Perception of monotonic load: different types of microsleep tolerance
Institute of Cardiological Research, Saratov State Medical University, 410005, Saratov, Russia
2 Center for Coordination of Fundamental Scientific Activities, National Medical Research Center for Therapy and Preventive Medicine, 101990, Moscow, Russia
3 Scientific Medical Center, Saratov State University, 410012, Saratov, Russia
4 Department of general practice dentistry, therapeutic dentistry and pediatric dentistry, Penza Institute for Postgraduate Medical Education, 440060, Penza, Russia
5 Laboratory of Neurobiology of Sleep and Wake, Institute of Higher Nervous Activity and Neurophysiology of RAS, 117485, Moscow, Russia
Accepted: 24 November 2023
Published online: 15 December 2023
Our study aims to clarify the mechanisms of successful microsleep tolerance that accompanies the monotonous solving of simple cognitive tasks. On the example of a homogeneous pool of participants with such stability, we demonstrated objectively calculated characteristics of psychometric and neural correlates that were statistically different in two identified subgroups for the process of long-term monotonous recognition of bistable images (Necker cubes). We hypothesized that the observed differences between the two types of monotony resistance were due to different personal characteristics of different subgroups, viz.: type I observers were characterized by an innate resistance to the occurrence of monotony caused by the organization of the brain and psyche, in particular, by pronounced left-sided frontal electroencephalogram (EEG) asymmetry; type II observers did not have such powerful innate quality albeit implemented a powerful mechanism of self-motivation and/or self-discipline. For subgroup I, it was demonstrated that significant areas of scalp spatial zones had well-formed event-related potentials (ERP), while in subgroup II, the number of spatial zones with well-formed ERP was significantly lower. At the same time, subjects in subgroup II made fewer errors in solving cognitive tasks than those in subgroup I. Use of the proposed simple psychometric characteristics (reaction time distributions) could be adapted to implement training with biofeedback for exercising attention control.
© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.