ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

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Petukhov A. Y., Polevaia S. A. Measuring cognitive potential based on the performance of tasks of various levels of complexity. Izvestiya VUZ. Applied Nonlinear Dynamics, 2022, vol. 30, iss. 3, pp. 311-321. DOI: 10.18500/0869-6632-2022-30-3-311-321

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Measuring cognitive potential based on the performance of tasks of various levels of complexity

Petukhov Aleksandr Y, Keldysh Institute of Applied Mathematics (Russian Academy of Sciences)
Polevaia Sofia Alexandrovna, Lobachevsky State University of Nizhny Novgorod

Purpose of work. The article is devoted to the topic of measuring the cognitive potential of a person on the basis of the obtained experimental data in order to identify its potential capabilities, as well as to monitor their dynamics, for example, to diagnose recovery after an illness. This goal is divided in the study into two tasks, namely, to assess the cognitive potential, it is necessary to develop two algorithms: 1. Assessment of the level of cognitive complexity of tasks. 2. Systems of levels of cognitive potential for an individual. Methods. The basis of the methods is a set of experimental, including specially developed author’s, techniques, as well as mathematical methods for processing data and calculating the entered specific parameters to formalize the cognitive potential. Results. On the basis of these methods, methods (and specific formulas) are proposed for calculating the cognitive potential of an individual using experimental data and tasks of various levels of complexity. Conclusion. Within the framework of this study, a methodology for determining the value of cognitive potential was created on the basis of the theory of information images / representations, as well as a specially developed web-toolkit for objectifying cognitive skills (including the so-called softskills). This value can be useful, both in studies related to changes in cognitive abilities as a result of the influence of various internal and external factors (for example, learning, diseases, injuries, etc.), diagnostic goals (for example, with the aim of determining the speed recovery after a disease that affects cognitive activity, such as a stroke or SARS-CoV-2), and in the formation of requirements for certain work positions that significantly depend on the cognitive abilities of the individual.

  1. Alexandrov YI. Psychophysiological regularities of learning and methods of training. Psychological Journal. 2012;33(6):5–19 (in Russian).
  2. Kozhevnikov VV, Polevaya SA, Shishalov IS, Bakhchina AV. Mobile HR-Meter (HR-Meter). Certificate of State Registration of Computer Programs 2014618634 dated 26.08.2014 (in Russian).
  3. Vandekerckhove J. A cognitive latent variable model for the simultaneous analysis of behavioral and personality data. Journal of Mathematical Psychology. 2014;60:58–71. DOI: 10.1016/
  4. Faugeras O, Inglis J. Stochastic neural field equations: a rigorous footing. Journal of Mathematical Biology. 2015;71(2):259–300. DOI: 10.1007/s00285-014-0807-6.
  5. Kooi BW. Modelling the dynamics of traits involved in fighting-predators-prey system. Journal of Mathematical Biology. 2015;71(6–7):1575–1605. DOI: 10.1007/s00285-015-0869-0.
  6. Haazebroek P, van Dantzig S, Hommel B. A computational model of perception and action for cognitive robotics. Cognitive Processing. 2011;12(4):355. DOI: 10.1007/s10339-011-0408-x.
  7. Geukes S, Gaskell MG, Zwitserlood P. Stroop effects from newly learned color words: effects of memory consolidation and episodic context. Frontiers in Psychology. 2015;6:278. DOI: 10.3389/fpsyg.2015.00278.
  8. Polevaya SA, Eremin EV, Bulanov NA, Bakhchina АV, Kovalchuk AV, Parin SB. Event-related telemetry of heart rate for personalized remote monitoring of cognitive functions and stress under conditions of everyday activity. Modern Technologies in Medicine. 2019;11(1):109–115. DOI: 10.17691/stm2019.11.1.13.
  9. Almeria M, Cejudo JC, Sotoca J, Deus J, Krupinski J. Cognitive profile following COVID-19 infection: Clinical predictors leading to neuropsychological impairment. Brain, Behavior, & Immunity — Health. 2020;9:100163. DOI: 10.1016/j.bbih.2020.100163.
  10. Anokhin KV. The genetic probes for mapping the neural network during training // In: The Principles and Mechanisms of the Human Brain. Leningrad: Nauka; 1989. P. 191–192 (in Russian).
  11. Petukhov AY, Polevaya SA, Yakhno VG. The theory of information images: Modeling based on diffusion equations. International Journal of Biomathematics. 2016;9(6):1650087. DOI: 10.1142/S179352451650087X.
  12. Petukhov AY, Polevaya SA. Modeling of communicative individual interactions through the theory of information images. Current Psychology. 2017;36(3):428–433. DOI: 10.1007/s12144-016-9431-5.
  13. Petukhov AY, Polevaya SA. Modeling of cognitive brain activity through the information images theory in terms of the bilingual Stroop test. International Journal of Biomathematics. 2017;10(7):1750092. DOI: 10.1142/S1793524517500929.
  14. Petukhov AY, Polevaya SA, Polevaya AV. Experimental diagnostics of the emotional state of individuals using external stimuli and a model of neurocognitive brain activity. Diagnostics. 2022;12(1):125. DOI: 10.3390/diagnostics12010125.
  15. Friston KJ, Price CJ. Dynamic representations and generative models of brain function. Brain Research Bulletin. 2001;54(3):275–285. DOI: 10.1016/S0361-9230(00)00436-6.
  16. Guclu U, van Gerven MAJ. Modeling the dynamics of human brain activity with recurrent neural networks. Frontiers in Computational Neuroscience. 2017;11:7. DOI: 10.3389/fncom.2017.00007.
  17. Herweg NA, Kahana MJ. Spatial representations in the human brain. Frontiers in Human Neuroscience. 2018;12:297. DOI: 10.3389/fnhum.2018.00297.