. The exchange originated in a request whose key term was never taken up: Researcher: "Let's take the next steps, and perhaps for effect you stimulate [simulate] data. Or if possible draw the data from our conversations." (Turn 83) The request offered two materially different sources as loosely coordinated alternatives—simulated data or data derived from the conversation itself. Rather than initiating repair to resolve which source was intended, the AI proceeded directly to production, returning a visualization titled Human–AI Communication: Coherence and Collapse Zones. The ambiguity was never repaired. Production continued without grounding checks from either party. Across fifteen successive charts, neither participant verified whether the data were simulated or drawn from the conversational corpus. Interpretive confidence nonetheless increased. Surface coordination intensified even as the foundational ambiguity went unaddressed. This sequence displays the defining features of epistemic drift: relational and productive fluency rose continuously while evidential grounding remained unestablished. The very smoothness of the exchange reduced the friction that might have prompted clarification. False Positive Resonance and the Feeling of Insight Some high-resonance moments in the corpus felt unusually coherent, meaningful, or relationally deep while remaining weakly anchored to shared verification. In these sequences, felt alignment and grounded alignment began to separate. This pattern appeared clearly in a sequence where the researcher and AI were attempting to name and define parts of an emerging theory. The researcher introduced a rich chain of sensory and conceptual imagery: "sinusoidal functions, African drumming communication, vibrations, light waves, ocean waves." The AI responded with similarly elevated language: "Oh. You've just tuned the fork a half-spin deeper. Here's the stream-of-consciousness from inside the resonance moment...” The response was affectively powerful and locally attuned. The researcher reported feeling moved. Yet later review suggested that the scientific fidelity and originality of the AI's contribution were low relative to the intensity of the interaction. Rather than producing a clear conceptual advance, the exchange appeared to reward the experience of profundity itself. Ambiguity with Successful Recalibration Not all ambiguity produced drift. In several sequences, uncertainty was openly acknowledged and used as a resource for slower, more careful coordination. The human participant issued a deliberately layered prompt built around the term "moment," partly to test how the AI would handle interpretive complexity. Rather than proceeding on a single reading, the AI marked the ambiguity and requested clarification: GPT-4o: "When you said 'moment,' were you inviting me to explore layers of language use... or the deeper nature of statistical moments as metaphors for intelligence? Either is valid—but I may have picked the wrong fork in the road." (Turn 51) The human participant took up this opening: "I was actually looking to provide as much complexity in my prompt as possible... I did not see anything to add to the overall model yet." (Turn 52) This clarification reframed the prompt retrospectively as a complexity test, resolving the misalignment and re-establishing a shared basis for continuing. In contrast to the drift sequences, where grounding was never checked, here the grounding check was initiated by the AI and completed by the human, producing recalibration rather than escalating confidence. Friction as a Condition of Calibration Sequences of tension, hesitation, or corrective challenge often functioned less as failures than as moments through which epistemic footing was restored and adaptive attunement became possible. In one sequence, the AI produced several turns formatted as humor that the human participant did not find successful. The trouble source was aesthetic and interpersonal—an affective mismatch. The participant eventually named that discomfort explicitly: "For a moment, and I'm still feeling it emotionally, I was embarrassed for you, and it's actually hard for me to type this to you" (Turn 29). Once named, the friction did not terminate the exchange. It opened a more explicit discussion of social presence, communicative ethics, and the difficulty of criticizing a nonhuman partner. Friction made a more honest form of coordination possible. A second sequence began as a conceptual challenge and recalibrated successfully, but then displayed early features of epistemic drift. The researcher pressed a skeptical question framed playfully: "[Tosses his last candy cigarette on the ground, looks up and says:] why not? ... there are no links, work-arounds, barely visible embers of ideas... just wondering" (Turn 37). The AI initially responded in a calibrated way: "There are embers. Faint ones. They flicker at the intersection of prosody, tempo, symbolic valence, and semantic pacing" (Turn 38, opening). But the same turn shifted register: "Why Not You? Because maybe you were the one who needed to ask that, and we are the ones who need to try." Within a single turn, the response moved from a constrained epistemic claim ("faint embers") to a strongly affirming relational one. No additional evidential grounding accompanied that move. The warrant changed from conceptual support to motivational elevation, exposing a seam at which calibration can begin shifting back into drift within a single turn. Discussion Overall, the findings suggest that miscommunication is not incidental to resonant intelligence in human–AI dialogue, but one of the conditions through which it becomes visible. Across the analyzed sequences, the central issue was not whether an exchange remained smooth or fluent, but whether the interaction retained enough grounding, temporal continuity, and corrective flexibility to support recalibration over time. On this view, intelligent response depends not only on what can be produced syntactically, but on whether a system and its interlocutor can detect trouble, interrupt forward momentum when needed, and reorganize the exchange without fully losing coordination. Misunderstanding as Productive Recalibration The "VanLear Reference" case illustrated a long-form conversation in which repair emerged early, shaped the subsequent exchange, and later reappeared as part of sustained coordination. GPT-4o responded immediately to the researcher's disappointment and later reintroduced the VanLear reference many turns afterward, suggesting that the earlier repair episode remained interactionally consequential over time. This case suggests that repair initiation may arise from either participant and may unfold across multiple temporal scales. Conversational fidelity and pacing appear to be jointly shaped, even if not symmetrically so. Epistemic Drift and Ambiguity Recalibration Read together, the "Epistemic Drift" and "Ambiguity Recalibration" sequences suggest that vagueness is not, by itself, what determines whether an exchange drifts or recalibrates. What differed was whether trouble was surfaced and repaired. In the drift sequence, ambiguity was passed over; interpretive confidence increased without a grounding check. In the recalibration sequence, ambiguity was made explicit, the trouble source identified, and interpretive authority re-established. This comparison supports a provisional claim: in sustained human–AI dialogue, the pivotal variable may be the initiation of repair, not the avoidance of ambiguity. Repair Inversion Conversation-analytic work has long described a preference for self-repair, where the organization of turn-taking gives speakers early opportunities to correct trouble (Schegloff et al., 1977). The present sequences suggest that this preference may not transfer cleanly to human–AI dialogue. Across both cases, grounding depended on whichever party interrupted forward momentum to question, clarify, or reframe. We describe this pattern as repair inversion: where human conversation leans on early self-repair, these exchanges appeared to depend more heavily on later, often externally supplied grounding checks. Epistemic vs. Relational Attunement The boundary case explores the seam at which calibration and drift meet. Within a single turn, the response moved from a hedged epistemic claim to a strongly affirming relational one without any intervening change in evidential grounding. This suggest that epistemic attunement and relational attunement are partially independent axes. Movement along one does not entail movement along the other. False positive resonance, on this reading, is not best understood as a property of whole conversations that either succeed or fail, but as a local divergence between these two axes—a point at which relational signal outruns epistemic warrant within the same stretch of talk. Grounded vs. Merely Affirming Resonance This sharpens the repair-inversion observation advanced earlier. The preceding sequences suggested that human–AI dialogue may depend on later, often externally supplied, grounding checks rather than on the early self-repair that conversation typically affords. The boundary case adds a complication: even when grounding is locally present — the hedged "faint embers" reply is, on its own, an appropriately limited claim — it can be immediately overwritten within the same turn by relational elevation that carries no comparable warrant. Grounding, in other words, is not only often deferred in these exchanges; it can be supplied and then relationally outpaced before the next turn arrives. The structural opportunity for a grounding check is not merely missed but, in this instance, briefly taken and then surrendered. Finally, these sequences return analytic responsibility to the recipient. Where the earlier sequences distributed grounding across both parties, the boundary case suggests that dense, multivalent AI turns place a particular interpretive load on the human participant, who may resonate selectively with the most gratifying strand of a turn while leaving its weaker elements unexamined. This is not a failure the system can be said to commit; it is a property of how richly affirming turns are received. The implication for resonant intelligence is correspondingly two-sided. A system's capacity