Regularities of human behavior and mind are the biggest challenges for quantitative modeling in cognitive science and the humanities. Mechanistic-like theories of classical behaviorism and psychophysics apparently do not meet these challenges, leaving spontaneous and creative aspects of our nature out of scope. Conceptually different alternatives, suitable to capture non-deterministic, subjective, and irrational regularities of human activity, are suggested by the formal framework of quantum theory applied to cognition and decision making, i.e. quantum cognition.
By taking advantage of quantum-like structures of cognitive state spaces, such as superposition, interference and entanglement, actuality and potentiality, complementarity, measurement, and other concepts expressed in simple and coherent mathematical form, the quantum cognition approach succeeds in many modeling tasks that are highly problematic for traditional methods. It allows, in particular, to build quantitative models of irrational decisions, cognitive fallacies, the semantics of the natural language, unexpected game equilibria and economic behavior, bioinformatics, artificial intelligence, and many more.
Many of the existing quantum-inspired models, however, do not have wide practical applications due to the lack of predictive power. They provide conceptual explanations of behavioral and cognitive phenomena, succeed in the post-factum fitting of experimental results, but fail to prognose these results in advance. Achieving predictive quality would drastically increase the practicality of quantum models in decision support systems, economics, robotics, information retrieval, and other areas of modern technology.
In recent years, predictive quantum models were developed in several directions of research. In one way or the other, most of them take advantage of the phase parameters of quantum states and operators, responsible for contextuality, subjectivity, and irrationality of quantum logic; productive ideas are suggested from behavioral, computational, semantic, and neural modeling perspectives.
Important pieces for resolving the puzzle are expected from modeling of cognitive causality underlying prognosis, pro-active thinking, decision- and meaning-making activities of living systems. This Research Topic invites quantum-inspired theoretical and experimental research-oriented at achieving the predictive ability of quantum-inspired models of cognition and behavior.
We welcome research and review articles including, but not limited to the following:
• modeling of cognitive causality, goal-oriented behavior, pro-active thinking, subjectivity, and semantics in natural cognitive systems.
• role of phase parameters in quantum models of cognition and behavior.
• modeling of individual and collective decision making, including belief formation and revision, economic behavior, and information retrieval.
• quantum logic in natural and artificial neural networks.
• subjectivity, contextuality, and semantics of the natural language.
• quantum approaches to subjectivity in data representation, robotics, and artificial intelligence.
Regularities of human behavior and mind are the biggest challenges for quantitative modeling in cognitive science and the humanities. Mechanistic-like theories of classical behaviorism and psychophysics apparently do not meet these challenges, leaving spontaneous and creative aspects of our nature out of scope. Conceptually different alternatives, suitable to capture non-deterministic, subjective, and irrational regularities of human activity, are suggested by the formal framework of quantum theory applied to cognition and decision making, i.e. quantum cognition.
By taking advantage of quantum-like structures of cognitive state spaces, such as superposition, interference and entanglement, actuality and potentiality, complementarity, measurement, and other concepts expressed in simple and coherent mathematical form, the quantum cognition approach succeeds in many modeling tasks that are highly problematic for traditional methods. It allows, in particular, to build quantitative models of irrational decisions, cognitive fallacies, the semantics of the natural language, unexpected game equilibria and economic behavior, bioinformatics, artificial intelligence, and many more.
Many of the existing quantum-inspired models, however, do not have wide practical applications due to the lack of predictive power. They provide conceptual explanations of behavioral and cognitive phenomena, succeed in the post-factum fitting of experimental results, but fail to prognose these results in advance. Achieving predictive quality would drastically increase the practicality of quantum models in decision support systems, economics, robotics, information retrieval, and other areas of modern technology.
In recent years, predictive quantum models were developed in several directions of research. In one way or the other, most of them take advantage of the phase parameters of quantum states and operators, responsible for contextuality, subjectivity, and irrationality of quantum logic; productive ideas are suggested from behavioral, computational, semantic, and neural modeling perspectives.
Important pieces for resolving the puzzle are expected from modeling of cognitive causality underlying prognosis, pro-active thinking, decision- and meaning-making activities of living systems. This Research Topic invites quantum-inspired theoretical and experimental research-oriented at achieving the predictive ability of quantum-inspired models of cognition and behavior.
We welcome research and review articles including, but not limited to the following:
• modeling of cognitive causality, goal-oriented behavior, pro-active thinking, subjectivity, and semantics in natural cognitive systems.
• role of phase parameters in quantum models of cognition and behavior.
• modeling of individual and collective decision making, including belief formation and revision, economic behavior, and information retrieval.
• quantum logic in natural and artificial neural networks.
• subjectivity, contextuality, and semantics of the natural language.
• quantum approaches to subjectivity in data representation, robotics, and artificial intelligence.
