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Frontiers in Psychology

From quantum semantic states to quantum semantic dynamics: a multiscale theory of literary meaning evolution

Tzonelih Hwang

Published Oct 28, 2026• 0 citations
Publisher

Abstract

Literary interpretation changes as readers encounter new passages, answer interpretive questions, and revisit earlier material in light of later context. Quantum cognition provides a probability calculus for judgments whose probabilities depend on context and probe order, but the use of Hilbert-space notation alone does not establish nonclassical behavior. This article develops Quantum Semantic Dynamics (QSD) as a constrained quantum-probabilistic model and distinguishes three claim levels: quantum-inspired representation, quantum-probabilistic structure, and intrinsic nonclassicality. The present paper advances only the second. The confirmatory implementation restricts the local semantic state to a two-dimensional space, fixes probe geometry in an independent pilot sample, reconstructs a population-level 2 × 2 density state from three linearly independent binary probe bases, and gives the off-diagonal state components an operational meaning through cross-basis judgments. Contextual evolution is restricted to a one-parameter completely positive trace-preserving contraction toward a preregistered context-target state rather than an arbitrary latent transition. Two probes form the randomized A → B/B → A sequence used for the quantum question-order (QQ) diagnostic; the third is measured in an independent arm for state identification. The paper derives the QQ equality, states its assumptions, and adds an uncertainty-style consistency constraint for the frozen noncommuting probe geometry. QSD is compared with explicitly defined classical comparator classes ranging from a static commutative model to matched sequential latent-state models. Compatibility with QQ or the Hilbert representation is not treated as proof of intrinsic nonclassicality; such a claim would require a separate contextuality analysis against an explicitly defined classical representability criterion. The resulting architecture makes the mathematical status, empirical burden, and rejection conditions of QSD explicit before data collection.

DOI
10.3389/fpsyg.2026.1948062

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A quantum-cognitive approach to dynamic meaning construction

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Therefore, we propose a pathway toward Quantum-Embodied AI and photonic intelligent systems by incorporating quantum-semantic principles. These systems could mimic the non-algorithmic fluidity of the human mind. Quantum probability is not an exotic addition to linguistics but a fundamental requirement for describing dynamic meaning. The research will first analyze the evolution from gradient representations to quantum interference, then formally express the wave-particle duality of meaning using mathematical methods. We will then validate this theoretical framework through the "big fish" case study and neurophysiological evidence, concluding with an exploration of its practical implications for Generative AI and Photonic we need the "quantum we the evolution of semantic theory. We will focus on mathematical models often to capture the nature of meaning. 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cs.AICross-listedarXiv:2605.23943•May 5, 2026•0 citations

