Cognitive metaphor in scientific discourse and its functions

This article is devoted to a comprehensive study of the cognitive nature of metaphor in scientific discourse, namely its role in the study of artificial intelligence (AI), which is especially relevant in a situation of rapid development of AI and the expansion of its fields of application. Researchers at various levels have not unreasonably noted that cognitive metaphor is used to describe AI in texts of various genres, from fiction to science; however, most large-scale research in this field focuses on the description of AI in the media and popular culture. In this article, we decided to consider cognitive metaphors that are used in describing AI in scientific discourse, and find out what functions metaphorical models perform not only in describing complex AI concepts, but also in their cognitive processing, conceptualization, and determining the vector of scientific thought development. The purpose of this study is to identify the main cognitive metaphors used in describing AI, critically analyze their functional load, and study the specifics of their use in modern scientific discourse. One of the main research questions is to determine to what extent general metaphorical models are applicable to the scientific context and to identify the unique features of scientific cognitive metaphor. In the course of this work, we solved a number of tasks, namely: we conducted a detailed systematic analysis of modern scientific literature on AI, formulated a definition of cognitive metaphor, described its structure and method of education, and determined the functions of cognitive metaphor in language.; The role of cognitive metaphor in the language of a certain scientific field has been identified, and cognitive metaphors used in describing AI in the compiled corpus have been selected and analyzed using the methodology of J. R. R. Tolkien. Lakoff and M. Johnson. The theoretical basis of the research consists of scientific works by T. N. Vinokurova, V. P. Danilenko, L. M. Alekseeva, D. V. Vasilenko, S. V. Grinev-Grinevich, J. Lakoff, M. Johnson, P. Norvig, S. Russell, and other authors, as well as materials from the project “Portraits and perceptions of AI and why they matter”.

1. Pyatunina AA. On the cognitive potential of metaphor in scientific discourse. Vestnik of Russian State University of Economics. The series "Psychology. Pedagogy. Education”. 2021;(3):120-129 (in Russian).

2. Filipczuk-Rosińska S. Analysing Metaphorical Political Discourse in the L2 Academic Classroom. Procedia. Social and Behavioral Science. 2016;228: 329-334 (in English).

3. McCormack E. Cognitive theory of metaphor. Moscow: Publishing House “Progress”; 1990:358-386 (in Russian).

4. Lakoff G., Johnson М. Metaphors We Live By. Chicago: The University of Chicago Press; 2003:256 (in English).

5. Teliya VN. Metaphorization and its role in creating a linguistic picture of the world. The role of the human factor in language: Language and the worldview: collection of articles. Moscow: Publishing House “Science”; 1988:173-204 (in Russian).

6. Krivoshlykova LV. On the definition of the concept of "Discourse". Bulletin of the Peoples' Friendship University of Russia. Theory of language. Semiotics. Semantics. 2010;(3):42-47 (in Russian).

7. Krasnykh VV. Fundamentals of psycholinguistics and theory of communication. A course of lectures. Moscow: Publishing House "Gnosis"; 2001: 270 (in Russian).

8. Maikova TA. Conceptual metaphor in the terminology of sociology. Bulletin of the Peoples' Friendship University of Russia. Theory of language. Semiotics. Semantics. 2015;(3):65-72 (in Russian).

9. Arutyunova ND. Metaphor and discourse. Collection. Theory of metaphor. Moscow: Publishing House “Progress”; 1990:5-32 (in Russian).

10. Chudinov AP, Solopova OA. Linguistic Political Prognostics: Models and Scenarios of Future. Procedia. Social and Behavioral Science. 2015;200:412-417 (in English).

11. Mishankina NA, Zheleznyakova AN. The Problem of Linguistic Analysis of Musical Metaphors at Lessons of Russian as a Foreign Language. Procedia. Social and Behavioral Science. 2015;215:316-321 (in English).

12. Tarev B.V. Lexical borrowings: linguistic and didactic aspects. SIBFU Magazine. Humanities.2012;7:944-950 (in English).

13. Roldán-Riejos AA, Plaza SM. Taste of the Technical Cuisine: Metals and Other Ingredients. Procedia. Social and Behavioral Science. 2015;178:201-206 (in English).

14. Ahrens K, Zeng WH. Referential and evaluative strategies of conceptual metaphor use in government discourse. Journal of Pragmatics.2022;188:83-96 (in English).

