First study on: Quantum–Emotional Field Theory (QEFT): An Informational Framework Linking Quantum Dynamics, Predictive Processing, and Context-Dependent Cognitive Stability
This work introduces Quantum–Emotional Field Theory (QEFT), a unified informational framework in which quantum state evolution, cognitive dynamics, and artificial intelligence instability are interpreted as manifestations of a common underlying structure governed by contextual informational fields. Within this model, the standard quantum formalism is extended by a contextual modulation term representing informational salience gradients of the environment. These gradients are not interpreted as subjective emotions, but as objective parameters that regulate state accessibility, compression, and effective coherence. The framework builds on Informational State Dynamics (ISD) and integrates concepts from predictive processing, free-energy minimization, and computational instability in large-scale generative models. Cognitive and artificial systems are modeled as observer-relative compression operators acting on a shared informational state space. A central contribution is the formalization of an informational production–integration balance, which determines system stability across scales. When production of informational states exceeds integration capacity, systems undergo a transition to unstable regimes characterized by loss of coherence, increased variability, and emergent structural fragmentation. This mechanism is proposed as a unifying explanation for quantum decoherence patterns, cognitive overload phenomena, and large language model hallucination regimes. QEFT remains empirically compatible with standard quantum mechanics while providing a higher-order interpretational layer that connects physical, cognitive, and computational domains through a single informational principle.
This work introduces Quantum–Emotional Field Theory (QEFT), a unified informational framework in which quantum state evolution, cognitive dynamics, and artificial intelligence instability are interpreted as manifestations of a common underlying structure governed by contextual informational fields. Within this model, the standard quantum formalism is extended by a contextual modulation term representing informational salience gradients of the environment. These gradients are not interpreted as subjective emotions, but as objective parameters that regulate state accessibility, compression, and effective coherence. The framework builds on Informational State Dynamics (ISD) and integrates concepts from predictive processing, free-energy minimization, and computational instability in large-scale generative models. Cognitive and artificial systems are modeled as observer-relative compression operators acting on a shared informational state space. A central contribution is the formalization of an informational production–integration balance, which determines system stability across scales. When production of informational states exceeds integration capacity, systems undergo a transition to unstable regimes characterized by loss of coherence, increased variability, and emergent structural fragmentation. This mechanism is proposed as a unifying explanation for quantum decoherence patterns, cognitive overload phenomena, and large language model hallucination regimes. QEFT remains empirically compatible with standard quantum mechanics while providing a higher-order interpretational layer that connects physical, cognitive, and computational domains through a single informational principle.
Human reasoning is traditionally modeled through rational-order frameworks that assume stability, separability, and coherence. Yet across judgment, valuation, perception, and social decision-making, empirical work consistently reveals patterned violations of these assumptions. These deviations intensify in real-world contexts shaped by institutional constraints, identity commitments, and collective narratives, where reasoning must navigate incompatible interpretive frames and interdependent evaluative pressures. Existing theories typically treat such phenomena as noise, bias, or bounded rationality, leaving no formal account of how rational-order rules interact with the variability inherent in social domains. This article proposes a structural framework that explains why these two regimes diverge and how their interaction produces systematic mismatches. Building on quantum probability theory, not as a physical metaphor but as a representational tool, it formalizes evaluative states that remain indeterminate until elicited, transform under contextual modulation, and become relationally coupled across agents and domains. Whereas, existing quantum-cognition models primarily address task-level effects such as conjunction errors or order dependence, this framework scales quantum principles to socially and institutionally embedded reasoning. The account identifies contradiction, interference, entanglement, and resolution as recurrent properties of real-world cognition and shows how quantum formalisms provide a coherent vocabulary for capturing these phenomena. To support cumulative progress, the article outlines a research agenda with empirically testable designs for distinguishing incompatible bases, assessing inseparability, modeling context-driven transformations, and integrating multi-level reasoning environments. This program positions quantum and classical approaches within a unified architecture and advances a broader science of reasoning.
Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.
Understanding the foundations of artificial cognitive consciousness remains a central challenge in artificial intelligence (AI) and cognitive science. Traditional computational models, including deep learning and symbolic AI, have demonstrated remarkable performance in pattern recognition and decisionmaking tasks. However, they lack essential features of consciousness, such as subjective experience, autonomous goal formation, and self-reflective processing. While theories like Integrated Information Theory (IIT) and Orch-OR suggest possible mechanisms for consciousness, they remain either mathematically abstract or face empirical challenges due to decoherence in biological conditions. This study addresses the fundamental research gap by proposing a quantum-information-based framework for artificial cognitive consciousness. Specifically, we introduce the Quantum Reality Function (QRF), a model that formalizes subjective cognition through global quantum coherence, quantum entanglement, and non-local informational integration. The QRF establishes a structured mechanism for autonomous decision-making and selfgenerated states, which existing computational approaches lack. Our methodological contribution involves defining a mathematically consistent formulation of QRF and demonstrating its feasibility through quantum coherence stabilization techniques, hybrid quantum-classical architectures, and coherencepreserving quantum cognitive subsystems. The results show that maintaining long-lived quantum coherence within cognitive architectures significantly enhances information integration, supporting the emergence of subjective-like processing. The findings suggest a new paradigm for AI, where consciousness emerges as an intrinsic property of structured quantuminformation processing rather than a byproduct of computation. This work provides foundational insights for the development of autonomous quantum-cognitive AI systems, with implications for ethics, AI governance, and the future of machine intelligence. Future research will focus on empirical validation through quantum computational experiments and integration with quantumassisted learning models.
