We propose the simple model of learning based on which we derive and explain the Yerkes-Dodson law - one of the oldest laws of experimental psychology. The approach uses some ideas of quantum theory of open systems (QTOS) and develops the method of statistical description of psychological systems that was proposed by author earlier.
We propose the consistent statistical approach for the quantitative description of simple psychological phenomena using the methods of quantum theory of open systems (QTOS). Taking as the starting point the K. Lewin's psychological field theory we show that basic concepts of this theory can be naturally represented in the language of QTOS. In particular provided that all stimuli acting on psychological system (that is individual or group of interest) are known one can associate with these stimuli corresponding operators and after that to write down the equation for evolution of density matrix of the relevant open system which allows one to find probabilities of all possible behavior alternatives. Using the method proposed we consider in detail simple model describing such interesting psychological phenomena as cognitive dissonance and the impact of competition among group members on its unity.
The cognitive frame in which most neuropsychological research on the neural basis of behavior is conducted contains the assumption that brain mechanisms per se fully suffice to explain all psychologically described phenomena. This assumption stems from the idea that the brain is made up entirely of material particles and fields, and that all causal mechanisms must therefore be formulated solely in terms of properties of these elements. One consequence of this stance is that psychological terms having intrinsic mentalistic and/or experiential content (terms such as "feeling," "knowing," and "effort") have not been included as primary causal factors in neuropsychological research: insofar as properties are not described in material terms they are deemed irrelevant to the causal mechanisms underlying brain function. However, the origin of this demand that experiential realities be excluded from the causal base is a theory of nature that has been known for more that three quarters of a century to be fundamentally incorrect. It is explained here why it is consequently scientifically unwarranted to assume that material factors alone can in principle explain all causal mechanisms relevant to neuroscience. More importantly, it is explained how a key quantum effect can be introduced into brain dynamics in a simple and practical way that provides a rationally coherent, causally formulated, physics-based way of understanding and using the psychological and physical data derived from the growing set of studies of the capacity of directed attention and mental effort to systematically alter brain function.
Statistical learning theory provides the mathematical foundations for understanding generalization from finite data. As quantum computing and quantum machine learning continue to develop, there is increasing interest in whether analogous principles can be formulated for learning settings involving quantum data, quantum models, or quantum computation. This paper offers an introductory perspective on quantum statistical learning theory. We begin by recalling the basic ideas of classical statistical learning theory, including empirical risk minimization, hypothesis classes, generalization, and sample complexity, and then discuss how these concepts must be adapted in the quantum setting. In particular, we distinguish between learning with classical data using quantum algorithms, learning with quantum models, and learning directly from quantum data. We then outline the central learning-theoretic objects in the quantum setting, including quantum hypotheses, measurement-based predictions, population and empirical risk, shot complexity, and information-extraction limitations. A simple illustrative example is used to highlight how finite measurements and quantum uncertainty affect learning performance. We conclude with a discussion of open problems and directions toward a more unified theory of generalization and learnability in quantum learning.
This paper begins with a historical review of the mutual influence of physics and psychology, from Freud's invention of psychic energy inspired by von Boltzmann' thermodynamics to the enrichment quantum physics gained from the side of psychology by the notion of complementarity (the invention of Niels Bohr who was inspired by William James), besides we consider the resonance of the correspondence between Wolfgang Pauli and Carl Jung in both physics and psychology. Then we turn to the problem of development of mathematical models for laws of thought starting with Boolean logic and progressing towards foundations of classical probability theory. Interestingly, the laws of classical logic and probability are routinely violated not only by quantum statistical phenomena but by cognitive phenomena as well. This is yet another common feature between quantum physics and psychology. In particular, cognitive data can exhibit a kind of the probabilistic interference effect. This similarity with quantum physics convinced a multi-disciplinary group of scientists (physicists, psychologists, economists, sociologists) to apply the mathematical apparatus of quantum mechanics to modeling of cognition. We illustrate this activity by considering a few concrete phenomena: the order and disjunction effects, recognition of ambiguous figures, categorization-decision making. In Appendix 1 we briefly present essentials of theory of contextual probability and a method of representations of contextual probabilities by complex probability amplitudes (solution of the ``inverse Born's problem'') based on a quantum-like representation algorithm (QLRA).
Neuroscience has realized valuable advances.As example, it has identified brain regions performing learning, memory, feeling and still more.The current tendency in neuroscience retains that adding pieces of knowledge day by day, we will finally arrive to an unified understanding of brain including the manner in which mind and consciousness arise and explain their functions.Also a number of empirical psychological results have been collected.However a profound gap remains between neuroscience advances and empirical psychological data .We retain that such existing gap is due to a missing theoretical model linking neuroscience to psychology .We have arrived to formulate a basic theoretical quantum model particularly of the perceptive -cognitive functions.The result is that quantum mechanics has a decisive role in human cognition.Our quantum model relates directly our mind entities.The model also finds the existing correlations between the brain time dynamics operating without direct awareness and the subsequent behaviour that is induced determining our subsequent behaviours.In the present paper we discuss in detail and for the first time such further basic features of the model.We perform one experiment on priming showing that its results agree with the given quantum mechanical basic model.
This book provides the first consideration of quantum principles in legal decision making. It shows how quantum probability theory can be applied to psychology and law to deepen our understanding of psychological and legal issues, suggesting a paradigm shift in the study of these areas. Written as an introduction to quantum probability theory for non-physicists, the book offers an accessible summary of how psychology can be applied to quantum phenomena for human decision making. It introduces psychologists and lawyers to fresh conceptual frameworks, including classical probability theory, classical statistics, data mining and quantum theory. Wojciechowski shows how quantum theory and concepts can be transferred to psychological research models, and can help to resolve previously unexplained psychological phenomena, influence scientific views, undermine traditional foundations of the legal system and result in the development of new research directions. Studies conducted by the author are used to show how problems facing legal decision making can be addressed in a completely new light with the application of quantum probability theory. This cutting-edge and innovative book will be of great interest to graduate students and researchers in the fields of forensic psychology and legal decision making, as well as legal professionals and legal scholars.