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Frontiers in Human Neuroscience•Jul 17, 2026•0 citations
Literary interpretation changes as readers encounter new passages, answer interpretive questions, and revisit earlier material in light of later context. Quantum cognition provides a probability calculus for judgments whose probabilities depend on context and probe order, but the use of Hilbert-space notation alone does not establish nonclassical behavior. This article develops Quantum Semantic Dynamics (QSD) as a constrained quantum-probabilistic model and distinguishes three claim levels: quantum-inspired representation, quantum-probabilistic structure, and intrinsic nonclassicality. The present paper advances only the second. The confirmatory implementation restricts the local semantic state to a two-dimensional space, fixes probe geometry in an independent pilot sample, reconstructs a population-level 2 × 2 density state from three linearly independent binary probe bases, and gives the off-diagonal state components an operational meaning through cross-basis judgments. Contextual evolution is restricted to a one-parameter completely positive trace-preserving contraction toward a preregistered context-target state rather than an arbitrary latent transition. Two probes form the randomized A → B/B → A sequence used for the quantum question-order (QQ) diagnostic; the third is measured in an independent arm for state identification. The paper derives the QQ equality, states its assumptions, and adds an uncertainty-style consistency constraint for the frozen noncommuting probe geometry. QSD is compared with explicitly defined classical comparator classes ranging from a static commutative model to matched sequential latent-state models. Compatibility with QQ or the Hilbert representation is not treated as proof of intrinsic nonclassicality; such a claim would require a separate contextuality analysis against an explicitly defined classical representability criterion. The resulting architecture makes the mathematical status, empirical burden, and rejection conditions of QSD explicit before data collection.
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.
Language isn't just a rigid system of symbols. Instead, it's a living, embodied phenomenon, deeply intertwined with our physical experience and shaped by our interaction with the environment (Wang, 2019;Zhou & Luo, 2024). However, the dynamic nature of language brings a significant challenge to cognitive science: the well-known "stability-plasticity dilemma" (Grossberg, 1980). On one hand, for clear communication, meanings of words need to be stable and widely recognized, so everyone can understand them, no matter when or who speaks them. On the other hand, these meanings must also be flexible and adaptable in varying contexts. While traditional computational models, from early generative grammar to standard Bayesian approaches, have excelled at modeling these stable meanings, they often treat semantic ambiguity as "noise" that needs to be eliminated, rather than a valuable resource (Gärdenfors, 2014).Even with the significant "probabilistic turn" in cognitive science, which brought Bayesian models to handle uncertainty, most of these models still rely on classical probability theory. They assume the meaning of a concept is a pre-defined distribution over a set of fixed features. As Bruza and Cole (2005) pointed out, this dependence on classical set theory creates a major epistemological barrier because it treats semantic ambiguity as "noise" rather than a fundamental part of meaning construction. Though scholars have recently developed more complex tools, like Gradient Symbolic Representations (GSR), to model meanings as weighted mixtures (Smolensky et al., 2014;Mondal, 2024), these approaches are still limited by Kolmogorovian probability. They still follow the Law of Total Probability, which forces conflicting meanings to be simply added together and mixed. We argue that this basic "mixture" method isn't enough to describe or handle complex situations where meanings are incompatible or interfere with each other in context. Therefore, much empirical evidence suggests that capturing these dynamic features requires a non-classical, quantum probability framework (Surov et al., 2021).The importance of this paradigm shift becomes most clear when we analyze how everyday language works and how we interpret deep meanings in complex literary works. A classic example of such "semantic superposition" is the iconic "big fish" in Ernest Hemingway's The Old Man and the Sea.Within the novel's narrative structure, this phrase isn't a static label. Instead, it operates simultaneously on multiple, even mutually exclusive, semantic levels. Here, it serves as a biological marlin, a worthy adversary, and a transcendent symbol of life's ultimate tragedy. A classical probabilistic model fails to capture the dynamic tension that a reader feels, because it forces these meanings to compete for probability mass, implying only one can be dominant. In contrast, human reading suggests that meaning exists in a "superposition" state. It stays that way until a specific context makes it "collapse" into a concrete interpretation. Crucially, these overlapping meanings aren't simple probabilistic blends. They are coherent "quantum states" within a complex adaptive system.This study proposes that Quantum Cognition offers the necessary mathematical formalism to resolve the "stability-plasticity dilemma". This is supported by its proven success in solving decision-making paradoxes in psychology (Busemeyer & Bruza, 2012;Widdows et al., 2023;Huang et al., 2025). We introduce an integrated quantum theoretical model. In this model, the interaction between embodied experience and linguistic context is characterized as a genuine quantum interference phenomenon.This framework reinterprets the tension between stability and plasticity through the lens of Wave-Particle Duality. In our model, the "particle" corresponds to the stable, discrete symbols used for communication. The "wave" captures the fluid, context-sensitive potential that allows for creative interpretation.Next, by employing the mathematical formalism of Hilbert space, we will mathematically demonstrate how semantic ambiguity can be maintained as a useful resource, rather than mere noise.This approach effectively overcomes the limitations inherent in traditional methods like static vectors and gradient symbolic mixtures. To ground these abstract formalizations, we focus on the "Big Fish" motif in Hemingway's The Old Man and the Sea. Through this case analysis, we will reveal how meaning dynamically evolves, similar to "state vector collapse". Our study also extends to address the fundamental limitations of current Artificial Intelligence, particularly Large Language Models (LLMs). We argue that current LLMs, relying heavily on static statistical correlations, lack the "grounding" for true understanding. Therefore, we propose a pathway toward Quantum-Embodied AI and photonic intelligent systems by incorporating quantum-semantic principles. These systems could mimic the non-algorithmic fluidity of the human mind. Quantum probability is not an exotic addition to linguistics but a fundamental requirement for describing dynamic meaning. The research will first analyze the evolution from gradient representations to quantum interference, then formally express the wave-particle duality of meaning using mathematical methods. We will then validate this theoretical framework through the "big fish" case study and neurophysiological evidence, concluding with an exploration of its practical implications for Generative AI and Photonic we need the "quantum we the evolution of semantic theory. We will focus on mathematical models often to capture the nature of meaning. 