I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory-optical illusion perception and group decision making-where individuals are treated as quantum-mechanical agents whose choices shift through context-dependent transitions rather than simple probabilities. I show that, when networked together, these units form a quantum-cognitive neural system that reproduces familiar collective and perceptual phenomena while naturally accommodating counterintuitive processes that challenge classical models. Bridging ideas from quantum cognition theory and neural networks, this approach offers a compact, physically grounded way to describe how real individuals and groups think, perceive and decide.
I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory-optical illusion perception and group decision making-where individuals are treated as quantum-mechanical agents whose choices shift through context-dependent transitions rather than simple probabilities. I show that, when networked together, these units form a quantum-cognitive neural system that reproduces familiar collective and perceptual phenomena while naturally accommodating counterintuitive processes that challenge classical models. Bridging ideas from quantum cognition theory and neural networks, this approach offers a compact, physically grounded way to describe how real individuals and groups think, perceive and decide.
I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory -- optical illusion perception and group decision making -- where individuals are treated as quantum-mechanical agents whose choices shift through context-dependent transitions rather than simple probabilities. I show that, when networked together, these units form a quantum-cognitive neural system that reproduces familiar collective and perceptual phenomena while naturally accommodating counterintuitive processes that challenge classical models. Bridging ideas from quantum cognition theory and neural networks, this approach offers a compact, physically grounded way to describe how real individuals and groups think, perceive and decide.
On the surface, behavioural science and physics seem to be two disparate fields of research. However, a closer examination of problems solved by them reveals that they are uniquely related to one another. Exemplified by the theories of quantum mind, cognition and decision-making, this unique relationship serves as the topic of this chapter. Surveying the current academic journal papers and scholarly monographs, we present an alternative vision of the role of quantum mechanics in the modern studies of human perception, behaviour and decision-making. To that end, we mostly aim to answer the 'how' question, deliberately avoiding complex mathematical concepts but developing a technically simple computational code that the readers can modify to design their own quantum-inspired models. We also present several practical examples of the application of the computation code and outline several plausible scenarios, where quantum models based on the proposed do-it-yourself model kit can help understand the differences between the behaviour of individuals and social groups.
At first glance, quantum mechanics and behavioural science seem worlds apart -- one rooted in equations and particles, the other in thoughts and choices. Yet, emerging research reveals a profound and unexpected bridge between them. This chapter explores that bridge through quantum models of cognition and decision-making, showing how principles from quantum mechanics can help understand, and even predict, the complexities of human perception, behaviour and societal dynamics. We introduce a computationally accessible framework grounded in quantum theory, designed to model ambiguity, bias and choice in a way that classical logic cannot. Drawing on interdisciplinary sources, we demonstrate how this approach not only enriches our understanding of individual cognition but also extends to AI, video game design, financial behaviour and collective decision-making in society. Through vivid case studies -- from optical illusions in video games to decision biases in economic, defence and political contexts -- we show how quantum models offer new explanatory and predictive power. Thus, this chapter invites readers from all professional backgrounds to reimagine cognition and decision-making, opening new avenues for science, technology and human understanding.
Contemporary machine learning (ML) systems excel in recognising and classifying images with remarkable accuracy. However, like many computer software systems, they can fail by generating confusing or erroneous outputs or by deferring to human operators to interpret the results and make final decisions. In this paper, we employ the recently proposed quantum tunnelling neural networks (QT-NNs) inspired by human brain processes alongside quantum cognition theory to classify image datasets while emulating human perception and judgment. Our findings suggest that the QT-NN model provides compelling evidence of its potential to replicate human-like decision-making. We also reveal that the QT-NN model can be trained up to 50 times faster than its classical counterpart.
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.
At first glance, quantum mechanics and behavioural science seem worlds apart---one rooted in equations and particles, the other in thoughts and choices. Yet, emerging research reveals a profound and unexpected bridge between them. This chapter explores that bridge through quantum models of cognition and decision-making, showing how principles from quantum mechanics can help understand, and even predict, the complexities of human perception, behaviour and societal dynamics. We introduce a computationally accessible framework grounded in quantum theory, designed to model ambiguity, bias and choice in a way that classical logic cannot. Drawing on interdisciplinary sources, we demonstrate how this approach not only enriches our understanding of individual cognition but also extends to AI, video game design, financial behaviour and collective decision-making in society. Through vivid case studies---from optical illusions in video games to decision biases in economic, defence and political contexts---we show how quantum models offer new explanatory and predictive power. Thus, this chapter invites readers from all professional backgrounds to reimagine cognition and decision-making, opening new avenues for science, technology and human understanding.