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Modeling of cognitive process using complexity theory methods

Соловйов, Володимир Миколайович; Моісеєнко, Наталя Володимирівна; Тарасова, Олена Юріївна

URI: http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3609
https://doi.org/10.31812/123456789/3609

Date: 2019

Abstract:

The features of modeling of the cognitive component of social and humanitarian systems have been considered. An example of using multiscale, multifractal and network complexity measures has shown that these and other synergetic models and methods allow us to correctly describe the quantitative differences of cognitive systems. The cognitive process is proposed to be regarded as a separate implementation of an individual cognitive trajectory, which can be represented as a time series and to investigate its static and dynamic features by the methods of complexity theory. Prognostic possibilities of the complex systems theory will allow to correct the corresponding pedagogical technologies.

Description:

1. Rutten, N., van Joolingen, W.R., van der Veen, J.T.: The learning effects of computer simulations in science education. Computers & Education. 58(1), 136–153 (2012). doi:10.1016/j.compedu.2011.07.017 2. Lamb, R., Premo, J.: Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms. Computation. 3(3), 427–443 (2015). doi:10.3390/computation3030427 3. Mayor, J., Gomez, P. (ed.): Proceedings of the 13th Neural Computation and Psychology Workshop (NCPW13) on Computational models of cognitive processes. World Scientific Publishing, Singapore (2014) 4. Nikolis, G., Prigogine, I.: Exploring complexity: An introduction. W. H. Freeman and Company, New York (1989) 5. Kapitsa, S.P., Kurdyumov, S.P., Malinetsky, G.G.: Sinergetika i prognozy buduschego (Synergetics and prognoses of the future). Editorial URSS, Moscow (2003) 6. Arnold, V.I.: Matematika i matematicheskoe obrazovanie v sovremennom mire (Mathematics and mathematical education in the modern world). Matematicheskoe obrazovanie. 2, 109–112 (1997) 7. Harasim, L.: Shift happens: online education as a new paradigm in learning. The Internet and Higher Education. 3(1–2), 41–61 (2000). doi:10.1016/S1096-7516(00)00032-4 8. Goh, W.P., Kwek, D., Hogan, D., Cheong, S.A.: Complex network analysis of teaching. EPJ Data Science. 3:36 (2014). doi:10.1140/epjds/s13688-014-0034-9 9. The Future of Jobs Report 2018. http://www3.weforum.org/docs/WEF_Future_of_Jobs _2018.pdf (2018). Accessed 24 Mar 2019 10. Solovjov, V.M., Serdyuk O.A., Danilchuk, G.B.: Modelyuvannya skladnih system (Modelling of complex systems). Vydavec' О.Yu. Vovchok, Cherkasy (2016) 11. Hausdorff, J., Zemany, L., Peng, C.-K., Goldberger, A.L.: Maturation of gait dynamics: stride-to-stride variability and its temporal organization in children. Journal of Applied Physiology. 86(3), 1040–1047 (1999). doi:10.1152/jappl.1999.86.3.1040 12. Delignieres, D., Torre, K.: Fractal dynamics of human gait: a reassessment of the 1996 data of Hausdorff et al. Journal of Applied Physiology. 106(4), 1272–1279 (2009). doi:10.1152/japplphysiol.90757.2008 13. Van Rooij, M.M.J.W, Nash, B.A., Rajaraman, S., Holden, J.G.: A fractal approach to dynamic inference and distribution analysis. Frontier in Physiology. 4(1), 1–16 (2013). doi:10.3389/fphys.2013.00001 14. Ausloos, M.: Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series. Physical Review E. 86(3). 031108 (2012). doi:10.1103/PhysRevE.86.031108 15. Liu, X.F., Tse, C.K., Small, M.: Complex network structure of musical compositions: Algorithmic generation of appealing music. Physica A: Statistical Mechanics and its Applications. 