Over the past two decades, quantum-like modeling (QLM) has emerged as a powerful framework for describing non-classical features of cognition and decision-making. Rather than assuming physical quantum processes in the brain, QLM employs the Hilbert space formalism to model contextuality, incompatibility of mental observables, and entanglement-like correlations. In this paper, we develop a quantum-informational model of mental markers within the broader I-field (information field) approach. We propose that, under conditions of information overload and limited cognitive resources, individuals primarily respond not to detailed semantic content but to compact content labels - mental markers - carrying cognitive and affective components. We formalize mental markers as structured quantum-like states and analyze the nonclassical correlations between their cognitive and affective components using the Contextuality-Incompatibility-Entanglement triad. Special attention is given to intra-system entanglement between rational (cognitive) evaluation and emotional (affective) coloring, accounting for context-dependent judgments, order effects, and affect-driven decision shifts. Illustrative examples with psychological interpretation and experimental perspectives are provided. An Appendix briefly discusses neurobiological analogues of information overload in neural networks, highlighting structural parallels with the proposed marker-based framework; coupling to the origin and diagnostics of neurological diseases is analyzed. The paper contributes to QLM by distinguishing inter-system and intra-system entanglement and by demonstrating that cognitive - affective entanglement constitutes a fundamental structural feature of mental markers in socially mediated information environments.
Abstract: Fragmentation is a defining feature of contemporary knowledge: quantum physicists, neuroscientists, economists, organizational theorists, linguists, and AI engineers each inhabit their own logic trees as internally coherent but mutually opaque structures of definitions, theorems, and methods. What is missing is a lightweight but precise intermediary framework that can map structural patterns, particularly coherence and entropy, across these domains without erasing their differences or sliding into pseudoscience. This article develops such a framework using the Grand Unified Field Theory of Coherence (GUFT) and its behavioural extension, ΔSyn. GUFT defines a field-level coherence index Ψ = E × T, where E (Empathy) measures the strength of reciprocal correlation or responsiveness between subsystems, and T (Transparency) measures the degree to which those correlations and dynamics are inspectable and modelled. ΔSyn adds an entropy-change law linking entropy dynamics to gradients in a coarse-grained entropy functional H and an ethical symmetry term E_s. We use quantum mechanics, especially decoherence and entanglement, as our base case. We show how standard quantum concepts (superposition, unitarity, decoherence, entanglement, quantum mutual information) can be mapped into GUFT’s invariants and without modifying the physics. We then extend this mapping to cognitive, organizational, monetary, algorithmic, and linguistic domains, using GUFT as a lattice overlay on the forest of domain-specific logic trees: we do not climb every tree; instead, we traverse the lattice of coherence measures and work backwards to bridge into each domain’s existing theory. We also flip the direction of travel: starting from empirical conclusions in linguistics and cognition (e.g., Chomskyan universal grammar, quantum cognition, vector semantics), we map them into GUFT and then infer when quantum-like mathematical architectures (Hilbert spaces, non-commutative probabilities) are likely to be fruitful models. COSO-style matrices make these mappings legible to governance and risk frameworks. Finally, we stress-test GUFT and ΔSyn on extreme domains—zero-point energy and “divine intelligence” to clarify what this framework legitimately supports (structural analogy, disciplined question-generation) and what it does not (violations of the second law, metaphysical claims). The result is a disciplined way to talk about coherence, decoherence, and entropy across fields, and a clearer articulation of how ΔSyn links structural coherence to normative questions in ethics, labour, AI, and universal linguistics.
Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-based architecture. These advancements have sparked growing calls to develop tests not only for intelligence but also for consciousness. However, existing benchmarks assess reasoning abilities across various domains but fail to directly address consciousness. To bridge this gap, this paper introduces the functional contextual N -Frame model, a novel framework integrating predictive coding, quantum Bayesian (QBism), and evolutionary dynamics. This comprehensive model explicates how conscious observers, whether human or artificial, should update beliefs and interact within a quantum cognitive system. It provides a dynamic account of belief evolution through the interplay of internal observer states and external stimuli. By modeling decision-making fallacies such as the conjunction fallacy and conscious intent collapse experiments within this quantum probabilistic framework, the N -Frame model establishes structural and functional equivalence between cognitive processes identified within these experiments and traditional quantum mechanics (QM). It is hypothesized that consciousness serves as an active participant in wavefunction collapse (or actualization of the physical definite states we see), bridging quantum potentiality and classical outcomes via internal observer states and contextual interactions via a self-referential loop. This framework formalizes decision-making processes within a Hilbert space, mapping cognitive states to quantum operators and contextual dependencies, and demonstrates structural and functional equivalence between cognitive and quantum systems in order to address the measurement problem. Furthermore, the model extends to testable predictions about AI consciousness by specifying informational boundaries, contextual parameters, and a conscious-time dimension derived from Anti-de Sitter/Conformal Field Theory correspondence (AdS/CFT). This paper theorizes that human cognitive biases reflect adaptive, evolutionarily stable strategies that optimize predictive accuracy (i.e., evolved quantum heuristic strategies rather than errors relative to classical rationality) under uncertainty within a quantum framework, challenging the classical interpretation of irrationality. The N -Frame model offers a unified account of consciousness, decision-making, behavior, and quantum mechanics, incorporating the idea of finding truth without proof (thus overcoming Gödelian uncertainty), insights from quantum probability theory (such as the Linda cognitive bias findings), and the possibility that consciousness can cause waveform collapse (or perturbation) accounting for the measurement problem. It proposes a process for conscious time and branching worldlines to explain subjective experiences of time flow and conscious free will. These theoretical advancements provide a foundation for interdisciplinary exploration into consciousness, cognition, and quantum systems, offering a path toward developing tests for AI consciousness and addressing the limitations of classical computation in representing conscious agency.