Psychophysiological methods for the diagnostics of human functional states: New approaches and perspectives

Chernorizov, A.M.; Isaychev, S.A.; Zinchenko, Y.P.; Galatenko, V.V.; Znamenskaya, I.A.; Zakharov, P.N.; Khakhalin, A.V.; Gradoboeva, O.N.

Авторы: Chernorizov Alexsander M., Isaychev S.A., Zinchenko Yury P., Galatenko V.V., Znamenskaya I.A., Zakharov P.N., Khakhalin A.V., Gradoboeva O.N.
Журнал: Psychology in Russia: State of the Art
Том: 9
Номер: 4
Год издания: 2016
Первая страница: 23
Последняя страница: 36
DOI: 10.11621/pir.2016.0403
Аннотация: L. S. Vygotsky in his famous methodological essay "The historical meaning of psychological crisis" (1928) emphasized the importance of studying any psychological process or state as a "whole" - that is, as characterized from the subjective and objective sides at the same time. This position is fully relevant for studying the human functional states (FSes). Today the objective psychophysiological diagnostics of human FSes in activities associated with a high risk of technological disasters (in nuclear-power plants, transportation, the chemical industry) are extremely relevant and socially important. This article reviews some new psychophysiological methods of FS assessment that are being developed in Russia and abroad and discusses different aspects of developing integral psychophysiological FS assessment. The emphasis is on distant methods of FS diagnostics: the bioradiolocation method, laser Doppler vibrometry, eye tracking, audio and video recordings, infrared thermography. The possibilities and limitations of the most popular emotion atlases - the Facial Affect Scoring Technique (FAST) and the Facial Action Coding System (FACS) - in developing distant visual-range and infrared-range systems for automated classification of facial expressions are analyzed. A special section of the article concentrates on the problem of constructing an integral psychophysiological FS index. Mathematical algorithms that provide a partition of FS indicators into different FS types are based on various methods of machine learning. We propose the vector approach for construction of complex estimations of the human FSes.
Добавил в систему: Черноризов Александр Михайлович

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