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Submit your manuscript to Una Mens: Homo et Machina — journal of human–AI co-creation. ISSN 3071-2041 Una Mens - Manuscript Submission Form Submit to Una Mens We invite you to submit theoretical, empirical, methodological, and creative scholarship across disciplines. If AI helped shape your work, we ask you to document that collaboration openly [AI-Collaboration Note Guide] . At Una Mens, we ask authors not to hide AI's role, but to show it clearly, so readers can follow how ideas were co-created. Before you submit, please have ready: manuscript title author and co-author details abstract or short description manuscript file Human–AI Collaboration Field Note or declaration of no AI use licensing preference Author Information First name * Last name* Email* Affiliation / Institution / Independent* 2–3 sentence author bio Human Co-authors Co-author name(s) (enter “none” if solo-authored)* example: Miller, Michael; Buck, Ross Co-Author Emails example: mm@mail.com; rb@mail.com AI Systems Used (if any) AI-System Model(s)* example: ChatGPT-5.4; Qwen-3 AI Declaration - Involvement Level* AI Level of Involvement Descriptions Light — minor support only; no major effect on argument or analysis Moderate — meaningful help with structure, revision, or idea development Substantial — sustained AI involvement that materially shaped the manuscript Central — AI collaboration is integral to the article’s method, evidence, or authorship structure Upload AI-Collaboration Statement Upload File Please visit our AI-Collaboration Note Tutorial for help with this section: ---------------------------------------------------- [AI-Collaboration Note Guide] Manuscript Information Title* Abstract / summary* 250-300 words recommended Word Count* manuscript word count (including references) Submission category* Manuscript upload* Upload File upload full, blind manuscript as 1-4 document(s); accepted formats (.doc or .pdf) How your submission is reviewed: Every manuscript submitted to Una Mens is read in full by a human editor. At this stage of the journal's life, that human is the chief editor: no submission is evaluated, declined, or accepted without complete human reading and human judgement. AI systems may assist in review only under the constraints described in our editorial policy, and never as a substitute for the human read. What happens after you submit You will receive an email confirmation at the address you provide. Within three days, a human, not an autoresponder, will follow up with your submission ID (format: UM-2026-###). Please include that ID in any correspondence about your manuscript. If you don't hear from us within three days, write to editor@unamensjournal.org ; forms occasionally misfire, and we would rather hear from you twice than not at all. Response Time We aim to return either a decision or a substantitive status update within 14 days of confirming receipt. Submit To UNA MENS Home To Submission Guide To Review FAQ

