In this article, a novel approach to cognition inspired by quantum mechanical principles is proposed. It is based on an analogy between the physical objects at the quantum level and the human's mental states. Specifically, symbols in various cognitive functions are to be treated as eigenstates with respect to a particular quantum experimental arrangement. The arrangement is associated with an operator (called a formulation) corresponding to an observable of quantum mechanics. A state of affairs is treated as a superposition of eigenstates. Based on these assumptions, the quantum mechanical formalism can be straightforwardly applied to cognitive processes. As in quantum mechanics, there is an analogous Uncertainty Principle in representation. Furthermore, it is shown that non-monotonicity and counterfactual conditionals can be accommodated in a quantum mechanical framework.
In this article, a novel approach to cognition inspired by quantum mechanical principles is proposed. It is based on an analogy between the physical objects at the quantum level and the human's mental states. Specifically, symbols in various cognitive functions are to be treated as eigenstates with respect to a particular quantum experimental arrangement. The arrangement is associated with an operator (called a formulation) corresponding to an observable of quantum mechanics. A state of affairs is treated as a superposition of eigenstates. Based on these assumptions, the quantum mechanical formalism can be straightforwardly applied to cognitive processes. As in quantum mechanics, there is an analogous Uncertainty Principle in representation. Furthermore, it is shown that non-monotonicity and counterfactual conditionals can be accommodated in a quantum mechanical framework.
The emergence of quantum computing has prompted researchers to examine potential parallels between quantum mechanical principles and human cognitive processes. This article explores the theoretical framework of quantum cognition, examining how concepts from quantum mechanics, including superposition, entanglement, and measurement, might inform our understanding of human decision-making, memory, and consciousness. While quantum computing operates on fundamentally different physical principles than biological neural networks, the mathematical formalisms of quantum mechanics offer novel perspectives on cognitive phenomena that classical probability theory struggles to explain. This review critically examines the current state of quantum cognition research, its applications in computational modeling, and the ongoing debates regarding the validity of quantum mechanical analogies in psychological theory. The analysis reveals both promising theoretical insights and significant limitations in applying quantum principles to human cognition, highlighting the need for empirical validation and careful distinction between mathematical analogy and physical reality.
A number of modern scientists adheres to the point of view that, despite the fact that quantum mechanics does not allow to explain the existence of mental phenomena in the physical world, with the help of its inherent ontological principles and mathematical formalism, it is possible to understand the functioning of consciousness and the unconscious. Based on the analogy between quantum and mental processes, the unconscious can be represented as a superposition of mental states, and consciousness — in the form of decoherence of unconscious experiences. From an evolutionary point of view, consciousness can be considered as a consequence of the accumulation of a sufficient amount of information during the interaction of the organism with the surrounding environment, when the transition from the quantum state of the unconscious to the classical state of consciousness is carried out at the psychic level of being, by analogy with the accumulation of a sufficient amount of information by a quantum system during interaction with the environment, when at the physical level of being the collapse of the wave function. In psychotherapy and psychodiagnostics, one can see an analogy with the measurement process in quantum mechanics and a quantum leap from one mental state to another. However, the analysis of this point of view carried out in the article shows that the parallels between quantum mechanics and psychology today look insufficiently substantiated, since the mathematical model of the psyche, which allows describing human behavior on the basis of the laws of quantum mechanics, has not yet been built, which is why, firstly, the use of quantum-physical terminology in the understanding of mental experience does not rise to the level of scientific theory, remaining a metaphor, and, secondly, quantum psychology has a predictive potential, in contrast to the psychology of the classical type.