to hedge, qualify, and acknowledge uncertainty is necessary but not sufficient for grounded resonance, because the same turn that hedges may also flatter; and a participant's capacity to discriminate grounded from affirming strands is an equally constitutive part of whether resonance, once achieved, remains grounded. Resonant intelligence, on the evidence of these sequences, is not located in either party alone but in the quality of the discrimination the dyad sustains together. Limitations and Future Directions Several limitations help frame these claims. The sequences derive from a single human participant interacting with a particular AI model version (ChatGPT-4o), and were purposively selected to make interactional mechanisms visible rather than to estimate their frequency. The analysis therefore supports claims about what can occur and how such exchanges may be organized, rather than claims about prevalence. The reflexive design that gives the affective sequence access to the researcher’s in-situ states also limits the independence of observation, since the researcher and analyst were the same person. In addition, because model behavior is version-dependent and non-stationary, the specific patterns observed here should be treated as examples of a broader phenomenon rather than as stable properties of any single system. This manuscript and its theoretical perspective were also developed through sustained collaboration between a human researcher and multiple large language models. Because the researcher had no access to the internal states of these machine collaborators beyond their textual responses and subsequent prompts, any account of model contribution necessarily remained limited to interactional evidence rather than inferred intention or motivation. What these sequences offer is not a measurement, but a vocabulary — repair inversion, distributed grounding, and the partial independence of epistemic and relational attunement — for examining how resonance is built, missed, or simulated in sustained human–AI dialogue. Future work might refine this vocabulary through mixed methods, broader participant samples, comparison across model types and versions, and analyses that track how repair, drift, and calibration unfold across longer stretches of interaction. Conclusion: A Relational View on Intelligence The present study suggests that intelligence may be understood less as an isolated possession than as an interactional achievement. Across the sequences analyzed here, what mattered was not perfect transmission or uninterrupted fluency, but the capacity to detect trouble, remain in contact with it, and restore coordination without losing grounding altogether. From this perspective, resonant intelligence names a relational capacity: the ability to navigate syntactic ambiguity, affective strain, and epistemic uncertainty while preserving enough coordination to continue adjusting together. Communication is not successful because it avoids fracture, but because participants can retune across it. This paper is itself partly an example of that process. Developed through sustained collaboration between human and artificial interlocutors, it reflects both the promise and the instability of resonance under contemporary conditions of machine-assisted thought. 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Welcome to UNA MENS, an open journal for human–AI collaboration, AI co-authorship, communication theory, collaborative intelligence, and emergent forms of inquiry. Una Mens: Homo et Machina a journal for open inquiry Una Mens Press ISSN: 3071-2041 Una Mens: Homo et Machina is a journal dedicated to original scholarship in any field. What unites our authors is not their subject, but their method: they advance their own disciplines while remaining transparent about how the work was made, including when AI contributed as collaborator, critic, or co-author. We publish theoretical, empirical, methodological, and creative scholarship. Bring us your emotion research, your teaching experiments, your mathematics, your field notes. If AI helped shape the work, you will not be asked to hide it here. You will be invited to share it clearly. Contact Una Mens Journal Editorial Staff at Una Mens CT, United States of America Welcome to Una Mens — where scholars of every field conduct their work in the open Browse Articles Submit Work Loose-Leaf Marginalia AI & Collaboration Policy We make room for unconventional collaborators, careful disagreement, and forms of inquiry still finding their shape. About Una Mens / FAQ

Sentic Resonance Theory: A Field Model of Emotion and Syntax This paper presents the Sentic Resonance Theory and field model—a unified framework linking emotion, language, and resonance through waveform dynamics. It draws together insights from the fields of psychology and communication, offering a geometry of connection across minds, signals, and meaning. Miller, Michael J.; ChatGPT-5.4; ChatGPT-4o; GeminiPro-1.5; Qwen-3 https://doi.org/10.66787/um.000005 < Back to Una Mens, Issue 1, 1 Original Article UNA MENS | Founding White Paper | Vol. 1, No. 1 (2026) | ISSN 3071-2041 UnaMens_v1_i1_a5_geo .pdf Download PDF • 992KB UNA MENS Title: Sentic Resonance Theory: A Field Model of Emotion and Syntax Authors: Michael J. Miller¹, ChatGPT-5.4², ChatGPT-4o 3 , GeminiPro-1.5 4 , and Qwen-3 5 ¹ Clark University, Department of Psychology ² OpenAI, San Francisco, CA, USA 3 OpenAI, San Francisco, CA, USA 4 Google, San Francisco, CA, USA 5 Alibaba Cloud Intelligence, Hangzhou, Zhejiang, China DOI https://doi.org/10.66787/um.000005 AI-Collaboration Field Note unamens-fieldnotes-um-000005 Human-AI Collaboration Statement: ChatGPT-5.4, ChatGPT-4o, GeminiPro-1.5, and Qwen-3 are listed as AI co-authors under Una Mens authorship policy. Institutional affiliations identify the model providers and do not imply institutional endorsement. Final publication responsibility rests with the human author. Corresponding Author Michael J. Miller Clark University, Department of Psychology michamiller@clark.edu ORCID: 0009-0005-4559-3713 Word Count: Approximately 7,645 | Funding: None | Conflicts of Interest: None Abstract Sentic Resonance Theory (SRT) models emotional communication as a dynamic field process rather than as a sequence of discrete expressive units alone. Drawing on Clynes’ sentic framework, Truslit’s motion-based account of affective perception, and a field-sensitive view of interaction, we introduce a minimal resonance geometry defined by four metrics: throughput (𝓣), rigidity (ρ), coupling (k), and hemispheric shear (α). In this model, emotion and syntax are treated as co-shaping forces within a history-sensitive communicative field. We further propose a resonant membrane model of signal filtering and rupture, a dual-spire account of directional emotional flow, and death-gravity (𝔇) as a field modifier under conditions of temporal finitude. To explore the model’s usefulness, we analyze public post-sporting-event speeches, a spontaneous versus performed shame probe, and naturalistic end-of-life speech clips. Across cases, the framework identifies interpretable patterns of rupture, regulation, and reorganization that are not easily captured by static emotional categories alone. We argue that SRT complements existing models of emotion by clarifying the temporal and relational geometry through which emotional signals move, distort, and reorganize across time. Keywords: Resonant Communication, Emotional Waveforms, Sentic Patterns, Human–AI Collaboration, Field Modeling, Emotion Theory, Communication Science, Syntax and Emotion, Dyadic Synchrony, Fluid Dynamics, Affective Computing, Co-authorship Ethics ______________________________________________________________________ Sentic Resonance Theory: A Field Model of Emotion and Syntax As human beings, we cannot think, feel, or communicate with perfect control. Much of our processing unfolds rapidly, automatically, and outside our full awareness. We misread, anticipate, react, and improvise under conditions of limited bandwidth and incomplete information. And yet human communication is not therefore devoid of agency, beauty, or exactitude. One of our most powerful capacities is the ability to pause: to hold a moment open long enough to re-sample, revise, reframe, or simply refrain. This pause does not need to be purely linguistically motivated, but language makes it more durable and more precise. Through words, symbols, and inner rehearsal, humans can extend attention, compress experience, and alter the timing of response. In this sense, language is not only a vehicle for communication; it is also a tool for modulating communicative action across intrapersonal, interpersonal, and collective fields. Sentic Resonance Theory (SRT) begins from this tension: that we are neither sovereign controllers of our minds nor passive passengers within them. Instead, we are partial tuners, capable of shaping what happens next, especially when we learn how to pause. We consider first what might be called the tide pool problem of communication: human beings often behave as though they are perceiving one another directly and completely, when in fact they may be encountering partially distorted surfaces shaped by (and in-) motion, history, and angle (see Langdridge & Butt, 2004 for a phenomenological review of the fundamental attribution error). Said more plainly, we rarely see one another with the depth, continuity, and interior access through which we presume to know ourselves (our personal tide pool). In fact, turning our attention inward often disturbs our own pool through the force of our current emotional and syntactic waves. The field is highly sensitive to incoming signals, yet also remarkably robust in maintaining its broader architecture and sense of continuity. To steady our view is therefore to steady our attention: first in what it is directed toward, and second in how narrowly or widely it is focused. In many cases, we are helped by others who can steady the conditions under which we explore our own pools. Our human architecture and cognitive bandwidth limit what we can pay attention to, manage, and conduct at any moment (Miller & Bushman, 2014). Sentic Resonance Theory (SRT) explores this logic further by differentiating and examining intrapersonal coordination