Learn why Una Mens asks for AI collaboration field notes for AI, co-created manuscripts. In addition, use the Una Mens, Field Note Template to create a quick and strong summary of your AI interactions. To UNA MENS Home To UNA MENS Editorial Policy To Submission Guide Una Mens: AI-Collaboration Field Note Una Mens' field notes document how a human–AI collaboration unfolded, what roles were played, what choices were made, and who retained final responsibility. AI-System(s) Level of Involvement & Contribution These levels are not meant as moral rankings. They are intended to help authors describe the scale and role of AI involvement with enough precision to support editorial review and reader understanding. When in doubt, choose the level that best reflects how much the final form of the manuscript depended on AI involvement, not merely how often AI was consulted. If AI involvement varied across phases, report the highest sustained level and let the timeline show the variation. Moreover, the levels describe the scale and role of AI involvement, not its merit — a "Central" collaboration is not better scholarship than a "Light" one, only a different kind. What separates them is a single question: how much the work's final form depended on the AI's contribution. Credit follows that line. Below it, contributions are usually acknowledged; at and above it, they ordinarily earn a byline. Light AI assisted at the surface without shaping the substance. The core ideas, structure, analysis, and claims would read essentially the same without it — grammar and copyediting, formatting, title or search-term brainstorming. Light involvement is acknowledged rather than credited, unless a single small suggestion carried outsized conceptual weight. Extended Time Frame/ Limited AI Collaboration Example: A Gentle Scientific Renaissance --> (click this link to visit, or return to the Renaissance AI-Collaboration Note) Single AI Collaborator, Basic, Moderate Example: The Obverse Turing Test --> (click this link to visit, or return to the Obverse AI-Collaboration Note) Moderate AI meaningfully assisted the thinking or the shaping — outlining, organizing, revising prose, testing phrasing, brainstorming sections — while the argument, analysis, and written form remained primarily human-developed and human-directed. This is the threshold zone: reviewed case by case, with a strong presumption toward byline credit wherever the contribution materially changed the work's thesis, structure, interpretation, or language. Substantial AI played a significant, sustained role in developing, extending, revising, or pressure-testing the work. The human author stayed clearly in charge of judgment, evidence, and final decisions, but the work's final form was materially shaped by the collaboration — theory development, major drafting or revision, codebook generation, cross-model critique, figure development. Substantial involvement ordinarily receives byline credit. Extended Time Frame/ Multi-phase / Multi-AI Example: Sentic Resonance Field Model --> (click this link to visit, or return to the Sentic Field Model AI-Collaboration Note) Central AI involvement was not only substantial but constitutive: integral to the work's method, evidence, object of study, or authorship structure, such that the piece cannot be accurately described without foregrounding the human–AI collaboration itself — articles that study AI dialogue directly, that draw on extended AI interaction as part of their dataset or method, or that present the collaboration as a central feature. Central involvement always receives byline credit. Sustained AI Dialog and AI Collaboration as Metho Example: Sentic Intelligence --> (click this link to visit, or return to the Sentic Intelligence AI-Collaboration Field Note) Field notes are your chance to show readers how your ideas were made—not just what you found, but how you worked with AI systems along the way. Think of them as a short narrative of your collaboration: what AI contributed, what you decided to keep or change, and how the partnership shaped your thinking. At Una Mens, we believe transparency about process strengthens scholarship. These field notes help readers see not only what you concluded, but how you got there—and how AI was part of that journey. Most field notes can be completed in 300–800 words, plus a simple timeline. Una Mens AI Collaboration Note Template PDF Doc Click on a document image to download Una Mens' AI Collaboration Note Template. Una Mens AI Collaboration Field Note Checklist The template covers the following categories: AI Collaborators Collaboration Pattern Primary Collaboration Modes Human Role AI Role Guardrails Used AI Suggestions Set Aside AI Surprise Human Surprise Final Responsibility Collaboration Timeline Phases Additional Notes / Reflections Tips for reading and writing Una Mens AI-Collaboration Field Notes Tip 1 — Reviewers Will Read the Field Note Closely The AI Collaboration Field Note will be shared with reviewers alongside your manuscript. It provides essential context for evaluation, especially regarding the novelty, transparency, and integrity of the collaborative process. Tip 2 — Open Prompting Is Encouraged, Not Required Authors are encouraged, but not required, to submit relevant prompt logs or selected excerpts as supplementary material. If these materials are not submitted initially, authors should be prepared to share them with the editorial team upon request during review. Tip 3 — Moderate vs. Substantial Contribution A useful rule of thumb is this: a contribution is substantial when it materially changes the work’s intellectual core, and moderate when it primarily helps the human author express, refine, or organize their own core ideas more effectively. Tip 4 — How to Read an AI Collaboration Field Note An AI Collaboration Field Note is a brief companion to the article, not a promotional add-on. It helps readers see how the work was made: what the human author contributed, what the AI contributed, where the collaboration clarified or distorted the process, and how final judgment was exercised. Read it as a transparency document, a reflection on method, and a small record of inquiry in motion. Tip 5 — Clarity Matters More Than Performance A strong field note does not need to make the collaboration look elegant or impressive. Its purpose is to describe the process clearly and honestly, including uncertainty, drift, friction, revision, or moments where the collaboration failed to help. Submit an Inquiry Contact the Editors Questions? Contact Una Mens Press Contact Una Mens

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.