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This article develops a quantum-compatible formalisation of minimal semiosis as the foundational organisational condition for a theory of consciousness grounded in triadic normativity and should be read as a direct extension of the Semiotic Theory of Consciousness previously formulated. In that earlier framework, a system counts as a minimal interpreter just in case it satisfies operational criteria C1–C4 (plurality and conflict of signals, manipulable interpretant, explicit normativity and sensori-motor closure), supports a stream of Elementary Semiotic Events (ESEs) with rate ρ, and realises a basal consciousness magnitude for some system-specific scaling parameter κ. Conscious experience is thereby understood as the phenomenological halo coextensive with normatively constrained interpretive dynamics in an Umwelt-indexed state space. The present contribution supplies a quantum-compatible logical–mathematical envelope for this semiotic core. It models the space of interpretive states as a structured domain on which families of contextual partitions induce non-Boolean event algebras and, in appropriate cases, non-commutative operator structures. Instead of postulating genuine quantum microphysics, the proposal adopts quantum probability and Hilbert-space techniques purely as a modelling grammar for contextuality, order sensitivity, non-commutativity and systematic violations of classical total probability—features independently observed in biological and cognitive systems. Drawing on Atmanspacher’s and beim Graben’s theory of contextual emergence, together with quantum-like models of cognition, the article shows how non-generating partitions of the semiotic state space give rise to complementary macro-descriptions of meaning, whereas generating partitions recover a classical, Kolmogorov-like regime. Within this architecture, the central operator of the semiotic theory—the intervention do(I)—is rendered as a contextual state-selection map acting on probability measures (or state vectors) over interpretive configurations, thus linking causal intervention with the algebraic structure of contextuality. The resulting hybrid framework assigns semiosis the role of ontology and quantum-compatible probability the role of mathematics, enhancing explanatory and predictive power without additional metaphysical commitments about quantum substrates. The article concludes by specifying empirical conditions—concerning the identification of ESEs, estimation of ρ and κ, and experimental implementations of do(I) in organisms and robots—under which such quantum-compatible models can advance the study of minimal consciousness in both biological and artificial systems.
Quantum cognition often explains order effects, contextuality, and violations of the law of total probability by replacing classical probability with quantum probability on a fixed event structure. This paper proposes a different interpretation: quantum probability is the fixed-spacetime projection of contextual spacetime formation under finite-state requirements. The framework begins not with time, space, objects, or probabilities, but with requirements such as finite representational capacity, single-state semantic stability, context-sensitive intervention, avoidance of explicit context labels, coherent world-formation, and intersubjective transformability. When these requirements cannot be realized within a single global Boolean event structure, the mismatch appears, under fixed-spacetime projection, as noncommutativity, interference, and quantum-like probability. Building on prior single-state approaches to contextuality, we reinterpret classical contextual bookkeeping cost as the fixed-spacetime shadow of contextual spacetime formation. Auxiliary memory or context labels in a classical representation correspond, in this account, to holonomy-like mismatch among locally Boolean logic-worlds. The interference term is the cross term generated when locally classical realization contributions are nontrivially glued and projected back into a fixed classical spacetime form. The result is a transcendental-operational realist account: objecthood, eventhood, probability, and spacetime are treated as forms of realization under requirements, while objectivity is defined by invariants preserved across observer- and history-dependent spacetime formations.
This deposit contains two companion technical notes developing a finite-dimensional semantic state algebra for unresolved readings, classical decisions, and order-sensitive semantic operations. The first note separates semantic states from their response formats. A response format is defined as an alphabet together with a prescribed resolution map. For a two-reading construction, four selector-polarity states are distinguished explicitly. The polarity-only yes/no map is proven non-injective, whereas the full selector-polarity map is bijective. A separate rank-nullity theorem establishes the corresponding global obstruction for linear maps from higher-dimensional semantic spaces into lower-dimensional decision spaces. A real two-dimensional space is sufficient for storage; under the additional requirements of associative and commutative multiplication, a unit, and division by every nonzero element, the unique two-dimensional real extension is the complex field. The classical real sector is embedded conservatively. The second note extends this framework to semantic operators. Questions and contextual updates generate a unital complex operator algebra, while order dependence is represented exactly by commutators and the associated derived Lie algebra. State-dependent and global order-sensitivity measures are introduced. Complex dimension two is shown to be minimal for noncommuting complex-linear dynamics, while terminal binary decisions remain separate real-valued readouts. Operator complexification preserves sums, composition, identities, and commutators. The construction is structural rather than empirical. It assumes neither quantum ontology nor a Born rule and does not claim a complete formal semantics of natural language.
Cognitive neuroscience has accumulated robust findings (e.g., order effects in judgment, multi-path motor preparation, perceptual binding, attentional selection, and the recursive construction of self and time) that systematically resist explanation within classical probabilistic and causal frameworks. We argue that these are not anomalies but signatures of a deeper, non-commutative architecture of cognition and brain dynamics. We propose quantum probability theory together with contextual probability theory, not as metaphorical analogies but as rigorous translational languages for cognitive processes in which observation actively transforms underlying state spaces. Underlying this framework is the conjecture that neural network dynamics in the brain are intrinsically organized to generate quantum-like representations-rather than merely being described by quantum mathematics from the outside. Their structural primitives (i.e., superposition, entanglement, projection, and non-commutativity) map onto premotor population coding, long-range cortical synchrony, prefrontal state dynamics, and default-mode network activity. On this basis we sketch a new sub-domain (namely, Cognitive Structural Science) that treats the geometry and algebra of cognitive state spaces as primary explananda, and outline a research program combining homologous human-macaque experiments with quantum-computer simulation as a constrained testbed.