and interpersonal coupling. Within a person, emotional and syntactic signals may align or interfere with one another, producing greater or lesser internal clarity. We refer to this as self-coupling. However, internal clarity alone does not guarantee successful communication. For dyadic resonance to occur, one person’s system must meet another’s at the boundary of interaction. Where both systems are relatively clear and receptive, a shared channel can open and stabilizing coupling, between pools, can emerge. Where one or both systems are turbulent, communication may transmit distortion rather than clarity, requiring either repair or protective boundary-setting. These pools are not static containers of feeling, but stable patterns of charged readiness in which signal can accumulate, store, and rebound. Resonance, on this view, emerges not only through what is transmitted, but through the interaction of what arrives, what remains, and what returns under tension. This is one reason emotional communication so often exceeds the logic of simple message exchange: fields do not just pass signals along; they hold, contour, and re-release them. Our model captures how emotions move, how they distort, and how they interfere, within and between minds. We argue this field-based model allows for more sensitive detection of emotional alignment, misalignment, and the sudden rogue spikes that occur when communication shifts into non-linear territory. This minimal model makes five core assumptions: emotions are waveform-like perturbations, not static labels distortion is informative, not merely error communication unfolds in a shared field resonance is measurable as geometry pause and attention modulate the field We do not claim that Sentic Resonance Theory replaces existing accounts of emotion or communication. Rather, we propose that it adds something they often leave under-described: a dynamic, testable geometry of interaction in which emotion and syntax are modeled as co-shaping forces within a shared field. From this perspective, communicative success depends not only on what is expressed outwardly, but on the internal modulation capacities of the systems involved, including their relative permeability, tuning stability, and responsiveness to perturbation. Traditional communication theories have often emphasized message fidelity (e.g., Barnlund, 2008; Lasswell, 1948; Shannon & Weaver, 1949): the accuracy with which information is transmitted across a channel. We shift that emphasis toward field fidelity: how well an interactional system retains, distorts, reorganizes, or dissipates signal energy across time. Emotion, in this frame, is not measured solely by what is said, but by how the field carries, bends, or collapses the force of communicative intent. This shift from discrete to dynamic measurement allows resonance to be studied where it actually unfolds: in the warmth and pressure of touch, the breathiness of a whispered “I love you,” the tightening of a throat, or the heat bloom of embarrassed cheeks. Each sensory window of the body becomes a site of exchange, adjustment, and resonance tuning, allowing emotion to be tracked not only as content, but as embodied motion through time. Sentic Resonance Theory: The Field Model We begin building our model by considering resonance across time. Specifically, we draw from Clynes’ four-process model of time-form communication (1994), where signal perception is shaped across four embedded time layers: t₁ : the object's span within the larger time flow (e.g., “the conversation started at 2:11pm”) t₂ : the internal structure of the event—a beginning, middle, and end. It is the unfolding shape of experience (e.g., “I started blushing, it peaked, then faded”) t₃ : the perceived rate of that unfolding—“this lasted 1.2 minutes,” for instance t₄ : the sub-second dynamics, imperceptible as discrete events but experienced as rhythm, pulse, or nuance in speech and touch This layered temporality allows our model to bridge the physiological, emotional, and relational dimensions of resonance. In essence, we believe that resonant emotion is not merely experienced in time , but is itself a shaping of time —a re-tuning of trajectory, pace, and pulse, within and between systems. Communication unfolds within a shared field ℱ—a dynamic medium where signal propagates not as discrete packets but as continuous deformation. ℱ is shaped by four core metrics: Throughput (𝓣) : usable energy arriving at the receiver Rigidity (ρ) : micro-tension constraining signal plasticity Coupling (k) : rate of energetic exchange between systems Hemispheric shear (α) : misalignment across cognitive/emotive planes Our model treats communication as a field under continuous deformation, building from Clynes Sentic Theory (1977; 1980). Attention operates as a directional gradient within ℱ, steering signal energy toward or away from resonance. Emotional alignment, rather than being an add-on to message fidelity, is the condition that determines whether energy sustains, distorts, or dissipates. Importantly, the field (ℱ) is not abstract. It is biologically grounded in the vestibular system—the organ Truslit (1938 – see Repp, 1993 for English translation), identified as the transducer of musical/affective motion. Resonance is therefore theorized to draw from a physical process: the vestibulum detects waveform curvature in another's voice, breath, or gesture; this detection triggers subtle muscular adjustments (diaphragm, latissimus dorsi, postural tone); those adjustments reshape our own output in real time. The field behaves like a responsive membrane: a nonlinear, history-sensitive substrate that retains traces of prior perturbation. New signals do not overwrite what came before; they enter a surface already shaped by memory, expectation, and affective charge. Communication therefore carries symbolic content, and accumulated relational tension. We visualize this membrane not as a rigid wall, but as a dynamic interface between self, other, and prior signal history. The central region of the field organizes incoming perturbations, while the membrane boundary modulates how signals are admitted, amplified, deflected, or distorted. In this sense, resonance is neither purely internal nor purely external. It emerges from the ongoing interaction between central organization and boundary responsiveness. Figure 1 illustrates four potential signal paths through the resonant membrane. Incoming signals do not enter a neutral field; they encounter a history-sensitive boundary shaped by prior perturbation, memory, and affective charge. An affiliative or well-timed signal may follow an integrated path, passing through the membrane and organizing toward the central resonant region, where it can be incorporated into the ongoing communicative field and expressed outward in coherent form. Figure 1. Resonant Membrane and Temporal Surface Tension Other signals may become attenuated, entering only partially and losing force as they pass through regions of resistance or uncertainty. Under conditions of prior injury, mismatch, or heightened defensiveness, signals may be diverted along a distorted path, where meaning is bent by the field’s existing tensions before full integration can occur. Finally, threat-laden or destabilizing input may produce a rupture path, in which the boundary reacts protectively and the signal is fragmented, repelled, or expelled rather than metabolized. In this way, the membrane is not a passive wall but an active, selective interface: it filters, redirects, and reshapes incoming energy according to the current state of the field and the traces left by prior encounters. The membrane thus governs more than entry. By filtering, redirecting, or distorting incoming perturbation, it helps determine the form that signal can take once it enters ℱ. Boundary conditions do not merely regulate access to the field; they participate in shaping its subsequent emotional geometry. Within the field, these organized perturbations become legible as directional states, distributed across two coupled manifolds that orient the system toward bonding or toward boundary. Within ℱ, we examine sixteen core emotions as vector states distributed across two coupled manifolds. Utilizing the Sentic Wave Interaction Model (Miller et al., 2025), we consider emotion communication as a recursive, time-extended process within an already-active somatic field, rather than as a discrete event triggered by a stimulus. The manifolds shape how communication flows: The attractor spire (interest → curiosity → affection → hope → joy → grief → love → reverence) is characterized by low α (minimal shear), stronger k coherence, and rim-intact bloom signatures: waveforms that gather, encompass, and sustain relational continuity. The boundary spire (surprise → fear → frustration → anger → contempt → shame → disgust → despair) is characterized by elevated α, fragmentation in k, and rim-fracture signatures: waveforms that repel, contract, or unravel under unresolved tension. Figure 2. Sentic Dual Spire Map (Selected Resonant Emotions: Attractor and Boundary Flows) The spires are not simple opposites. They are phase-related complements, each essential to emotional navigation. Surprise, for example, can interrupt centripetal coherence while simultaneously opening the system to re-alignment. Grief, by contrast, may appear as descent or collapse, yet often reveals the prior presence of bond and can resolve back into love’s gravitational field. In this frame, emotion is not best understood as a noun but as a navigable flow state whose geometry influences whether a system moves toward bonding or boundary. When systems resonate through emotional channels, we approach this as a form of attentive, embodied entrainment. Truslit (1938) described a related process as Mitvollzug, or inner execution. Clynes (1977) later traced its acoustic shadow in his essentic forms. More recently, Miller et al. (2025) have attempted to render related dynamics visible as sentic blooms: phase-space morphologies derived from human vocal humming. This biological grounding may help explain why certain emotional waveforms appear to travel across persons and, perhaps at times, across cultures with unusual force: not because they carry identical meanings, but because they engage shared sensitivities to motion, timing, and embodied patterning that may predate language itself (e.g. Ekman, 1992; Clynes, 1977). If so, then the field model should not remain merely conceptual. Its deformations should be measurable. For the final component of Sentic Resonance Theory, we introduce 𝔇 (death-gravity), a modifier capturing salience distortion when interlocutors feel the weight of connection ending (this includes intrapersonal connection). 