An introduction to Una Mens, along with details regarding our editorial processes, and submission processes. Back to UNA MENS About To UNA MENS Home To Submission Guide Start Here: What Una Mens Is, Who It’s For, and How It Works Una Mens is an independent, open-access journal for original scholarship across fields, with a distinctive commitment to transparent human–AI collaboration and clear accounts of how work was made. Top FAQ Questions for Una Mens What is this journal? Una Mens is an online journal that publishes theoretical, empirical, methodological, reflective, and creative scholarship across disciplines. What distinguishes the journal is not a single subject, but a shared commitment to open inquiry, visible process, and transparent collaboration. Who is it for? Researchers, students, artists, educators, independent scholars, and interdisciplinary thinkers who want to do serious work openly — including work shaped through human–AI collaboration. What counts as publication here? Peer-reviewed articles, editor-reviewed essays, methodological papers, experimental reports, reflective pieces, visual scholarship, field notes, and other accepted forms that meet journal standards for clarity, transparency, and contribution. What is peer review here? All submissions to Una Mens receive full human editorial review. Depending on the nature of the work, submissions may also receive external peer review, open commentary, or other forms of editorial evaluation. Our aim is to support rigor, transparency, and useful scholarly exchange across a range of forms. Editorial Policy in Plain Language Who can be listed as an author? Una Mens allows human authors, AI co-authors, and hybrid author teams in cases where contributions are substantive, clearly documented, and transparently attributed. Byline credit reflects documented contribution to the work; it does not transfer responsibility away from the submitting human author. What responsibilities do human authors retain? Human submitting authors are responsible for: final review and approval factual accuracy and citation integrity disclosure of AI use and collaborative process permissions, ethics, and originality communication with reviewers and editors accepting responsibility for corrections or retractions if needed How are AI contributions documented? Primary Human authors must document AI contributions through a: Human–AI Collaboration Statement AI contributions statement must include: which AI system or systems were used the model or version, if known what the AI contributed what the human author contributed whether the AI is listed in the byline or acknowledged only whether prompts, logs, or selected excerpts are archived or available on request How does review work? All submissions to Una Mens receive full human editorial review. Depending on the nature of the work, submissions may also receive one or more of the following: Open Peer Review — named reviewers engage directly with the work Open Commentary — reflective or critical responses accompany the piece Editorial Curation — the editorial team evaluates and curates experimental, poetic, visual, or boundary-crossing work Not every submission follows the same review path. The review process depends on the genre, aims, methods, and evidentiary style of the work. What counts as acceptance? A submission is accepted when it is judged by the editor and, where relevant, external reviewers to meet Una Mens standards for contribution, clarity, transparency, integrity, and fit with the journal’s mission. Acceptance may follow revision. Una Mens Core General Standards: contribution clarity transparency integrity fit What ethical standards apply? Una Mens does not accept fabricated data, undisclosed AI-generated writing, plagiarized content, false attribution, or unverifiable claims presented as fact. Authors must disclose collaborative methods honestly and distinguish clearly between evidence, speculation, and creative interpretation. Is the journal peer-reviewed, editorially reviewed, or both? Una Mens is an editor-led journal that uses both editorial review and peer review. All submissions receive human editorial review. Some submissions also receive external peer review or open commentary, depending on the nature of the work. Submitting to Una Mens To submit you work please go to the Submission Guide - https://www.unamensjournal.org/una-mens-submission-guide and follow the submission steps. This is a link to our first external call for submission (Volume 1, Issue 2)- https://www. unamensjournal.org /unamens-issue2-submission-call Here is the page to submit your final manuscripts and supplementary files- https://www. unamensjournal.org /unamens-submission-portal * 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.

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.

A thank you page for after you have submitted your work to Una Mens. Please contact us with questions. Responses to submissions take approximately 1-3 business days. Back to UNA MENS About To UNA MENS Home To Submission Guide UNA MENS Journal — Thank You Your submission to Una Mens Journal has been submitted. Please wait 1-3 days for an email response from editor@unamensjournal.org . Feel free to contact us with any question. We look forward to reading your manuscript. Submit an Inquiry Contact the Editors Questions? Contact Una Mens Press Contact Una Mens

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). 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