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The relevance of the undertaken research consists in considering psycholinguistics an interdisciplinary field, which studies the interrelation between mind and language. It is important to perceive learning foreign language as an act of cognition, experience, and creativity in the psycholinguistic aspect of studying. Psycholinguistics concerns with the study of the cognitive process that supports the acquisition and use of language.  The purpose of the paper is to reveal the importance of psycholinguistics approach and cognitive science for learning a foreign language in the context of psycholinguistic approach and cognitive methods for learning second language, based on achievements of the “Scientific School of A.V. Khutorsky”. Methodology is of an overview-analytical nature with an attempt to apply cognitive techniques to learning. Our observations on the psycholinguistic approach and the cognitive methods are based on the “Myth of Niels Bohr and the barometer question” by Alexander Calandra. Results. The analysis made it possible to determine how the logic of reflections has been explored from the lens of psycholinguistics and how the range of cognitive methods can be enlisted to learn a foreign language. It turns next to an overview of cognitive techniques used in psycholinguistics as applied to study. The verbal presentation of the idea is not only a form of compressed thought or interactive, creative cognition, but it also has a literary quality and makes use of a range of devices in a way. In the article, the solution formation reflects the features of transforming mental representations about the multidimensional space of life. Conclusions. According to the research, the paper concludes that cognitive methods are the ability to create judgments that are paradoxical in form and deep in content, perceived as deviating from the norm, and humor also presupposes the presence of the inverse ability to perceive such judgments in their entirety and depth and emotional brightness.

Context. Knowledge bases are the main element of artificial intelligence systems. They are formed on the basis of two generally accepted approaches: the object-oriented approach and object-structural approach. Knowledge structuring through its ordering, classification and typing of selected classes is the main operation that is implemented in both approaches. Quite often there are situations when data or knowledge is not exact and it is impossible to perform their exact classification. These features necessitate the development of new approaches aimed at solving problems of extracting knowledge from large arrays of unordered data, structuring, presenting and analytical processing of inexact knowledge in automated construction of knowledge bases. Objective. The objective of this paper is a research of new approaches for solving problems of representation of knowledge about cases in intellectual decision support systems. Method. An approach aimed at modifying Case-Based Reasoning method on the basis of Rough Set Approach has been proposed in this paper. The proposed method forms a partition of cases to determine the degree of their belonging to the goal classes using upper and lower approximations of goal classes, considering the relative importance of classification attributes and formed equivalence classes. Results. The proposed modification of Case-Based Reasoning method allows extracting knowledge about cases from arrays of unordered data with the purpose of the case base construction, and handling the inconsistent (in cases where for the same values of attributes cases belong to different classes), and incomplete (in cases where the values of some attributes or information of the case belonging to the given class is missing or unreliable) information about cases. Conclusions. The proposed method of representation knowledge about cases, their adaptation and subsequent search in the case base formed under uncertainty and existence of inexact, rough, inconsistent initial data constitutes a theoretical basis for constructing intellectual decision support systems.