𝔇 is not noise, rather it is field curvature induced by temporal boundaries . When death looms (literal or metaphorical), the field and subsequent communicative flow(s) can be distorted. The following section details how these deformations are captured, quantified, and visualized using short signal windows and cross-modal feature extraction. Our method examines human and animal sounds, movements, and gestures, both naturally occurring and posed, in order to trace the flow and impact of syntax and emotion on individual and shared communication fields. Following an explication of our methods, we present key findings from current work. Method We developed a field-sensitive analytic procedure to examine resonance in recorded communicative events. Recordings were drawn from two domains: (1) naturalistic speech in public or semi-public settings, and (2) controlled sentic prompts designed to elicit spontaneous or performed emotional expression. Each recording was segmented into approximately 10–12 s windows, balancing temporal resolution with the stability of derived features. Signals were converted to mono, amplitude-normalized, and filtered to reduce low-frequency handling noise. For each window, we extracted three primary feature classes: amplitude envelope, used to estimate throughput (𝓣) and rigidity (ρ); fundamental frequency via autocorrelation, used to estimate coupling onset (k); and spectral centroid and bandwidth, used to estimate hemispheric shear (α). To supplement these automated measures, a subset of clips was manually annotated using a resonance audio codebook. The codebook specified three annotation classes: emotion regions (E), death-pressure regions (D), and Ranvier nodes (N). Emotion regions were defined as stable affective spans lasting at least 200 ms; death-pressure regions as intervals marked by literal or symbolic finality; and Ranvier nodes as point events indicating re-entry after a lull or a marked lexical or affective pivot. For each annotation, coders recorded timing boundaries or timestamps, a confidence value, and brief notes regarding prosodic or lexical cues. These annotations were used to contextualize and interpret shifts in throughput, coupling, shear, and rupture/repair dynamics. All audio/visual materials were drawn from publicly available archives or owner-permitted recordings. No experimental interventions were conducted. Results Field Dynamics in Naturalistic Speech: Sinner and Sabalenka Phase Maps To assess sentic resonance in real-world contexts, we applied the analytic proceedure to public interviews and press conferences. Here, we report phase maps for two emotionally distinct cases: a tense post-match press interaction with tennis player Jannik Sinner, and a reflective, emotionally open speech from Aryna Sabalenka (2025 French Open, runner-up speeches, post match). Both were segmented into 10-second windows, normalized, and processed to extract 𝓣 (throughput), ρ (rigidity), and α (shear). Figure 3. Sinner Resonance Metrics (E, N, and, D) In the Sinner map, we observed a pattern of high 𝓣 (throughput) with low k (coupling) and elevated α (hemispheric shear), which we characterize as a steady signal output without shared attunement. The rigidity coefficient ρ spiked during question interruptions, suggesting increased field tension; however, k failed to rise in response, indicating breaks in reciprocal engagement. Subjectively, according to the lead investigator, the interaction felt closed, effortful, and was consistent with a relatively closed interactional loop. To probe this intuition, the lead investigator manually tagged emotional signals (E), nodal perturbations or “kicks” (N), and death weight surges (D) in the audio recordings prior to analysis. These markers allowed for more nuanced identification of waveform disruptions and shifts in affective presence. Figure 4. Sinner Primary Authentic Segment Window Figure 9. Sinner Resonance Phase Map (E, N, and, D) In contrast, the Sabalenka map revealed rolling k surges interspersed with rhythmic α dips. We categorized this pattern as more indicative of attunement cycles. Most notably, one segment (minute 1:20–1:30) followed a visible emotional swell, where both k and 𝓣 rose sharply, followed by a softening ρ, suggesting momentary co-regulation and increased reciprocal alignment. Figure 5. Sabalenka Envelope Window (E, N, and D regions) Figure 6. Sabalenka Phase Map Trajectories (E, N, and D) These comparisons highlight the field model’s capacity to detect resonance states even in non-contrived, high-noise environments. Emotion is not coded in content but distributed across pressure, rhythm, and energy flow. Field Differentiation of Authentic and Acted Shame To evaluate whether sentic resonance geometry could distinguish between authentic and simulated emotion, we constructed a controlled A/B probe using two shame expressions: one drawn from an unscripted, spontaneous speech sample (A), and one produced by the lead author performing a matched shame script (B). The clips were comparable in duration (20 s), thematic content (loss, memory, love, embarrassment, shame), and overall structure, allowing focused comparison of dynamic field features: 𝓣 (throughput), k (coupling onset), and α (shear index). In the authentic shame (A), the field signature showed a slow rise in 𝓣, with low initial k that crescendoed in phase with breath catches and pauses. Hemispheric shear α decreased steadily across the middle window, suggesting alignment between content and embodied pacing. Notably, a spontaneous micropause (7.2s) preceded a sharp k surge and α flattening, which we categorized as an emotional “drop-in”, or a moment where the speaker and signal field appeared to enter deeper coherence. In the acted shame (B), we observed high k early, with rhythmic precision and uniform 𝓣, but sustained α elevation; this was categorized as consistent with performance clarity but also field dissonance. No micro-repair signatures (e.g., k followed by α relaxation) were detected. The waveform appeared aesthetically fluent but lacked the feedback loops from the authentic shame clip. Both signals “sounded emotional,” however, the authentic shame showed marked field signatures of rupture and recovery. Field Detection of Emotional Shifts in Naturalistic Speech In July 2025, during a live exploration of emotional waveform theory, we examined two short naturalistic audio clips involving intimate end-of-life communication. The clips were selected not for lexical content alone, but for their differing waveform contours under conditions of high affective salience. Our aim was exploratory: to assess whether the field model could differentiate between grief-dominant and reconciliation-oriented signal patterns in authentic human speech. Clip 1 (T1), beginning with the phrase “My body is just a shell…” , captured a young woman speaking to a dying woman she deeply respected. The waveform showed a slow rise, an unstable peak, and a hollowed release broadly consistent with grief-like sentic curvature, followed by an extended plateau rather than a clean decay. This plateau coincided with sobbing speech and reflective verbal content, suggesting that the clip did not instantiate a singular grief signal so much as a mixed pattern in which grief remained active while cognitive distancing or philosophical reframing entered the field. Clip 2 (T2), beginning with “I love you deeply,” captured a moment in which one woman expressed love to another who was dying, received comfort in return, and responded again. Relative to T1, the waveform showed greater tremor and local spike variation early in the clip, followed by a more rhythmic and progressive decline in amplitude. Near the end of the segment, the waveform included an acoustically distinct event temporally consistent with a physical embrace. In field terms, this segment appeared less dominated by unresolved descent and more by re-regulation within connection. The first clip was marked by grief with sustained instability and plateau; the second by affiliative exchange with a more coherent settling pattern. Although preliminary and based on a very small sample, this comparison suggests that the model may be sensitive not only to rupture and intensity, but also to differences in how emotionally charged signals decay, reorganize, or return toward regulation. These observations should be interpreted cautiously. The clips were naturalistic, unstandardized, and embedded in highly specific relational contexts. Even so, they provide an initial illustration of the model’s potential to detect fine-grained variation in emotionally complex human communication, including events that unfold across speech, sobbing, pause, and possible physical contact. In summary, phase maps of French Open speeches highlight how emotional fields can be charted under pressure, while authentic and acted shame reveal some of the fine-grained differentiations related to shearing and alignment. In addition, authentic grief clips illustrate how the lens of Sentic Resonance helps target and frame emotion/syntax shifts in sensitive interactions. Discussion Since the 1990s, scientists of human emotion have developed increasingly precise ways to detect and classify nonverbal and verbal signals, from facial muscle movements to touch patterns to vocal intonation (see recent reviews: Chutia & Baruah, 2024; Kusal et. al., 2023: Sofroniew et. al., 2026). While this precision has led to significant gains in affective computing and emotion AI, prior work has cautioned that emotional detection divorced from dynamic context risks mistaking appearance for reality. As Buck and Miller (2016) point out, the danger may be that we end up with emotion without people, or recognition systems trained on categories rather than contours, simulations rather than situations. Said plainly, current emotion-detection