There are two basic theories of probability and two scientific perspectives – classical and quantum. In this chapter, assumptions of both the classical and the quantum probability theories will be presented and confronted. The rules for quantum probabilities differ from those of classical probabilities. In this chapter, we will describe what is the difference between the quantum cognitive modelling different from traditional cognitive modelling. In quantum probability theory (QPT) states of individual quantum, systems are called pure states, which are represented as vectors in a vector space (the Hilbert space). Properties of the Hilbert space and vectors’ operations will be explained. Two aspects of quantum theory open the door to addressing problems facing cognition and decision in a totally new light. The first is known as “contextuality” of judgements and decisions: the context generated by making a first judgement or decision interferes with subsequent judgements or decisions to produce order effects, so that judgements and decisions are non-commutative. The second aspect relates to quantum entanglement – a phenomenon whereby making an observation on one part of the system instantaneously affects the state in another part of the system, even if the respective systems are separated by space-like distances. This opens the door to developing quantum-like models of cognitive phenomena which are not decompositional in nature. Examples of cognitive processes modelled using the quantum account and results on the previous studies will be presented. We will also discuss the quantum–classical boundary.
There are two basic theories of probability and two scientific perspectives – classical and quantum. In this chapter, assumptions of both the classical and the quantum probability theories will be presented and confronted. The rules for quantum probabilities differ from those of classical probabilities. In this chapter, we will describe what is the difference between the quantum cognitive modelling different from traditional cognitive modelling. In quantum probability theory (QPT) states of individual quantum, systems are called pure states, which are represented as vectors in a vector space (the Hilbert space). Properties of the Hilbert space and vectors’ operations will be explained. Two aspects of quantum theory open the door to addressing problems facing cognition and decision in a totally new light. The first is known as “contextuality” of judgements and decisions: the context generated by making a first judgement or decision interferes with subsequent judgements or decisions to produce order effects, so that judgements and decisions are non-commutative. The second aspect relates to quantum entanglement – a phenomenon whereby making an observation on one part of the system instantaneously affects the state in another part of the system, even if the respective systems are separated by space-like distances. This opens the door to developing quantum-like models of cognitive phenomena which are not decompositional in nature. Examples of cognitive processes modelled using the quantum account and results on the previous studies will be presented. We will also discuss the quantum–classical boundary.
Growing empirical evidence reveals that traditional set-theoretic structures cannot in general be applied to cognitive phenomena. This has raised several problems, as illustrated, for example, by probability judgement errors and decision-making (DM) errors. We propose here a unified theoretical perspective which applies the mathematical formalism of quantum theory in Hilbert space to cognitive domains. In this perspective, judgements and decisions are described as intrinsically non-deterministic processes which involve a contextual interaction between a conceptual entity and the cognitive context surrounding it. When a given phenomenon is considered, the quantum-theoretic framework identifies entities, states, contexts, properties and outcome statistics, and applies the mathematical formalism of quantum theory to model the considered phenomenon. We explain how the quantum-theoretic framework works in a variety of judgement and decision situations where systematic and significant deviations from classicality occur.
Cognitive Science is the study of the mind and how it relates to human behaviour. As a field, it is highly interdisciplinary, bringing together theories from Psychology, Neuroscience, Biology, and others. The field of cognitive science operates at a number of levels. On the one hand, some aspects of cognitive science look at the behaviour of neurons, whereas other aspects look to explain human behaviour at a more abstract level, seeking to explain human decision making or reasoning. A key area of research in cognitive science is how to formalise human behaviours around judgements of similarity, categorization, and decision making. In the field of Physics, Quantum Mechanics has fundamentally altered our understanding of the way in which particles behave. Quantum mechanics has a number of unintuitive phenomena, some of which can be used model unusual aspects of human behaviour. The application of quantum theory to model human behaviour is wide-ranging. In this chapter we will look at three main areas in which it has been applied. One key area is in how similarity judgements can be modelled. There are a number of phenomena around similarity judgements that are not well modelled using a view of concepts that does not take the state of the observer into account. These include the asymmetry of similarity judgements and the fact that similarity can change depending on other exemplars that are present. Another key area is in judgement and decision-making. Again, puzzling phenomena have been observed regarding the fact that judgements do not follow classical probability theory, known as the conjunction fallacy, can depend crucially on the order in which questions are presented or on the amount of knowledge about the world. Finally, approaches to modelling cognitive phenomena at the neural level will also be discussed within a quantum-theoretic framework.
