Influence of the thought Process on the Emotional Component of the Performing Activity of a Music Institution Student

Authors

  • Natalia Vladimirovna Pavlova Associate Professor, Candidate of Pedagogic Sciences, Department of Vocal Performing and Opera Training, Moscow State Institute of Music named after A.G. Schnittke, Marshal Sokolovsky St, Moscow, Russia. Author

DOI:

https://doi.org/10.61841/tynmd531

Keywords:

Emotional Component, Frisson

Abstract

At the present stage of development of music education, the integrity of the performance of a music institution student is a science of educating and training a person in the unity of theory and practice, combining a special, intellectual, and emotional component. The relevance of the correlation of the intellectual-mental and emotional component in the study of the performance of a music institution student is unquestionable, as the training of specialist music high school involves the formation and development of the competitive ability of the specialist to demonstrate their knowledge, skills, and practice-confirmed theory. The very mechanism of the human brain has long been considered the most difficult problem in science. In the last decade, because of scientific and technological progress, more and more complex human intellectual abilities have begun to manifest. Most studies of the relationship between intellectual-mental and emotional components are aimed at developing combinations of emotions and reason in the theoretical understanding of human activity. Performing activities of a music institution student expand this theoretical framework. Within the framework of performing activities, a music institution student can learn, acquiring new knowledge and implementing it in practice. In the performance activity of a music institution student, the composer's idea is realized in the interrelation of intellectual, mental, and emotional relations. Thus, the synthesis of the musical aspects of performing, the personality of the student by her characteristics (ability, emotionality, temperament and character, mindset, motivation, etc.), and intellectual-mental processes that promote the formation of objective criteria and indicators of the performance of a music institution student as a whole. 

Downloads

Download data is not yet available.

References

[1] Dmitriev, L.B. (2007). The basics of vocal technique. Moscow: Music, p. 368

[2] Ilyin, E.P. (2001). Emotions and feelings. St. Petersburg: Peter, p. 752

[3] Pavlov, I.P. (1973). Twenty Years of Experience in Objective Study of the Higher Nervous Activity (Behavior) of Animals. Science, p. 661 https://www.runivers.ru/philosophy/lib/book6240/144989/

[4] Soloviev, K.P. (2019). Computational studies of systemic and particular principles of information processes in recurrent neural systems: abstract. diss. ... can. biol. Sciences (02.01.03). MIPT (SI), p. 19

[5] Teplov, B.M. (1985). Selected Works: In 2 Volumes. Moscow: Pedagogy. T.1, 328 s. T.2, p. 360

[6] Khaikin, S.E. (2006). Neural networks. Full course. Moscow: Williams Publishing House, 2006. 2nd ed., p.

[7] Hel, I. (2017). Artificial Intelligence and Jeffrey Hinton: The Godfather of “Deep Learning.” Retrieved from: https://hi-news.ru/research-development/iskusstvennyj-intellekt-i-dzheffri-xinton-otec-glubokogoobucheniya.html.

[8] Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., Wu, Y. (2017). Stdp-compatible approximation of backpropagation in an energy-based model. Neural computation. Vol. 29, pp. 555-577.

[9] Lillicrap, T.P., Cownden, D., Tweed, D.B., and Akerman, C.J. (2014) Random feedback weights support learning in deep neural networks. – arXiv:1411.0247v1 [q-bio.NC].

[10] Lillicrap, T.P., Cownden, D., Tweed, D.B., Akerman, C.J. (2016). Random feedback weights support error backpropagation for deep learning. – Nat Commun 7, 13276. Retrieved from: https://doi.org/10.1038/ncomms13276

[11] Nokland, Arild. (2016) “Direct Feedback Alignment Provides Learning in Deep Neural Networks.” arXiv:1609.01596 [cs, stat]. Retrieved from: http://arxiv.org/abs/1609.01596

[12] Ponulak, F., Hopfield, J.J. (2013) Rapid, parallel path planning by propagating wave fronts of spiking neural activity. – //Frontiers in Computational Neuroscience. Retrieved from: https://doi.org/10.3389/fncom.2013.00098

[13] Russell, B. (1931). The scientific outlook. London, George Allen and Unwin, 285 p.

[14] Shakirov, V.V., Solovyeva, K.P., Dunin-Barkowski, W.L. (2018). Review of State-of-the-Art in Deep Learning Artificial Intelligence. Optical memory and neural networks. Vol. 27 (2). pp. 65-80.

[15] Xiao, W., Chen, H., Liao, Q. (2018). Biologically plausible learning algorithms can scale to large datasets. – arXiv:1811.03567.

[16] Retrieved from (2020): https://www.classicalmusicnews.ru/articles/goose-of-music/

[17] Retrieved from (2020): http://www.nanonewsnet.ru/articles/2016/murashki-pobezhali-kak-svyazanamuzyka-fiziologiya

Downloads

Published

31.07.2020

How to Cite

Vladimirovna Pavlova, N. (2020). Influence of the thought Process on the Emotional Component of the Performing Activity of a Music Institution Student. International Journal of Psychosocial Rehabilitation, 24(5), 880-891. https://doi.org/10.61841/tynmd531