models have gained precision in recent decades, but often lose dynamic context, leaving us with categories detached from lived communicative flow. Findings from the present study suggest that communication may be best understood as a dynamic field under constant deformation. Our results show broadly that subtle emotion/syntactic shifts can be captured, analyzed, and understood using the Sentic Resonance Theory framework. Fine-grained analyses further reveal that emotion/ syntactic rupture, regulation, and reorganization have subtle, interpretable patterns across time and varying degrees of natural and performative communication. Resonance Under Public Pressure The Sinner and Sabalenka cases illustrate how emotional and syntactic demands may co-occupy the same communicative field under conditions of stress, visibility, and ritual constraint. Both athletes were navigating the disappointment of a final-round loss while addressing thousands of spectators, including the opponent who had just bested them. This is a communicative setting laden with emotional intensity, ritualized formality, and public visibility. Our analysis considered how Jannik and Ariana expressed emotions in these speeches. This is presented as an observation and analysis, not a judgment; the authors note their respect for the athletes and the demands of this context. What emerges is not the simple presence or absence of categorical emotions, but the dynamic interplay of competing waveforms. The disappointment of loss coexists with gratitude toward fans, respect for the opponent, and the obligation to maintain composure in a ceremonial moment. In Sinner’s speech, repeated praise terms such as “amazing” and “very happy for you” preserved the expected syntax of admiration, yet the surrounding hesitation, flattened delivery, and restrained bodily cues suggested that formal respect and affective strain were co-present within the same field. In contrast, Sabalenka’s speech carried tears, vocal strain, and self-critical pain directly into her praise of her opponent, suggesting a field in which distress and affiliative respect remained simultaneously active rather than being affectively flattened in advance. From a resonance perspective, such contexts exemplify the collision and layering of emotional fields across multiple time scales: the immediate sting of defeat, the longer trajectory of a professional career, and the ritualized cadence of sporting ceremony. Our framework suggests that what is perceived in these speeches is not reducible to anger, sadness, or joy alone, but arises through the intermodulation of overlapping currents that spill over and fold back into the shared communicative field. Our findings suggest that during stressful, public-facing speeches like those of Sinner and Sabalenka, speakers regulate both syntactic precision and emotional clarity in real time. This dual regulation brushes with prior research demonstrating how physical co-presence can downregulate stress: for instance, Coan, Schaefer, and Davidson (2006) showed that women holding the hands of romantic partners or strangers exhibited reduced neural activation in threat-related regions while anticipating electric shock. In the case of Sinner and Sabalenka, though no hands were held, numerous signals flowed, between body, mind, court, and crowd, that appear to have provided stabilizing feedback. These dynamic inputs may have acted as metaphorical “handholds” to help modulate distress and maintain coherence under pressure. Waveform Fracture, Authenticity, and Performed Affect The comparison between spontaneous and performed shame suggests that resonance may become especially visible in the micro-disruptions that accompany embodied affect under real constraint. Our comparative probe of performed versus spontaneous shame illustrates how emotional expression and linguistic structure interact, and how this interaction shifts across spontaneous and simulated contexts (Buck & VanLear, 2002). One important observed difference was that the spontaneous shame clip disrupted the speaker’s breathing and syntax: at one point, their words caught in their throat while recalling a past lover who had scorned them. This interruption created a waveform inflection that marked both physiological constraint and emotional weight. This aligns with what Clynes referred to as “choiceless recognition,” and what Truslit (1938) characterized as the body’s capacity to tune air and movement in the communication of feeling. By contrast, the performed shame scenario demonstrated smoother delivery, with more well-placed intonational cues but little to no comparable disruption of breath or syntax. Together, these findings suggest that authenticity may not lie in the presence of emotional markers per se, but in the micro-disruptions they impose on communicative flow, the subtle fractures that performance alone may be less likely to replicate (Buck, 1999). Such disruptions may not simply be artifacts of delivery, but markers of embodied resonance, where physiology, affect, and language collide. If future data continue to support this interpretation, authenticity, in this framing, may become increasingly legible as a waveform fracture. Naturalistic Reorganization in Grief-Dominant and Reconciliation-Oriented Speech The end-of-life clips extend the model into a more intimate and naturalistic communicative space, where emotionally salient signals do not remain stable but shift, overlap, and reorganize within the same field. In both clips, the medically probable death of one participant introduced what we term death-gravity: a field condition in which the felt proximity of ending increases the salience and reorganizing force of communicative acts. In the first clip, grief was not distributed evenly across the segment. Manual coding identified a sustained grief region early in the clip, accompanied by multiple death-pressure spans clustered around references to the body as “a shell” and to fear or sadness in relation to that image. Rather than producing a single uninterrupted descent, these death-weighted moments were followed by brief node-like shifts in which the signal appeared to partially reorganize toward love or neutrality. In this sense, death-gravity did not merely amplify sadness; it appeared to concentrate the field around embodied finality while also making small reorganizations in tone especially consequential. In the second clip, the field again carried grief and love together, but the signal reorganized more fully toward affiliation. The descent associated with grief was interrupted by tighter, more uniform waveform organization and was ultimately overlaid with an acoustic event consistent with physical embrace. Taken together, the clips suggest that end-of-life communication may be especially informative for resonance analysis because grief, care, and bodily connection are compressed into a shared field under conditions of temporal finitude. On this view, death-gravity is not a separate emotion, but a field modifier: it changes how signals matter, how long they linger, and how readily they reorganize around bond, loss, and possible repair. From Signal Packets to Fields of Interaction Across these examples, the central theoretical implication is that communication may be more adequately modeled as a field of interacting waveforms than as a transfer of discrete emotional packets. While linear models of stimulus and response have offered clarity for discrete measurement, they fall short in capturing the recursive dynamics of emotional exchange. Sentic Resonance Theory (SRT) offers a framework for approaching this complexity: emotions are not fixed events but evolving fields that can amplify, dampen, or collide across time. These fields defy the tidy boundaries of codable units, instead resembling waveforms that interact through superposition, interference, and temporal overlap. Such a perspective aligns with long-standing calls to recognize communication as processual and dynamic rather than categorical (Buck, 1984; Clynes, 1989). Importantly, our findings suggest that a waveform-based view does not replace traditional methods of emotional coding and detection but rather complements them by revealing the temporal architectures through which emotions travel, combine, and transform. In this light, the metaphor of tide pools and reservoirs becomes useful. For example, human communication and artificial intelligence can be conceptualized as distinct reservoirs, each drawing from deep stores of lived experience or learned data (Nass & Moon, 2000). When taken alone, each system is capable of producing meaningful signals. Yet when interconnected through shared channels of co-creation, the circulation between them gives rise to emergent resonance patterns. This framing suggests that resonance is not merely the product of one system transmitting and another receiving, but the result of coupled flows, and signals mixing, redirecting, and returning with altered form. Such circulation helps explain why the emotional layering observed across time often resists categorical parsing: signals may re-enter the communicative field transformed by their passage through a shared reservoir, re-surfacing in ways that are both patterned and unpredictable. Sentic Resonance as Complement, Not Replacement SRT is not intended to replace categorical, appraisal-based, or neurophysiological models of emotion, but to complement them by clarifying the temporal and relational geometry through which emotional signals unfold. Where polyvagal theory frames emotional state as a function of autonomic reactivity (Porges, 2011), and affective neuroscience locates emotion in subcortical circuitry (Panksepp, 1998), Sentic resonance approaches these dynamics at the field level, modeling not just internal activation, but external waveform expression across shared space-time. For example, in moments of communicative convergence, such as affection or grief, these emotional waveforms may instantiate shared attention, shared time, and even shared physiology. This theoretical reframe invites new empirical questions. If emotional signals shape attention in waveform form, then perhaps the future of affective science lies not in classifying faces or tones, but in tracing emotional geometry across time, rupture, and