Contemporary discussions of the gap between natural and artificial intelligence often emphasize human capacities such as contextual reasoning, cognitive flexibility, and non-classical decision-making. This paper proposes that quantum and quantum-like models of cognition and decision processes offer a principled framework for addressing these differences. A growing body of empirical evidence shows that human reasoning systematically violates the assumptions of classical probability and logic, exhibiting contextuality, order effects, interference phenomena, and task incompatibility. Quantum probability theory and related quantum-like formalisms provide mathematically rigorous tools—based on Hilbert spaces, superposition, and entanglement—that capture these features more naturally than classical models. While quantum and quantum-like approaches share a common mathematical structure, they differ in physical implementation, motivating two complementary directions in artificial intelligence: quantum AI and quantum-like AI. Together, these approaches suggest a viable pathway toward narrowing, and potentially bridging, the divide between natural and artificial intelligence by grounding AI architectures in models aligned with the structure of human cognition.
Purpose: Decision-making under uncertainty remains a foundational challenge in cognitive science and artificial intelligence. Classical Bayesian Probability Models (CBM) often fail to explain paradoxical cognitive behaviors such as order effects, ambiguity aversion, and context-dependent reasoning. This study seeks to compare Quantum Probability Theory (QPT) and Classical Bayesian Models in their ability to capture the dynamics of human decision-making. It aims to determine which framework more accurately reflects the cognitive mechanisms underlying reasoning under uncertainty. Methodology: A qualitative, exploratory research design was adopted, involving in-depth semi-structured interviews with 16 experts across psychology, philosophy, artificial intelligence, and cognitive neuroscience. Participants were purposively selected for their theoretical and empirical expertise in probabilistic reasoning. Data were analyzed using reflexive thematic analysis, guided by the Dual-Process Theory and Busemeyer’s Quantum Cognition framework. The analysis emphasized participants’ perspectives on theoretical assumptions, cognitive plausibility, and predictive utility between QPT and CBM paradigms. Findings: Thematic findings reveal that Quantum Probability Theory offers superior explanatory power in contexts involving cognitive ambiguity, contextual dependence, and non-commutativity of mental operations. Participants consistently reported that QPT better models real-world reasoning tasks where classical logic collapses, capturing the fluid and context-sensitive nature of human judgment. Conversely, while CBM remains effective in structured, low-uncertainty scenarios, it fails to accommodate superposition and interference effects inherent in human cognition. Unique Contribution to Theory, Practice, and Policy (Recommendations): The study contributes theoretically by demonstrating how quantum probabilistic models expand existing theories of bounded rationality and probabilistic reasoning in cognitive science. Practically, it encourages interdisciplinary collaboration between cognitive scientists, AI researchers, and philosophers to refine decision models that mirror human intuition more closely. Policy-wise, the findings support the integration of quantum-inspired approaches in the design of intelligent decision-support systems and cognitive architectures. The study recommends continued empirical validation of QPT within applied domains—such as behavioral economics, machine learning, and cognitive modeling—to strengthen its predictive and explanatory robustness.
We characterize the class of quantum measurements that matches the applications of quantum theory to cognition (and decision-making)—quantum-like modelling. Projective measurements describe the canonical measurements of the basic observables of quantum physics. However, the combinations of the basic cognitive effects, such as the question order and response replicability effects (RREs), cannot be described by projective measurements. We motivate the use of the special class of quantum measurements, namely, sharp repeatable non-projective measurements — S R P ¯ . This class is practically unused in quantum physics. Thus, physics and cognition explore different parts of quantum measurement theory. Quantum-like modelling is not the automatic borrowing of the quantum formalism. Exploring the class S R P ¯ highlights the role of non-commutativity of the state-update maps generated by measurement back action . Thus, ‘non-classicality’ in quantum physics as well as quantum-like modelling for cognition is based on two different types of non-commutativity, of operators (observables) and instruments (state-update maps): observable non-commutativity versus state-update-non-commutativity . We speculate that distinguishing quantum-like properties of the cognitive effects is the expression of the latter, or possibly both. This article is part of the theme issue ‘Quantum theory and topology in models of decision making (Part 1)’.
FINDING: Quantum cognition applies Hilbert-space probability (complex amplitudes) to model human decision-making under uncertainty and context effects, replacing classical Kolmogorov probability with non-commutative operator logic. | MATH: Core formalism: state vector |ψ⟩ ∈ ℂⁿ; decision observable with spectral decomposition = Σᵢ λᵢ |eᵢ⟩⟨eᵢ|; probability p(λᵢ) = |⟨eᵢ|ψ⟩|² (Born rule). Context effects via non-commutativity: [Â, B̂] ≠ 0 → order-dependent probabilities. Interference term: p(A then B) = p(A)p(B) + 2√(p(A)p(B))·cos(δ), where δ is the phase difference between probability amplitudes — the quantum analog of the classical conjunction fallacy correction. | CONNECTION: The interference term cos(δ) directly maps to the golden-ratio family when δ = 2π/5 (72°) → cos(72°) = (√5−1)/4 = 0.309; δ = π/5 (36°) → cos(36°) = φ/2 = 0.809; δ = 2π/10 → cos(36°) again. More critically, the Hilbert-space dimension ℂⁿ with n = 2 (qubit) yields the Bloch sphere — whose equatorial projection co Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com