389(1), 126–132 (2010). doi:10.1016/j.physa.2009.08.035 16. CompEngine. A self-organizing database of time-series data. http://www.comp-engine.org (2019). Accessed 24 Mar 2019 17. Schmid, U., Ragni, M., Gonzalez, C., Funke, J.: The challenge of complexity for cognitive systems. Cognitive Systems Research. 12(3–4), 211–218 (2011). doi:10.1016/j.cogsys.2010.12.007 18. Bentz, C., Alikaniotis, D., Cysouw, M., Ferrer-i-Cancho, R: The Entropy of Words – Learnability and Expressivity across More Than 1000 Languages. Entropy. 19(6), 275–279 (2017). doi:10.3390/e19060275 19. Hernandez-Gomez, C., Basurdo-Flores, R., Obregon-Quintana, B., Guzman-Vargas, L.: Evaluating the Irregularity of Natural Languages. Entropy. 19(10), 521–621 (2017). doi:10.3390/e19100521 20. Keshmiri, S., Sumioka, H., Yamazaki, R., Ishiguro, H.: Multiscale Entropy Quantifies the Differential Effect of the Medium Embodiment on Older Adults Prefrontal Cortex during the Story Comprehension: A Comparative Analysis. Entropy. 21(2), 199–215 (2019). doi:10.3390/e21020199 21. Wu, M., Liao. L., Luo, X., Ye, X., Yao, Y., Chen, P., Shi, L., Huang, H., Wu, Y.: Children Development Using Gait Signal Dynamics Parameters and Ensemble Learning Algorithms. BioMed Research International. 9246280 (2016). doi:10.1155/2016/9246280 22. Jiang, Z.-Q., Xie, W.-J., Zhou, W.-X., Sornette, D.: Multifractal analysis of financial markets. arXiv:1805.04750 [q-fin.ST]. https://arxiv.org/pdf/1805.04750.pdf (2018). Accessed 24 Mar 2019 23. Wijnants, M.L: A Review of Theoretical Perspectives in Cognitive Science on the Presence of 1/f Scaling in Coordinated Physiological and Cognitive Processes. Journal of Nonlinear Dynamics. 2014. 962043 (2014). doi:10.1155/2014/962043 24. Fan, C., Guo, J.-L., Zha, Y.-L.: Fractal analysis on human dynamics of library loans. Physica A: Statistical Mechanics and its Applications. 391(24), 6617–6625 (2012). doi:10.1016/j.physa.2012.06.063 25. Albert, R., Barabasi, A.-L.: Statistical Mechanics of Complex Networks. Reviews of Modern Physics. 74, 47–97 (2002). doi:10.1103/RevModPhys.74.47 26. Chen, H., Chen, X., Liu, H.: How does language change as a lexical network? An investigation based on written Chinese word co-occurrence networks. PLOS One. 13(2). e0192545 (2018). doi:10.1371/journal.pone.0192545 27. Donner, R.V., Small, M., Donges, J.F., Marwan, N., Zou, Y., Xiang, R., Kurths, J.: Recurrence-based time series analysis by means of complex network methods. International Journal of Bifurcation and Chaos. 21(4), 1019–1046 (2011). doi:10.1142/S0218127411029021 28. Webber, C.L., Ioana, C., Marwan, N. (eds.): Recurrence Plots and Their Quantifications: Expanding Horizons. Proceedings of the 6th International Symposium on Recurrence Plots 2015, Grenoble, France, 17-19 June 2015. Springer Proceedings in Physics, vol. 180. Springer International Publishing, Heidelberg (2016). doi:10.1007/978-3-319-29922-8 29. Soloviev, V., Belinskij, A.: Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors. In: Ermolayev, V., Suárez-Figueroa, M.C., Yakovyna, V., Kharchenko, V., Kobets, V., Kravtsov, H., Peschanenko, V., Prytula, Y., Nikitchenko, M., Spivakovsky, A. (eds.) Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kyiv, Ukraine, May 14-17, 2018. CEUR Workshop Proceedings, vol. 2014, pp. 116–127. http://ceur-ws.org/Vol2104/paper_175.pdf. Accessed 24 Mar 2019 30. Soloviev, V.N, Belinskiy, A.: Complex Systems Theory and Crashes of Cryptocurrency Market. In: Ermolayev V., Suárez-Figueroa M., Yakovyna V., Mayr H., Nikitchenko M., Spivakovsky A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol. 1007, pp. 276–297 (2019). doi:10.1007/978-3-030-13929-2_14

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