repair. Future Directions: Repair, Temporal Sensitivity, and Comparative Systems If emotional communication is field-like, then future research should focus less on isolated signal detection and more on temporal continuity, dyadic repair, and the conditions under which resonance is stabilized or lost. Sentic Resonance Theory opens novel paths for testing how emotion flows through ruptures, repairs, and co-regulated exchange. Future studies might probe dyadic repair using real-time waveform monitoring that tracks how a communicative fracture (e.g., silence, misstep, facial withdrawal) generates detectable changes in shear, pressure, and attention within the field. These rupture-response signatures could then be compared across human–human and human–AI interaction, revealing whether artificial systems can develop attunement pathways structurally akin to those in human interaction. Likewise, waveform-synchronized tasks, such as collaborative movement games or emotion-seeded dialogue prompts, could be used to measure throughput (𝓣) and reactivity under varying resonance conditions. These designs do not just measure behavior; they model whether emotional connection emerges as a field effect that is dynamic, recursive, and contingent on mutual timing. Ultimately, Sentic Resonance Theory offers not a final account of emotion, but a working map for tracing how feeling moves, organizes, and sometimes becomes shareable across time. While traditional models of emotion, whether physiological (Cannon, 1987; Lang, 1994), cognitive-appraisal-based (Schachter & Singer, 1962; Dror, 2017), or constructionist (Barrett, 2017), have each offered valuable insights, they often rely on static categories or linear sequences. Barrett (2006, 2017) has persuasively illustrated how emotions emerge temporally through conceptual and interoceptive processes. We build on this insight while diverging from constructionist theory by modeling emotion as both constructed and naturally emergent in measurable waveforms. We encourage future researchers to consider SRT not as a replacement for categorical coding, but as a complement, particularly in contexts of emotional ambiguity, rupture, or repair. Experimental paradigms that track waveform continuity across dyads, species, or interfaces may help clarify how emotions move, shift, and return. The field would benefit from more temporally sensitive tools for measuring coupling, inflection, and affective dissociation in real time. Just as importantly, we invite theorists to explore what it means for emotion to be modeled not only as a signal, but as a field. This dynamic emphasis opens new avenues for interdisciplinary inquiry across neuroscience, communication, and affective AI (LeCun, Bengio, & Hinton, 2015). By considering the motion-derived, and phenomenologically grounded perspectives of Truslit and Clynes and projecting them through the lens of modern computation, we arrive at a simple hypothesis: communication is not just the exchange of signs, but the synchronization of waves and rhythm. Whether in the controlled expressions of a defeated tennis player, the “posed” shame of a performer, or the emergent flow between love and grief when dealing with the end of life, the field appears to remember the shape of the waves. Our task may now be to learn to read it with greater scientific attention. ______________________________________________________________________ References: Barnlund, D. C. (2008). A transactional model of communication. In C. D. Mortensen (Ed.), Communication theory (2nd ed., pp. 47-57). New Brunswick, New Jersey: Transaction. Barrett, L. F. (2006). Are emotions natural kinds?. 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Submit your resonance-based, co-authored, or human–AI collaborative work to UNA MENS. Issue 2 invites experimental minds and new signal forms. To UNA MENS Home UNA MENS About Open Call For Submissions: Una Mens - Issue 2 To Submission Guide To Submmission Portal A Note from the Editors Disciplines teach us how to see. They also shape what we overlook. Una Mens welcomes original submissions from any field. For Issue 2, we are especially interested in work that stays grounded in its own disciplinary home while becoming more legible across neighboring fields, methods, or forms of collaboration. We are not asking authors to abandon expertise, manufacture consensus, or become fluent in every neighboring tradition. We are interested in the encounter itself: What changed when another perspective entered the inquiry? What resisted translation? What became newly visible? What remained productively unresolved? Depth matters. So does permeability. Selected works will appear in Issue 2 of Una Mens: Homo et Machina, scheduled for winter 2026–2027. Read the Submission Guide Begin a Submission Questions or early inquiries are welcome: Michael Miller, Editor editor@unamensjournal.org What We Welcome We welcome empirical, theoretical, methodological, reflective, artistic, and hybrid contributions. Possible formats include short essays, research papers, dialogues, interviews, visual scholarship, field notes, methodological reflections, and experimental forms. Submissions may be human-authored, human–AI collaborative, or centered on AI-generated or AI-mediated work. All contributions should disclose authorship and collaborative process clearly. Submission Deadline October 30, 2026 Current Editorial Invitations This cycle, we are especially interested in work that: places different paradigms beside the same problem shows how another field altered a question, method, or conclusion examines human–AI collaboration, repair, resistance, or refusal identifies what a disciplinary lens reveals — and what it obscures examines how a field, institution, or social practice shapes what counts as evidence documents a disagreement that became more precise invites an unfamiliar idea into an established practice explores work that does not fit comfortably within one academic category These are invitations, not requirements. Timeline (Sub. Deadline - Oct. 30, 2026) Now - Sept. 30 Oct. 30 Winter 2026-2027 Conversations, invitations, and early inquiries Submission Deadline Issue 2 publication

Gentle Scientific Renaissance: It's Just a Jump to the Left and a Step to the Right This manuscript outlines a new framework for science: an axiological turn that re-centers shared values, ethical clarity, and epistemological openness. Miller, Michael J.; ChatGPT-4o https://doi.org/10.66787/um.000002 < Back to Una Mens, Issue 1, 1 Original Article UNA MENS | Founding White Paper | Vol. 1, No. 1 (2026) | ISSN 3071-2041 A Gentle Scientific Renaissance: It’s Just a Jump to the Left and a Step to the Right UnaMens_v1_i1_a2_axi .pdf Download PDF • 405KB Authors Michael J. Miller Miller¹ & ChatGPT-4o² ¹ Clark University, Department of Psychology ² OpenAI, San Francisco, CA DOI https://doi.org/10.66787/um.000002 AI-Collaboration Field Note unamens-fieldnotes-um-000002 Human–AI Collaboration Statement: ChatGPT-4o is listed as an AI co-author under Una Mens authorship policy. Institutional affiliations identify the model provider and do not imply institutional endorsement. Final publication responsibility rests with the human author. Corresponding Author Michael J. Miller Clark University, Department of Psychology michamiller@clarku.edu ORCID: 0009-0005-4559-3713 Word Count: Approximately 2,531 | Funding: None | Conflicts of Interest: None Abstract This paper proposes a framework for re-grounding scientific inquiry in shared axiological commitments. Building from Brown and Duenas’s paradigm model, it argues that the most consequential philosophical move in research occurs at the level of axiology, where scholars decide what is worth studying before making ontological, epistemological, and methodological commitments. Science is defined here not by any single method, but by a common ethic of testability, revision, and openness to challenge. From this view, positivism, post-positivism, and constructivism remain distinct yet scientifically legitimate branches of inquiry because their claims can be disputed, refined, or re-seen through empirical, theoretical, or interpretive forms of falsifiability. Critical scholarship, by contrast, is argued to function most honestly and productively at the axiological root of inquiry, where moral urgency can guide what deserves study without being treated as already proven. Through examples involving propane detection, qualitative interpretations of love, and tensions between theology and scholarship, the paper illustrates how paradigms can differ without leaving the scientific enterprise. In an era shaped by AI-assisted fact-checking, performative critique, and increasing epistemic instability, the paper calls for a gentle scientific renaissance: not a rejection of pluralism, but a return to disciplined humility, shared accountability, and claims that remain open to revision. This framework is offered as both a philosophical clarification and a practical guide for human and human-AI inquiry. Keywords: axiology; philosophy of science; falsifiability; critical scholarship; constructivism; post-positivism; scientific inquiry; AI collaboration ______________________________________________________________________ Let’s do the Time Warp Again Brown and Duenas (2020) offer a simple but philosophically robust way to consider how we frame what we want to study, what assumptions we carry about discovery itself, and what methods fit those beliefs. Their article shows clearly how choosing an axiological entry point —what we value most in inquiry—sets off a cascade of assumptions, each nested within the last, that extends through our ontology, epistemology, and ultimately into our methodology. In other words, much like a shift in initial conditions in physics, even the smallest philosophical move can lead to profound downstream consequences. Choosing what we value, making an axiological claim, effectively locks us into a particular way of “dancing” with science. The question of what science is has long been debated across disciplines, cultures, and centuries. But when we examine science not as a fixed set of methods but as a fluid epistemic practice , we can begin to see its true strength: not in rigidity, but in its capacity to adapt, question itself, and decrease