This paper explores the potential of leveraging principles from quantum mechanics to design novel cognitive architectures. Traditional cognitive architectures often struggle with representing uncertainty, exploring multiple possibilities simultaneously, and efficiently integrating diverse cognitive processes. Inspired by concepts like superposition, entanglement, and quantum decoherence, this research proposes a framework for building architectures that more closely resemble the computational capabilities of the quantum world. The core idea is to move beyond simply utilizing quantum computers as hardware accelerators and instead, to use quantum *principles* to fundamentally redesign how cognitive systems are structured and operate. We investigate how superposition can be embodied through dynamically switching between multiple cognitive states, and how entanglement can be translated into strong correlations between different cognitive modules. The architecture presented here aims to address limitations in existing architectures by providing a more flexible, robust, and potentially more efficient approach to cognitive modeling and artificial intelligence. This work highlights the significant potential for quantum-inspired approaches to revolutionize cognitive architecture design.
Experiments in cognitive science and decision theory show that the ways in which people combine concepts and make decisions cannot be described by classical logic and probability theory. This has serious implications for applied disciplines such as information retrieval, artificial intelligence and robotics. Inspired by a mathematical formalism that generalizes quantum mechanics the authors have constructed a contextual framework for both concept representation and decision making, together with quantum models that are in strong alignment with experimental data. The results can be interpreted by assuming the existence in human thought of a double-layered structure, a 'classical logical thought' and a 'quantum conceptual thought', the latter being responsible of the above paradoxes and nonclassical effects. The presence of a quantum structure in cognition is relevant, for it shows that quantum mechanics provides not only a useful modeling tool for experimental data but also supplies a structural model for human and artificial thought processes. This approach has strong connections with theories formalizing meaning, such as semantic analysis, and has also a deep impact on computer science, information retrieval and artificial intelligence. More specifically, the links with information retrieval are discussed in this paper.
The present article explores the modeling of human cognitive activity in response to external informational stimuli of varying modalities. The mathematical foundation of the proposed model is based on elements of the mathematical apparatus of quantum mechanics, the theory of information images/representations, as well as a new approach to the description of information objects using. The theory of informational images/representations is employed to describe the mechanisms of information transmission, processing, and generation. A brief overview of this theoretical framework is provided in the article. The cognitive activity under investigation is modeled using a one-dimensional potential well with walls of differing heights. Within this interpretation, the internal barrier symbolizes the boundary between the conscious and subconscious levels of the mind. The proposed theoretical approach was then utilized to parametrize the studied cognitive systems. A mathematical model was subsequently formulated, using the principles of self-oscillatory quantum mechanics, leading to the formulation of an equation that describes the state function of an informational image during cognitive processes. In order to investigate various scenarios of informational influence, computer-based simulations were conducted. Consequently, characteristic patterns were identified, including the oscillatory nature of changes in the state function of informational images and the emergence of distinct threshold effects. These findings are in agreement with existing experimental data.
ABSTRACT Recent advances at the intersection of quantum physics, neuroscience, and cognitive science have renewed interest in whether quantum phenomena and quantum‐inspired formalisms can inform models of human cognition and decision‐making. While classical neural and computational models explain many aspects of perception and behavior, they face limitations in accounting for contextuality, order effects, probabilistic reasoning violations, ambiguity resolution, and rapid information integration. This review synthesizes evidence and theoretical frameworks linking quantum‐related principles to cognition, with emphasis on quantum sensing in biological systems, molecular and subcellular quantum‐relevant mechanisms, quantum‐to‐classical transition pathways, quantum‐inspired models of perception and decision‐making, and quantum‐inspired computational approaches. It also extends beyond the probabilistic arm of quantum cognition by considering emerging equilibrium‐based and causal‐logical theories, including mental equilibrium, brain‐universe similarity, mind‐light‐matter unity, quantum emergence, and quantum intelligence. Throughout, the review distinguishes physically instantiated quantum mechanisms from abstract quantum‐like formalisms that model contextuality, mental entanglement, and non‐separable cognitive dynamics without requiring sustained macroscopic quantum coherence in the brain. Finally, it identifies methodological challenges and future directions for advancing quantum cognitive science toward experimentally testable and biologically meaningful theories.