This is the project associated with the Full Day Tutorial on Quantum Models of Cognition and Decision from the 38th Annual Conference of the Cognitive Science Society (CogSci 2016). The tutorial is an exposition of a rapidly growing new alternative approach to building computational models of cognition based on quantum theory. The cognitive revolution that occurred in the 1960’s was based on classical computational logic, and the connectionist/neural network movements of the 1970’s were based on classical dynamical systems. These classical assumptions remain at the heart of both cognitive architecture and neural network theories, and they are so commonly and widely applied that we take them for granted. Quantum theory provides a different approach to logic, reasoning, probabilistic inference, and dynamical systems. For example, quantum logic does not follow the distributive axiom of Boolean logic; quantum probabilities do not obey the disjunctive axiom of Kolmogorov probability; quantum reasoning does not obey the principle of monotonic reasoning. It turns out that humans do not obey these restrictions either, which is why we consider a quantum approach.
This is the project associated with the Full Day Tutorial on Quantum Models of Cognition and Decision from the 38th Annual Conference of the Cognitive Science Society (CogSci 2016). The tutorial is an exposition of a rapidly growing new alternative approach to building computational models of cognition based on quantum theory. The cognitive revolution that occurred in the 1960’s was based on classical computational logic, and the connectionist/neural network movements of the 1970’s were based on classical dynamical systems. These classical assumptions remain at the heart of both cognitive architecture and neural network theories, and they are so commonly and widely applied that we take them for granted. Quantum theory provides a different approach to logic, reasoning, probabilistic inference, and dynamical systems. For example, quantum logic does not follow the distributive axiom of Boolean logic; quantum probabilities do not obey the disjunctive axiom of Kolmogorov probability; quantum reasoning does not obey the principle of monotonic reasoning. It turns out that humans do not obey these restrictions either, which is why we consider a quantum approach.
Quantum cognition, a growing topic at the interface of quantum physics and cognitive research, provides a new viewpoint on the intricacies of human thought processes. In contrast to classic Boolean algebraic models, which have long dominated cognitive psychology, quantum cognition proposes a new framework influenced by quantum mechanics' algebraic structure. This deviation derives from the understanding that natural notions, based on prototypes, are more closely related to convex sets than to the rigid rules of Boolean algebra. Drawing on the fundamental work of luminaries such as John von Neumann, quantum cognition reveals a unique probability theory based on ortho-algebras, challenging traditional probabilistic reasoning based on Boolean algebras. This change opens up solutions to complex challenges in rationality, logical thinking, and probabilistic reasoning, ushering in a new era of cognitive modelling. Several arguments in the literature support the importance of quantum cognition in explaining cognitive phenomena.
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.
Artificial intelligence has long sought to mirror human intelligence through computational models of perception, learning, and reasoning. In parallel, cognitive science has shown that human judgment frequently deviates from classical logic and probability theory. This research-in-progress investigates how quantum-probabilistic models can be implemented as quantum circuits and examines the stability of such circuits, using the conjunction fallacy as a test case. Responses are compared across human behavioural data, Monte Carlo simulations, quantum simulations, and physical quantum hardware, explicitly testing how interference-driven reasoning patterns persist under controlled stochastic and decoherence-induced noise. This comparison provides insight into how cognitive probability structures are preserved—or distorted—across classical and quantum computational implementations. The paper demonstrates a proof-of-concept pipeline for encoding cognitively motivated probability structures in computational models and evaluating their behaviour under simulation and hardware noise, with implications for future decision support system design.
Quantum cognition research applies abstract, mathematical principles of quantum theory to inquiries in cognitive science. It differs fundamentally from alternative speculations about quantum brain processes. This topic presents new developments within this research program. In the introduction to this topic, we try to answer three questions: Why apply quantum concepts to human cognition? How is quantum cognitive modeling different from traditional cognitive modeling? What cognitive processes have been modeled using a quantum account? In addition, a brief introduction to quantum probability theory and a concrete example is provided to illustrate how a quantum cognitive model can be developed to explain paradoxical empirical findings in psychological literature.
Quantum physics is perhaps the most successful scientific achievement in the history of theoretical science. It was created to explain puzzling facts that could not be interpreted using classical physical theory, and it did so by proposing an entirely new set of revolutionary concepts. The earlier classical physical theory is currently regarded as a subset of the more general quantum theory. In the process of constructing quantum mechanics, physicists created a new theory of probabilistic and dynamic systems that is more general than the previous classic theory. This book is not about quantum physics – it focuses on the application of the probabilistic dynamic system generated by quantum theory to a new field: cognition and legal decision-making. Almost all prior cognitive and decision science modelling relied on assumptions derived from classical probability dynamic systems. However, these fields have also encountered peculiar findings that are inexplicable within this limited framework. Quantum notions may provide some solutions. In this book, we examine the reasons why and show how these concepts can be applied to the fields of cognition and legal decision-making.
Quantum theory has inspired many new ideas for understanding Cognition and decision making. Given the nascent state of this field, many new challenges still remain. What are the strengths and unique contributions of these ideas? What are likely next steps in its development? How can it be applied to practical problems in cognitive science? Is this a rational or an irrational system of reasoning? What, if any, are its connections to neuroscience? Should the hard problem of consciousness be considered? These are questions that we try to address in this chapter, albeit at times in a limited way. We end with a debate with a fictitious skeptic (actually previous reviewers) of these ideas. Let us start by reviewing what we have covered so far. Contributions to cognition and decision We begin by summarizing the conclusions reached in previous chapters regarding the usefulness of quantum theory for modelling phenomena in cognition and decision. Chapter 1 provided some motivation and Chapter 2 provided a theoretical introduction to quantum theory. Chapter 3 presented a quantum model to account for question order effects on survey research. An exact and parameter-free test was derived from the model, which produced surprisingly accurate predictions for results obtained from three different large Gallup poll survey studies. Chapter 4 described a quantum model of probability judgments to account for findings that produce violations of classic probability theory, including conjunction and disjunction fallacies and order effects on inference.