Psycholinguistic matrix of image formation: case study of educational network discourse
DOI:
https://doi.org/10.56294/sctconf2024690Keywords:
Educational Network Discourse, Image Formation, Matrix, PsycholinguisticAbstract
The psycholinguistic matrix of image formation is a model that elucidates the intricate process of constructing mental images within the human mind. Comprising three integral components – perceptual, cognitive, and emotional – it provides a framework for comprehending how individuals interpret and react to the information presented in various forms, particularly within educational network discourse. In the context of educational network discourse, this matrix serves as an invaluable tool for understanding how people assimilate knowledge and navigate digital content. The psycholinguistic matrix of image formation proves particularly relevant in the development of effective digital educational materials. By understanding how individuals perceive, process, and emotionally connect with online content, it becomes possible to design learning resources that optimize comprehension, retention, and engagement. Practical applications include using images to enhance information reception, incorporating videos to facilitate comprehension, and integrating interactive elements to sustain interest and active participation within educational network discourse. Understanding this matrix offers profound insights into the learning processes occurring within the digital landscape. By leveraging this knowledge, we can harness the potential of technology and innovation to create more effective and engaging online learning experiences. The study addresses a significant gap in knowledge by exploring the psycholinguistic aspects of image creation within educational network discussions. The objective is to understand how digital images and text interact to influence learning, which represents a novel application of psycholinguistic analysis to educational materials. This investigation is important because it offers insights into optimizing digital educational platforms, an area with growing relevance in modern education
References
1. Tsaras K, Papathanasiou I V, Vus V, Panagiotopoulou A, Katsou M A, Kelesi M, Fradelos E C. Predicting factors of depression and anxiety in mental health nurses: A quantitative cross-sectional study. Med Arch. 2018;72(1):62-67. http://www.doi.org/10.5455/medarh.2017.72.62-67
2. Belkin L, Iurynets Ju, Sopilko I, Belkin M. Culture and the use of information understanding in the field of national security (a case study of Ukraine). J Int Leg Commun. 2022;5(2):36-58. https://doi.org/10.32612/uw.27201643.2022.5.pp.36-58
3. Zarrella R J, Mayer R E. The effects of animated pedagogical agents on learning: A meta-analysis. J Educ Psychol. 2012;104(2):273-289.
4. Kobzeva T A, Kulish V S. Violation of ethics in professional communication and legal liability of people’s deputies. Leg Horiz. 2020;24(37):16-22. http://www.doi.org/10.21272/legalhorizons.2020.i24.p16
5. Bartlett F C. Remembering: A study in experimental and social psychology. Cambridge: Cambridge University Press; 1932.
6. Miller G A. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol. Rev. 1956;63(2):81-97. https://doi.org/10.1037/h0043158
7. Fodor J A. The Modularity of Mind. Cambridge: The MIT Press; 1983.
8. Anderson J R. Cognitive psychology and its implications. 5th ed. Belper: Worth Publishers; 2000.
9. Strauss E D, Schloss K B, Palmer S E. The effects of imagined experiences of objects on preferences for colors. J Vis. 2011;11:392-392.
10. Binder J R, Conant LL, Humphries C J, Fernandino L, Simons S B, Aguilar M, Desai R H. Toward a brain-based componential semantic representation. Cogn Neuropsychol. 2016;33(3-4):130-174.
11. Kim S, Yoon M, Kim W, Lee S, Kang E. Neural correlates of bridging inferences and coherence processing. J Psycholinguist Res. 2012;41(4):311-321. https://doi.org/10.1007/s10936-011-9185-z
12. Kihlstrom JF, Cantor N. Social intelligence. In: Sternberg R J, Kaufnan S B, ed. The Cambridge Handbook of Intelligence. Cambridge: Cambridge University Press; 2017. p. 564-581. https://doi.org/10.1017/CBO9780511977244.029
13. Collins M X. Information density and dependency length as complementary cognitive models. J Psycholinguist Res. 2014;43(5):651-681.
14. Ghasisin L, Yadegari F, Rahgozar M, Nazari A, Rastegarianzade N. A new set of 272 pictures for psycholinguistic studies: Persian norms for name agreement, image agreement, conceptual familiarity, visual complexity, and age of acquisition. Behav Res Methods. 2015;47(4):1148-1158. https://doi.org/10.3758/s13428-014-0537-0
15. Mayer R E. Applying the science of learning to instructional design: Evidence-based principles for the design of instruction. New York: Routledge; 2010.
16. Park H S, Moreno R, Brunken R. The impact of multimedia redundancy on learning from animated instructional videos. Comput Human Behav. 2014;38:171-179.
17. Wei W, Matías M, Tanvi P, Martin J P, Hoffman P. Modulation of brain activity by psycholinguistic information during naturalistic speech comprehension and production. Cortex. 2022;115:287-306. https://doi.org/10.1016/j.cortex.2022.08.002
18. Wilson R E, Gosling S D, Graham L T. A review of Facebook research in the social sciences. Perspect Psychol Sci. 2012;7:203-220.
19. Gemius Audience. Internet audience of Ukraine in September 2017. https://www.slideshare.net/LesyaPrus/internet-audience-of-ukraine-in-september-2017; 2019.
20. Chomsky N. Syntactic structures. The Hague: Mouton & Co; 1957.
21. Johnson-Laird P N. Mental models. Towards a cognitive science of language, inference and consciousness. Cambridge: Cambridge University Press; 1983.
21. Pinker S. The language instinct. New York: W. Morrow & Co; 1994.
22. Rudolph S, Giesbrecht E. Compositional matrix-space models of language. In Hajič J, Carberry S, Clark S, Nivre J, ed. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala: Association for Computational Linguistics; 2010. p. 907–916.
23. Turner E S, Brown J. A matrix model of semantic representation. Cogn Sci. 2015;39(5):1137-1162.
24. Kartsaklis D, Ramgoolam S, Sadrzadeh M. Linguistic matrix theory. https://arxiv.org/abs/1703.10252;2017.
25. Kandasamy W B V, Kandasamy I, Smarandache F. Linguistic matrices. Miami: Editorial Global Knowledge; 2022.
26. Johnson-Laird P N, Byrne R M J. The mental models theory of language comprehension and production. Lang Linguist Compass. 2015;9(1):1-11
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Copyright (c) 2024 Hanna Truba, Iryna Klymkova, Alina Proskurnia, Yuliia M. Krasilova, Violetta V. Ulishchenko (Author)
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