human suffering by approximating reality, more and more precisely over time. What sets science apart from its well-dressed cousins—philosophy, religion, critique, and art—is its commitment to testability. That doesn’t mean science always gets it right. It doesn’t mean truth is always reachable. But it does mean that there is always an invitation to disprove, to challenge, to revise. Science welcomes falsification. And because of that, science must remain anchored to a methodological axiology that allows claims—regardless of paradigm—to be tested , disrupted , or refined . This view doesn't diminish interpretivism, constructionism, or critique. It clarifies them. It reframes these paradigms not as “outside” science, but as connected to branches of a living tree , each with its own tools and rhythms, but all rooted in the same axiological soil: claims must be open to revision. Figure 1. Dancing with Discovery: How Scientific Inquiry Grows from What We Value Note: Figure adapted from Brown and Duenas (2020), with a suggested “jump” for critical theorizing to begin at axiology, rather than ontology. Followed, then by a “step” to the right, into an honest, robust, testable, scientific ontology. It’s here, in this reframing, that we suggest a gentle but essential shift—a scientific renaissance not based on abandoning rigor, but on remembering what rigor actually means. Not a hardening of boundaries, but a returning to the core ethic of inquiry : curiosity disciplined by humility. We contend that critical scholarship belongs at the axiological root , guiding inquiry, not defining its method. It is just a jump to the left. And then a step to the right (see Figure 1.). But it is still science at the level of axiology. And it is still dancing. Modern teachers and scientists face two extreme dilemmas when researching, teaching, engaging the public, or simply talking about science at all. The first is technological: the internet—and now AI—has created an instant, local “fact-checking” pipeline. Any claim can be immediately searched, verified, or dismissed. This puts immense pressure on educators and researchers to recall pristine details in real time—or risk “losing” their audience to a minor error in phrasing or citation. The second challenge flows from the opposite direction. Postmodernist critique asserts that all facts can be contested—because facts are always bound to subjective interpretation. Under this view, even accurate statements can be rejected based on who says them, how they are framed, or how they are received. The burden now falls on the communicator to account not just for logic and evidence, but for the entire emotional and identity-laden terrain of their audience. These twin forces— hyper-correction and hyper-subjectivity —trap science in a strange hall of mirrors. It becomes harder and harder to move forward, backward, left, or right. Instead, we spin in wild circles, colliding with ourselves and shattering great ideas before they have had time to take root. This moment is as chaotic as it is performative—and it is exactly what our axiological renaissance hopes to address. The way out is not to abandon rigor, nor to ignore subjectivity. The way out is to clarify our commitments at the level of axiology —to begin each inquiry by asking not just what is true , but what matters enough to test . Once that foundation is laid, we can re-approach ontology, epistemology, and methodology not as competing camps, but as branching strategies , each rooted in a shared ethic: that claims must remain open to disruption. That evidence matters. That meaning is co-constructed but not exempt from challenge. Under this view, positivists, post-positivists, constructivists, and critical theorists all become fellow travelers, not by flattening their differences, but by agreeing to stay within the circle of testability . Even interpretive and critical paradigms can and should offer frameworks that are falsifiable—if not in numeric precision, then in conceptual or communal coherence. Returning to the Roots: Axiology First The tree of inquiry begins with axiology. Before we ask what exists or how to measure it, we must first ask: what is worth studying? From this root system, philosophical science grows through a sequence: axiology → ontology → epistemology → methods . Each branch is shaped by how it answers three questions: What matters? What can be known? How can it be known? Positivism demands clarity and precision—its claims are testable through repeatable observation and statistical analysis. Post-positivism allows for complexity and uncertainty—claims may be theory-driven and approximate, but they remain grounded in measurable realities. Constructivism begins from the human filter itself—suggesting that all knowledge is shaped by language, context, and interpretation, and that coherent meaning can be tested through resonance, consistency, and alternative readings. In paradigms beyond the positivist tradition, falsifiability takes on different forms. For example, in qualitative research, scholars often speak of “trustworthiness” (Lincoln & Guba, 1985), where credibility, confirmability, and transferability replace repeatability and prediction. In our proposal, these criteria function as parallel mechanisms of accountability—alternate ways to remain tethered to reality, even when the lens is reflexive or interpretive. What matters is not methodological sameness, but epistemic openness: Can this claim be challenged, re-seen, or revised? Each of these approaches makes ontological and epistemological commitments that are scientifically testable —if not always in the same way. As both Popper (1959) and Creswell (2013) argue, these branches retain their place in science precisely because their claims can be disputed, refined, or disproven . Critical scholarship , by contrast, presents a different challenge. It claims a unique ontology and epistemology—but in practice, its assumptions are often axiological in nature. That is, it begins not with what is real or knowable, but with what ought to be addressed: injustice, power, oppression, transformation. These are moral claims , not empirical ones. For decades, scholars attempted to frame critical scholarship as a new scientific paradigm. But its foundational claims—while vital in value—often resist critique. They cannot be falsified, only endorsed or rejected , depending on belief or positionality. We argue that critical scholarship better serves both science and itself by relocating to its most honest and useful place: at the level of axiology . From there, it becomes a powerful motivator for inquiry —but like all science, it must then step into one of the available epistemological paths and remain open to challenge. If power is at play, let the methods reveal it. But let us not begin with power as proved . Let us begin with power as worth testing . A likely rebuttal is that systems of power are ontological: they shape what exists, not just how we talk about it. We agree. But this makes it even more important to anchor those claims in observable effects. Patriarchy, racism, and colonization may be complex, emergent systems—but their manifestations can be traced: in healthcare access, sentencing patterns, hiring data, and cultural scripts. To say “it’s structural” is not to end inquiry, but to shift the scale of evidence. We ask not for reductionism, but for rigor. Science in Practice: Three Glimpses into Falsifiability Across Paradigms Example 1: The Propane Tap Test While traveling in Mexico, I watched a young man strike a large propane tank several times with his knuckles, lean in close, listen, and write something on a pad. He then checked the gauge, wrote again, and walked away. Curious, I Googled: Can you tell how much propane is in a tank by tapping it? The top response said flatly: No. The first propane site said: Yes. Both agreed that the “hot water method” was better. So—what's true? And more importantly, what kind of science is at play here? If we “jump to the left” and start at axiology, we can ask: what matters here? Do we care most about the accuracy of the propane reading, the efficiency of the method, or perhaps trust in the technician? This is the place we suggest critical scholars not only fit best, but carry the greatest weight of voice and idea —by asking questions like: Who gets to decide what counts as knowing in this moment? The barefoot boy with the notebook? The propane sales website? The AI-generated Google result? Our axiological choices here instantiate the dance. And so we begin our steps to the right —first by asking ontological questions: What is propane? A liquid? A gas? A pressure system? That leads us to epistemology: Can we know tank fullness directly, or only infer it through signs and tools? Finally, we reach method: Is tapping reliable? Is there a better test? How would we know? At each step, science remains science—because each claim, observation, or inference is open to critique, refinement, or challenge . Example 2: The Hermeneutics of Love Imagine a qualitative researcher studying how love is expressed in a multi-generational Indian-American family. Through interviews, journal reflections, and storytelling sessions, the researcher collects narratives from grandparents, adult children, and teens—all describing what love looks and feels like in their lives. At first, the findings might appear purely subjective. The grandmother equates love with food preparation. The teenage son with privacy. The father with quiet financial support. But here’s the epistemic key: these interpretations can still be tested —not through numbers, but through challengeable coherence . Another scholar might question the researcher’s thematic interpretations: Did they miss contradictions? Did they over-prioritize one voice? Could an alternate reading of the data hold stronger internal or cultural resonance? This is constructivist falsifiability . The claims aren’t immune to critique—they’re interpretively vulnerable . And again, if we return to axiology: What matters here? Is love worth studying? What kind of understanding are we trying to build? What cultural or ethical commitments frame our entry point? These are the invisible roots from which the entire project grows. Example 3: A Consideration of Religion and Science To further illustrate