Transformation of the structure of semantic memory in learning a foreign language

 
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Abstract

Context and relevance. This article is devoted to identifying structural changes that occur in human semantic memory (SM) when learning a foreign language (FL). This topic has been repeatedly addressed by researchers, but until now it has been studied exclusively within the framework of the “verbal fluency” paradigm, the application of which has shown contradictory results. In order to solve this problem, we conducted a study on English-language material using the “snowball sampling” method, which allows us to present the structure of the SM as a network built on free associations. Objective. The study`s aim is to determine the characteristics of structural changes in SM during the study of FL. Hypothesis. In the course of studying the FL, the network structure of the SM changes towards increasing size, flexibility, connectivity, greater conformity with the “small world” structure and less separation. Methods and materials. The present study involved two groups of linguistic students: the first consisted of first-years (N = 27; gender: 15 f., 12 m.; age: M = 18.14), and the second consisted of fourth-years (N = 29; gender: 18 f., 11 m.; age: M = 21.44). The participants completed a “snowball sampling” task. Based on the results of the task, a network structure of the SM was constructed for each participant, and an intergroup comparison of its parameters was carried out. Results. The results showed that when learning a FL, the SM networks become larger in size (due to an increase in the number of nodes and edges), more connected (due to an increase in the average node degree), more flexible (due to a decrease in the shortest path length), less separated (due to a decrease in modularity), but do not differ in the “small world” index. Conclusions. The features of the changes in SM structure when learning a foreign language are shown. The results emphasize the role of the SM in the development of language abilities in foreign language learners.

General Information

Keywords: semantic memory, learning a foreign language, semantic networks, “snowball sampling” paradigm, free associations

Journal rubric: Educational Psychology

Article type: scientific article

DOI: https://doi.org/10.17759/exppsy.2026190210

Funding. The study was conducted within the framework of the state assignment of the Ministry of Education and Science of Russia «Integrated assessment of cognitive and emotional resources of participants in the Internet communication in their native and foreign languages». No 125090210031-6.

Supplemental data. All the data are available upon request to the author.

Received 04.02.2025

Revised 27.10.2025

Accepted

Published

For citation: Barmin, A.V. (2026). Transformation of the structure of semantic memory in learning a foreign language. Experimental Psychology (Russia), 19(2), 155–168. (In Russ.). https://doi.org/10.17759/exppsy.2026190210

© Barmin A.V., 2026

License: CC BY-NC 4.0

References

  1. Валуева, Е.А., Лаптева, Н.М., Поспелов, Н.А., Ушаков, Д.В. (2024). Феномен инкубации и активация семантической сети. Культурно-историческая психология, 20(4), 40—51. https://doi.org/10.17759/chp.2024200405
    Valueva, E.A., Lapteva, N.M., Pospelov, N.A., Ushakov, D.V. (2024). Incubation and activation of the semantic network. Cultural-Historical Psychology, 20(4), 40—51. (In Russ.). https://doi.org/10.17759/chp.2024200405
  2. Величковский, Б.М. (1982). Современная когнитивная психология. М.: МГУ.
    Velichkovskiy, B.M. (1982). Modern cognitive psychology. Moscow: MSU. (In Russ.).
  3. Морозова, О.А. (2017). Структурное сетевое моделирование в когнитивной науке. Психологические исследования, 10(55). https://doi.org/10.54359/ps.v10i55.351
    Morozova, O.А. (2017). Structural network modelling in cognitive science. Psychological Studies, 10(55). (In Russ.). https://doi.org/10.54359/ps.v10i55.351
  4. Agustin-Llach, M. (2022). How age and L2 proficiency affect the L2 lexicon. System, 104, 102697. https://doi.org/10.1016/j.system.2021.102697
  5. Barabási, A.L., Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509—512. https://doi.org/10.1126/science.286.5439.509
  6. Borodkin, K., Kenett, Y.N., Faust, M., Mashal, N. (2016). When pumpkin is closer to onion than to squash: The structure of the second language lexicon. Cognition, 156, 60—70. https://doi.org/10.1016/j.cognition.2016.07.014
  7. Christensen, A.P. (2018). NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis in R. The R Journal, 10(2), 422—439. https://doi.org/10.32614/RJ-2018-065
  8. Collins, A.M., Loftus, E.F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407—428. https://doi.org/10.1037/0033-295X.82.6.407
  9. Collins, A.M., Quillian, M.R. (1969). Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 8(2), 240—247. https://doi.org/10.1016/S0022-5371(69)80069-1
  10. Feng, X., Liu, J. (2023). The developmental trajectories of L2 lexical-semantic networks. Humanities and Social Sciences Communications, 10(1), 1—12. https://doi.org/10.1057/s41599-023-01621-1
  11. Ferguson, C.J. (2016). An effect size primer: A guide for clinicians and researchers. In: A.E. Kazdin (Ed.), Methodological issues and strategies in clinical research (4th ed., pp. 301—310). American Psychological Association. https://doi.org/10.1037/14805-020
  12. Fritz, C.O., Morris, P.E., Richler, J.J. (2012). Effect size estimates: current use, calculations, and interpretation. Journal of experimental psychology: General, 141(1), 2—18. https://doi.org/10.1037/a0024338
  13. Humphries, M.D., Gurney, K. (2008). Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PloS one, 3(4), e0002051.
  14. Ke, J., Yao, Y. (2008). Analysing language development from a network approach. Journal of Quantitative Linguistics, 15(1), 70—99. https://doi.org/10.1080/09296170701794286
  15. Kenett Y.N., Levy O., Kenett D.Y., Stanley H.E., Faust M., Havlin, S. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Proceedings of the National Academy of Sciences of the United States of America, 115(5), 867—872. https://doi.org/10.1073/pnas.1717362115
  16. Kenett, Y.N., Levi, E., Anaki, D., Faust, M. (2017). The semantic distance task: Quantifying semantic distance with semantic network path length. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(9), 1470—1489. https://doi.org/10.1037/xlm0000391
  17. Kuperman, V., Stadthagen-Gonzalez, H., Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior research methods, 44, 978—990. https://doi.org/10.3758/s13428-012-0210-4
  18. Li, J., Jiang, H., Shang, A., Chen, J. (2021). Research on associative learning mechanisms of L2 learners based on complex network theory. Computer Assisted Language Learning, 34(5-6), 637—662. https://doi.org/10.1080/09588221.2019.1633356
  19. Morais, A.S., Olsson, H., Schooler, L.J. (2013). Mapping the structure of semantic memory. Cognitive science, 37(1), 125—145. https://doi.org/10.1111/cogs.12013
  20. Steyvers, M., Tenenbaum, J.B. (2005). The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive science, 29(1), 41—78. https://doi.org/10.1207/s15516709cog2901_3
  21. Watts, D.J., Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440—442. https://doi.org/10.1038/30918
  22. Wulff, D.U., Mata, R. (2022). On the semantic representation of risk. Science advances, 8(27), eabm1883.

Information About the Authors

Artem V. Barmin, Junior Researcher, Laboratory for Cognitive Studies of Communication, Moscow State Linguistic University, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-9658-6621, e-mail: art.barmin@mail.ru

Conflict of interest

The author declares no conflict of interest.

Ethics statement

All subjects provided informed consent to participate in the study. Participants took part in the study voluntarily and their data were analyzed anonymously.

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