Context effects produced by question orders reveal quantum nature of human judgments
TLDR
It is suggested that quantum probability theory, initially invented to explain noncommutativity of measurements in physics, provides a simple account for a surprising regularity regarding measurement order effects in social and behavioral science.Abstract:
The hypothesis that human reasoning obeys the laws of quantum rather than classical probability has been used in recent years to explain a variety of seemingly “irrational” judgment and decision-making findings. This article provides independent evidence for this hypothesis based on an a priori prediction, called the quantum question (QQ) equality, concerning the effect of asking attitude questions successively in different orders. We empirically evaluated the predicted QQ equality using 70 national representative surveys and two laboratory experiments that manipulated question orders. Each national study contained 651–3,006 participants. The results provided strong support for the predicted QQ equality. These findings suggest that quantum probability theory, initially invented to explain noncommutativity of measurements in physics, provides a simple account for a surprising regularity regarding measurement order effects in social and behavioral science.read more
Citations
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Book
The Oxford Handbook of Computational and Mathematical Psychology
TL;DR: This book presents Quantum Models of Cognition and Decision, a new approach to Mathematical and Computational Modeling in Clinical Psychology that combines Bayesian Estimation in Hierarchical Models and Quantum Models, and its Applications.
Journal ArticleDOI
Quantum cognition: a new theoretical approach to psychology
TL;DR: In this article, the authors compare and contrast probabilistic models based on Bayesian or classical versus quantum principles, and highlight the advantages and disadvantages of each approach, including the advantages of using quantum models to address cognitive phenomena that have proven recalcitrant to modeling by means of classical probability theory.
Journal ArticleDOI
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
TL;DR: It is argued that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques.
Journal ArticleDOI
An evidential dynamical model to predict the interference effect of categorization on decision making results
Zichang He,Wen Jiang +1 more
TL;DR: A new evidential dynamical (ED) model based on Dempster–Shafer (D-S) evidence theory and quantum dynamical modelling is proposed and an inspiring dynamical decision making framework is proposed in this paper.
Quantum cognition: A new theoretical approach to psychology
TL;DR: This review compares and contrasts probabilistic models based on Bayesian or classical versus quantum principles, and highlights the advantages and disadvantages of each approach.
References
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Measures on the Closed Subspaces of a Hilbert Space
TL;DR: In this paper, a measure on the closed subspaces of a Hilbert space is defined, which assigns to every closed subspace a non-negative real number such that if the subspace is a countable collection of mutually orthogonal sub-spaces having closed linear span B, then
Book
Quantum Models of Cognition and Decision
Jerome R. Busemeyer,Peter Bruza +1 more
TL;DR: The foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory, 'contextuality' and 'quantum entanglement', are introduced, which allow cognitive phenomena to be modeled in non-reductionist ways.
Journal ArticleDOI
Importance of quantum decoherence in brain processes.
Max Tegmark,Max Tegmark +1 more
TL;DR: It is argued that the degrees of freedom of the human brain that relate to cognitive processes should be thought of as a classical rather than quantum system, i.e., that there is nothing fundamentally wrong with the current classical approach to neural network simulations.