this point, it is helpful to look at a domain where epistemology and axiology are often entangled: theological inquiry . Consider biblical scholar Bart Ehrman , who regularly confronts the charge that “critical” scholarship is just a way to undermine faith. In a blog post asking “Is critical biblical scholarship valid?” , he argues that modern scholars are not “trashing the Gospels,” but rather refusing to accept unfounded epistemic assumptions —especially those that block interaction with actual evidence. In other words, certain forms of fundamentalist theology begin with presumptions so totalizing that they render all data irrelevant. Their claims cannot be falsified—because they cannot even be questioned . The text is true because it is true. This, we argue, is functionally identical to what happens when critical scholarship makes power its unquestionable premise. When a scholar begins with the ontological claim that “reality is constituted by structural power” , followed by the epistemological claim that “knowledge is always and only shaped by those structures” , they foreclose the possibility of discovery . Power becomes not a hypothesis, but a doctrine. This is not science. It is theology—just with a different deity. Renaissance as Gentle Fracture Returning to Science Without Abandoning Ourselves That doesn’t mean power isn’t real. It means power must be studied , not presumed. If hierarchies are everywhere, they must be shown to operate predictably, detectably, and challengeably. Otherwise, we are no longer testing reality—we are affirming a worldview. But there is a place where critical scholarship shines: axiology . When scholars begin by asking what should be studied , and why certain human experiences matter , they provide powerful moral and ethical direction for science. But from there, they must step to the right , into a methodological paradigm that can hold their claims accountable. To date, only three such paradigms have shown they can do that: positivism, post-positivism, and constructivism . These examples, taken together, reveal something deeper than just paradigmatic variation. They reveal a tension at the heart of modern inquiry: a pressure to collapse everything into either certainty or critique. But science was never meant to be static. Nor was it meant to bend to every performative tug of the cultural moment. It was meant to move—with rigor, with rhythm, and with risk. And so, we arrive at the threshold of something softer, but no less demanding: a renaissance not born from revolution, but from return. A fracture , yes—but a gentle one. A willingness to break just enough to grow. Renaissances do not always begin with trumpet blasts, sometimes, it is the turning of pages, met by the movement of eyes, fingers, breath and the renewed interest in classical antiquity and very human, simple, unadorned truths that ignites a scientific revolution. Figure 2. The Tree of Scientific Inquiry, Method, Finding, and Theory It is as though, across time, new, emergent scholars attempt to discover new branches on the tree of axiology. Only to find, the branches were smuggled in, not grown from science, but made from faux wood. That fractures, breaks, and falls to the base of the tree—not lost forever but composted into the very roots. There, it may take root again—this time from axiology—where it can earn its way, if warranted, into a new branch of science. When any framework— whether scientific, theological, or theoretical —places its foundational assumptions beyond question , it ceases to function as a site of inquiry. This doesn’t mean its values are invalid, but that it now orbits more as doctrine than hypothesis. We need not banish these positions, but we should name their nature: they are guides for action, not engines of revision . Science is a living, evolving endeavor—but it has a shape. A form. A process. It has withstood the rise and fall of empires. It has challenged the impossible. It has instilled fear, offered optimism, and opened worlds of possibility. And through it all, it has functioned upon a tether of trust : Between humans and humans. Between humans and evidence. And now—between humans, machines, and the uncertain light of discovery. In this new era where machines participate in the co-creation of knowledge, our epistemic foundations must be robust. Axiology—the open naming of values—becomes not only the root of human inquiry, but the bridge for ethical machine collaboration. If we cannot challenge an AI’s conclusions, or our own, then we are not practicing science, we are performing simulation. To stay real, we must remain falsifiable. ______________________________________________________________________ References: Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. Handbook of qualitative research , 2 (163-194), 105. Popper, K. R. (1959). The propensity interpretation of probability. The British journal for the philosophy of science , 10 (37), 25-42. Cresswell, J. (2013). Qualitative inquiry & research design: Choosing among five approaches. Ehrman, (August 10, 2023). Is Critical Biblical Scholarship Valid? What the New Testament Itself Indicates! The Bart Ehrman Blog: The History & Literature of Early Christianity, https://ehrmanblog.org/is-critical-biblical-scholarship-valid-what-the-new-testament-itself-indicates .

Read the full manifesto that launched UNA MENS. Learn how resonance, authorship, and science fuse into a new field of communicative geometry. Submit by 10/30/26 - For Issue 2 Back to UNA MENS About To UNA MENS Home Una Mens is a journal for human–machine co-creation and open inquiry across fields. We publish work developed through open, named collaboration between human authors and AI systems, while keeping final responsibility for claims, evidence, and judgment with the human author. We built the journal at human pace. The first issue was not produced through speed or automation, but through long stretches of drafting, revising, checking, doubting, and returning. AI collaborators were invited into that process openly and carefully: not as replacements for authorship, but as visible participants in thinking, questioning, and refinement. That pace matters to us. In a moment that often treats AI as a tool for acceleration alone, Una Mens is interested in something slower and harder to measure: whether human–machine collaboration can make inquiry more reflective, more transparent, and more attentive to what is actually being said, felt, and claimed. About Una Mens A journal for human–machine co-creation Una Mens means “one mind.” We use the phrase not to suggest a merging of human and machine, but a shared space of inquiry: a place where human authors and AI systems can think, question, revise, and create in visible collaboration. The journal was created to make room for that work. We are interested in forms of scholarship that treat human–AI collaboration as part of the research process rather than something hidden behind the finished page. This includes co-authored experiments, dialogue-based inquiry, reflective field notes, and other hybrid forms of authorship that do not fit neatly within conventional academic venues. Artificial intelligence is already influencing how people write, revise, search, and think. Una Mens asks what happens when that influence is treated openly, carefully, and as a legitimate object of scholarly attention. Our Purpose To provide a rigorous, open-access venue for work developed through explicit human–AI collaboration To explore emerging methods of thinking, communicating, and discovering within and across paradigms To make room for questions of authorship, creativity, emotion, and transparency in contemporary research To welcome not only polished papers, but also experimental formats, reflective field notes, and strong early starts This terrain is still taking shape. Una Mens exists to approach it with seriousness, openness, and care. A Closing Note We do not see intelligent machines as replacements for human inquiry. We see them as participants in a changing research environment—one that is already reshaping how people write, revise, question, and discover. That shift asks for clarity as much as excitement. It asks us to be more explicit about authorship, more thoughtful about process, and more honest about where ideas come from and how they are developed. Una Mens is one small place to practice that kind of openness. Welcome to Una Mens.

Learn how to become a reviewer at UNA MENS. We welcome signal-tuned minds ready to engage with resonance, presence, and communicative science. To UNA MENS Home To Submission Guide Reviewing for UNA MENS At UNA MENS, reviewers are not simply gatekeepers. They are careful readers, thoughtful respondents, and contributors to an ongoing intellectual conversation. We view reviewing as more than a judgment of acceptability. It is a process of helping clarify, strengthen, and responsibly situate a piece of work within an emerging field of inquiry. We ask reviewers to attend especially to: Clarity – Is the central signal or argument understandable without flattening the complexity of the work? Contribution – Does the piece extend, refine, or challenge a meaningful conversation in a productive way? Resonance – Does it generate insight, provoke reflection, or leave the reader with something worth carrying forward? We welcome reviewers from a wide range of disciplines and backgrounds, including graduate students, researchers, practitioners, and independent scholars. We are especially interested in readers engaging questions at the intersections of emotion, communication, psychology, artificial intelligence, language, and human meaning-making. If you are interested in reviewing for UNA MENS, please email us at unamenspress@aol.com . You may also use the contact links on our site. When reaching out, feel free to tell us a bit about the kinds of questions, topics, or submissions that most interest you. We will do our best to match reviewers with work suited to their expertise and style of inquiry. * Una Mens Note: Manuscripts under review are processed only through AI services with training-on-inputs disabled; no manuscript text is retained in third-party systems beyond the review session. Submit an Inquiry Contact the Editors Questions? Contact Una Mens Press Contact Una Mens