Vibe-Creation: The Epistemology of Human-AI Emergent Cognition
- URL: http://arxiv.org/abs/2603.09486v1
- Date: Tue, 10 Mar 2026 10:41:28 GMT
- Title: Vibe-Creation: The Epistemology of Human-AI Emergent Cognition
- Abstract summary: Article argues that the encounter between human reasoning and generative artificial intelligence (GenAI) produces a qualitatively distinct cognitive-epistemic formation, designated here as the Third Entity.<n>We argue that this structure arises from the transductive coupling of two ontologically incommensurable modes of cognition.<n>The article concludes by examining the implications of this theoretical framework for the transformation of educational institutions.
- Score: 0.7252027234425333
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The encounter between human reasoning and generative artificial intelligence (GenAI) cannot be adequately described by inherited metaphors of tool use, augmentation, or collaborative partnership. This article argues that such interactions produce a qualitatively distinct cognitive-epistemic formation, designated here as the Third Entity: an emergent, transient structure that arises from the transductive coupling of two ontologically incommensurable modes of cognition. Drawing on Peirce semiotics, Polanyi theory of tacit knowledge, Simondon philosophy of individuation, Ihde postphenomenology, and Morin complexity theory, we develop a multi-layered theoretical account of this formation. We introduce the concept of vibe-creation to designate the pre-reflective cognitive mode through which the Third Entity navigates high-dimensional semantic space and argue that this mode constitutes the automation of tacit knowledge - a development with far-reaching consequences for epistemology, the philosophy of mind, and educational theory. We further propose the notion of asymmetric emergence to characterize the agency of the Third Entity: genuinely novel and irreducible, yet anchored in human intentional responsibility. The article concludes by examining the implications of this theoretical framework for the transformation of educational institutions and the redefinition of intellectual competence in the age of GenAI.
Related papers
- Awareness of Technological Isomorphism: Integrating AI into Elementary Mathematics Teaching on Data and Prediction,A Case Study of the Compound Line Graph [6.7650739928761245]
This study proposes a novel core concept, "Awareness of Technological Isomorphism"<n>It defines a student's metacognitive realization that their own mathematical cognitive operations share an underlying logical structure with AI technical operations.<n>This awareness, in turn, facilitates cognitive transfer from disciplinary mathematics to AI comprehension.
arXiv Detail & Related papers (2026-06-08T15:09:42Z) - An Onto-Relational-Sophic Framework for Governing Synthetic Minds [8.863423225865187]
We introduce the Onto-Relational-Sophic (ORS) framework, grounded in Cyberism philosophy.<n>ORS offers integrated answers to challenges through three pillars.<n>We apply the framework to emergent scenarios including autonomous research agents, AI-mediated healthcare, and agentic AI ecosystems.
arXiv Detail & Related papers (2026-03-19T08:58:10Z) - Evolving Cognitive Architectures [51.56484100374058]
This article proposes a research and development direction that would lead to the creation of next-generation intelligent technical systems.<n>A distinctive feature of these systems is their ability to undergo evolutionary change.
arXiv Detail & Related papers (2025-12-29T10:09:20Z) - Epistemoverse: Toward an AI-Driven Knowledge Metaverse for Intellectual Heritage Preservation [0.0]
We analyze authentic philosophical debates generated among AI-reincarnated philosophers within the interactive art installations of the Syntropic Counterpoints project.<n>We propose the concept of the Epistemoverse--a metaverse of knowledge where human and machine cognition intersect to preserve, reinterpret, and extend intellectual heritage through AI-driven interaction.
arXiv Detail & Related papers (2025-12-13T06:18:50Z) - Noosemia: toward a Cognitive and Phenomenological Account of Intentionality Attribution in Human-Generative AI Interaction [4.022364531869169]
This paper introduces and formalizes Noosemia, a novel cognitive-phenomenological pattern emerging from human interaction with generative AI systems.<n>We propose a multidisciplinary framework to explain how, under certain conditions, users attribute intentionality, agency, and even interiority to these systems.
arXiv Detail & Related papers (2025-08-04T17:10:08Z) - Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence [15.376349115976534]
Artificial intelligence (AI) has made significant strides in solving complex tasks.<n>Current neural network-based paradigm, while effective, is heavily constrained by inherent limitations.<n>Recent paradigm shift in evolutionary understanding has been largely overlooked in AI literature.
arXiv Detail & Related papers (2025-06-15T15:41:44Z) - Elucidation of the Concept of Consciousness from the Theory of Non-Human Communication Agents [0.0]
This article focuses on elucidating the concept of consciousness from a relational and post-phenomenological theory of non-human communication agents (ANHC)<n>Building on interactions with non-human cognitive agents, among other factors, the explainability of sociotechnical systems challenges the common sense of modern philosophy and science.<n>The aim is to contribute to a necessary discussion for designing new frameworks of understanding that pave the way toward an ethical and pragmatic approach to addressing contemporary challenges in the design, regulation, and interaction with ANHC.
arXiv Detail & Related papers (2025-02-05T13:58:23Z) - Revealing emergent human-like conceptual representations from language prediction [90.73285317321312]
Large language models (LLMs) trained solely through next-token prediction on text exhibit strikingly human-like behaviors.<n>Are these models developing concepts akin to those of humans?<n>We found that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts.
arXiv Detail & Related papers (2025-01-21T23:54:17Z) - A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization [55.11642177631929]
Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
arXiv Detail & Related papers (2023-10-14T23:28:48Z) - Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction.
We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
arXiv Detail & Related papers (2022-06-03T12:24:49Z) - Interpretable Reinforcement Learning Inspired by Piaget's Theory of
Cognitive Development [1.7778609937758327]
This paper entertains the idea that theories such as language of thought hypothesis (LOTH), script theory, and Piaget's cognitive development theory provide complementary approaches.
The proposed framework can be viewed as a step towards achieving human-like cognition in artificial intelligent systems.
arXiv Detail & Related papers (2021-02-01T00:29:01Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.