15. Antonova T. Social Conflict through Conceptual Metaphor in Media Discourse. Procedia. Social and Behavioral Science. 2014;154:368-373. (in English).

16. de la Rosa VM, Lázaro LM. Legitimating meritocracy as part of the American Dream through the ritual of commencement speeches. Linguistics and Education. 2022;72:101-107. (in English).

17. Torregrosa G, Sánchez-Reyes S. Raising Metaphor Awareness in English for Law Enforcement. Procedia. Social and Behavioral Science. 2015;212:304-308 (in English).

18. Marugina, NI. Conceptual Metaphor as a Model Generating Literary Discourse. Procedia. Social and Behavioral Science. 2014;154:112-117 (in English).

19. Filipczuk-Rosińska S. Analysing Metaphorical Political Discourse in the L2 Academic Classroom. Procedia. Social and Behavioral Science. 2016;228:329-334 (in English).

20. Mishankina, NA, Panasenko EA. Metaphorical Modelling of the Concept “Technology”. Procedia. Social and Behavioral Science. 2016;236:101-106 (in English).

21. Portrayals and perceptions of AI and why they matter. 2018:28. Available at: https://royalsociety.org/-/media/policy/projects/ai-narratives/AI-narratives-workshop-findings.pdf [Accessed: 4 July 2025] (in English).

22. Bewersdorff A. Assessing student errors in experimentation using artificial intelligence and large language models: A comparative study with human raters. Computers and Education: Artificial Intelligence. 2023;5. Available at: https://www.sciencedirect.com/science/article/pii/S2666920X23000565 [Accessed: 11 July 2025] (in English).

23. Peretz-Andersson E. Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. International Journal of Information Management. 2024;77. Available at: https://www.sciencedirect.com/science/article/pii/S026840122400029X [Accessed: 3 July 2025] (in English).

24. Lee J. AI as “Another I”: Journey map of working with artificial intelligence from AI-phobia to AI-preparedness. Organizational Dynamics. 2023;3. Available at: https://www.sciencedirect.com/science/article/pii/S0090261623000384 [Accessed: 28 June 2025] (in English).

25. Amariles DR. Promises and limits of law for a human-centric artificial intelligence. Computer Law & Security Review. 2023;48. Available at: https://www.sciencedirect.com/science/article/pii/S0267364923000067 [Accessed:14 June 2025] (in English).

26. Coeckelbergh M. Climate change and the political pathways of AI: The technocracydemocracy dilemma in light of artificial intelligence and human agency. Technology in Society. 2023;75. Available at: https://www.sciencedirect.com/science/article/pii/S0160791X23002117 [Accessed: 20 June 2025] (in English).

27. Quan Y. Societal impacts of artificial intelligence: Ethical, legal, and governance issues. Societal Impacts. 2023;3. Available at: https://www.sciencedirect.com/science/article/pii/S2949697724000055 [Accessed: 19 June 2025] (in English).

28. An H. Forecasting daily extreme temperatures in Chinese representative cities using artificial intelligence models. Weather and Climate Extremes. 2023;42. Available at: https://www.sciencedirect.com/science/article/pii/S2212094723000749 [Accessed: 16 June 2025] (in English).

29. Vo V. Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Science & Medicine. 2023;338. Available at: https://www.sciencedirect.com/science/article/pii/S0277953623007141 [Accessed: 25 June 25] (in English).

30. Su J. Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities. Computers and Education: Artificial Intelligence. 2023;4. Available at: https://www.sciencedirect.com/science/article/pii/S2666920X23000036 [Accessed: 27 June 2025] (in English).

31. Bhattacharya S. Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda. Transportation Research Part E: Logistics and Transportation Review. 2024;184. Available at: https://www.sciencedirect.com/science/article/pii/S1366554524000450 [Accessed: 18 June 2025] (in English).

32. Rizvi S. Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence. 2023;4. Available at: https://www.sciencedirect.com/science/article/pii/S2666920X23000243 [Accessed: 10 July 2025] (in English).

33. Chen X. Information fusion and artificial intelligence for smart healthcare: a bibliometric study. Information and Processing & Management. – 2023;1. Available at: https://www.sciencedirect.com/science/article/pii/S030645732200214X. [Accessed: 10 July 2025] (in English).