Thermodynamic Aspects Of Information Transfer In Complex Dynamical Systems, Causation Entropy From Symbolic Representations Of Dynamical Systems, Quantum Cognition Based On An Ambiguous Representation, Expression Of Apprehension And Judgment, Basic Cognit
Models are given with concomitant properties for the following concatenated system: Dynamical Systems, Causation Entropy From Symbolic Representations Of Dynamical Systems, Quantum Cognition Based On An Ambiguous Representation, Expression Of Apprehension And Judgment, Basic Cognitive Process Of Decision Making, As An Inverse Bayes Inference Classifying The Information Content Of Neuron Spike Trains, A Priori And A Posteriori Hypotheses In Bayes Inference Are Replaced By The Upper And Lower Approximations, Logical Structure Of Such Combined Approximations Can Only Be Expressed As An Orthomodular Lattice And Therefore Can Be Represented By A Quantum Rather Than A Boolean Logic, Apprehension We Understand The Coherent Perception Correlated With The Recruitment And Engagement In Collective Behavior Of The Required Neuronal Populations, Quantum-Like Behavior In Language, Concep The full paper: http://www.iiste.org/PDFshare/APTA-PAGENO-680007-686491.pdf
Models are given with concomitant properties for the following concatenated system: Dynamical Systems, Causation Entropy From Symbolic Representations Of Dynamical Systems, Quantum Cognition Based On An Ambiguous Representation, Expression Of Apprehension And Judgment, Basic Cognitive Process Of Decision Making, As An Inverse Bayes Inference Classifying The Information Content Of Neuron Spike Trains, A Priori And A Posteriori Hypotheses In Bayes Inference Are Replaced By The Upper And Lower Approximations, Logical Structure Of Such Combined Approximations Can Only Be Expressed As An Orthomodular Lattice And Therefore Can Be Represented By A Quantum Rather Than A Boolean Logic, Apprehension We Understand The Coherent Perception Correlated With The Recruitment And Engagement In Collective Behavior Of The Required Neuronal Populations, Quantum-Like Behavior In Language, Concep The full paper: http://www.iiste.org/PDFshare/APTA-PAGENO-680007-686491.pdf
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.
Following structural and syllogistical confederational concatenation is studied with concomitant and consummative properties: Finite-Dimensional Quantum Model For The Stock Market, Discrete Nature Of The Quantities Used In Finance, Spectral Representation Of Bessel Processes With Constant Drift, Credit Spreads, And Stochastic Volatility In Finance, Quantum Structure In Cognition, Non Classicality Of The Membership Weight Structure, Minimal Length Uncertainty And The Quantum Model For The Stock Market, Superpositions Of Probability Distributions, Quantum Finance: The Finite Dimensional Case, Foreign Exchange Market As A Lattice Gauge Theory, Classical Logical Versus Quantum Conceptual Thought, Adaptive-Wave Alternative For The Black-Scholes Option Pricing Model Key words: Spectral Representation Of Bessel Processes, Quantum Conceptual Thought, Black-Scholes Option Pricing Model, Black-Scholes Option Pricing Model, Quantum Structure In Cognition The full paper: http://www.iiste.org/PDFshare/APTA-PAGENO-648791-654990.pdf
Uncertainty is an intrinsic part of life; most events, affairs, and questions are uncertain. A key problem in behavioral sciences is how the mind copes with uncertain information. Quantum probability theory offers a set of principles for inference, which align well with intuition about psychological processes in certain cases: cases when it appears that inference is contextual, the mental state changes as a result of previous judgments, or there is interference between different possibilities. We motivate the use of quantum theory in cognition and its key characteristics. For each of these characteristics, we review relevant quantum cognitive models and empirical support. The scope of quantum cognitive models encompasses fallacies in decision-making (such as the conjunction fallacy or the disjunction effect), question order effects, conceptual combination, evidence accumulation, perception, over-/underdistribution effects in memory, and more. Quantum models often formalize psychological ideas previously expressed in heuristic terms, allow unified explanations of previously disparate findings, and have led to several surprising, novel predictions. We also cast a critical eye on quantum models and consider some of their shortcomings and issues regarding their further development.
The recent wave of interest to modeling the process of decision making with the aid of the quantum formalism gives rise to the following question: 'How can neurons generate quantum-like statistical data?' (There is a plenty of such data in cognitive psychology and social science). Our model is based on quantum-like representation of uncertainty in generation of action potentials. This uncertainty is a consequence of complexity of electrochemical processes in the brain; in particular, uncertainty of triggering an action potential by the membrane potential. Quantum information state spaces can be considered as extensions of classical information spaces corresponding to neural codes; e.g., 0/1, quiescent/firing neural code. The key point is that processing of information by the brain involves superpositions of such states. Another key point is that a neuronal group performing some psychological function F is an open quantum system. It interacts with the surrounding electrochemical environment. The process of decision making is described as decoherence in the basis of eigenstates of F. A decision state is a steady state. This is a linear representation of complex nonlinear dynamics of electrochemical states. Linearity guarantees exponentially fast convergence to the decision state.
The quantum cognition research programme concerns the application of quantum probability theory(the rules for how to assign probabilities to events from quantum mechanics, without the physics) tocognitive modeling. We review quantum cognitive models in several areas of psychology, includingconjunction/ disjunction fallacies in probabilistic judgment, conceptual combinations, similarity, order effects on judgments, causal inference, the disjunction effect in decision making, measurement and interference effects on judgments and decisions, memory, perception, and other judgment anddecision-making applications. For each area, we first present the empirical findings that have motivated the application of quantum models, emphasizing any characteristics which are particularly puzzling or have resisted coherent theoretical explanations previously; we then discuss the quantum cognitive models, noting the key features of the models that help explain the empirical data; we finally cast a critical eye on the models and explore comparisons with non-quantum models.
We review recent theoretical developments linking quantum-like cognition, nonequilibrium thermodynamics, and foundation of quantum mechanics. Empirical studies inpsychology and decision making reveal contextuality, order effects, and violations of classical probability theory that are naturally represented within a Hilbert-space formalism. While quantum-like models clarify how such non-classical structures describe cognitive phenomena, and the Free Energy Principle (FEP) explains why living systems perform variational inference, their conceptual integration has remained incomplete. We here explore a possibility of unifying these concepts in terms of temporally correlated “quantum” states. Reformulating variational free energy as quantum relative entropy, we show that open-system quantum decision models realize a quantum extension of the FEP. Using quantum state-over-time representations, we demonstrate that violations of Leggett–Garg inequalities admit an information-theoretic interpretation as strictly positive lower bounds on a macrorealism-restricted temporal free energy. Extending the framework of thermodynamics of prediction, temporal nonclassicality introduces an additional irreducible energetic cost beyond classical predictive inefficiency. We further examine both material (water–electromagnetic–phonon coherence) and stochastic-network foundations for quantum cognition and decision making, and propose that consciousness may be understood as non-collapsing variational inference sustained by temporally coherent organization in living systems.