Abstract
We present the quantum-like model of information processing by the brain’s neural networks. The model does not refer to genuine quantum processes in the brain. In this model, uncertainty generated by the action potential of a neuron is represented as quantum-like superposition of the basic mental states corresponding to a neural code. Neuron’s state space is described as complex Hilbert space (quantum information representation). The brain’s psychological functions perform self-measurements by extracting concrete answers to questions (solutions of problems) from quantum information states. This extraction is modeled in the framework of open quantum systems theory. In this way, it is possible to proceed without appealing to the state’s collapse. Dynamics of the state of psychological function F is described by the quantum master equation. Its stationary states represent classical statistical mixtures of possible outputs of F (decisions). This model can be used for justification of quantum-like modeling cognition and decision-making. The latter is supported by plenty of statistical data collected in cognitive psychology.
Highlights
Recent years have been characterized by tremendous development of quantum information theory and engineering
We can point to the wave of interest to quantum-like models in cognition, psychology, and decision-making
The main stimulus for development of quantum-like modeling came from decision theory that has been suffering of numerous paradoxes, some of them unresolvable by means of the traditional methods
Summary
Recent years have been characterized by tremendous development of quantum information theory and engineering. We can point to the wave of interest to quantum-like models in cognition, psychology, and decision-making. The main stimulus for development of quantum-like modeling came from decision theory that has been suffering of numerous paradoxes, some of them unresolvable by means of the traditional methods. We point out that Tversky and Kahenman [3,4] and other researchers in psychology and economics (starting with the seminal paradoxes of Allais [5] and Ellsberg [6]) demonstrated cases where classical probability (CP) prescription and actual human thinking persistently diverge, at least relative to baseline classical intuitions. There is a plenty of probabilistic data that does not match the laws of CP These data were typically related to probability fallacies and irrational behavior. Tversky and Kahenman advertised the heuristic approach as an alternative to CP-modeling
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