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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 26, No. 0, pp.157-174
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Jan 2026
Received 20 Dec 2025 Revised 10 Jan 2026 Accepted 10 Jan 2026
DOI: https://doi.org/10.15738/kjell.26..202601.157

한국어에 차용된 영어 단어의 시각적 재인: 어휘 빈도와 철자 불투명도의 역할

Yechan Lee ; Donghyun Kim
(First author) PhD Student, Department of English Language and Literature Kyungpook National University dldpcks3082@gmail.com
(Corresponding author) Assistant Professor, Department of English Language and Literature Kyungpook National University, Tel: +82-53-950-5124 donghyun@knu.ac.kr
Visual recognition of English loanwords in Korean: The role of lexical frequency and orthographic opacity


© 2026 KASELL All rights reserved
This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study examined the interaction between semantic and orthographic processing in Korean learners of English as they visually recognize English words. Fifty Korean learners of English performed a visual lexical decision task consisting of 200 stimuli: 50 loanwords, including 25 with a single phonological competitor in Korean and 25 with multiple semantic competitors, 50 control words, and 100 nonwords. Response times were analyzed using linear mixed-effects models, and accuracy was analyzed using logistic mixed-effects models. The results showed that loanwords elicited significantly faster responses and higher accuracy than control words. However, the number of semantic competitors and the frequency of use in Korean were not significant predictors. Instead, English lexical frequency significantly influenced both response times and accuracy, and the degree of orthographic (spelling-to-pronunciation) opacity, the extent to which spelling and pronunciation diverge, emerged as an additional key predictor of performance. Specifically, words with higher orthographic opacity tended to yield slower responses and lower accuracy. These results indicate that L2 lexical processing in Korean learners is primarily driven by within-language factors, including English lexical frequency and orthographic opacity, rather than by L1-based familiarity. This finding suggests that cross-linguistic influences on L2 word recognition are constrained by the organization of orthographic and phonological representations in L2 and the strength of orthography-phonology mappings.

Keywords:

lexical decision, English loanwords, orthographic opacity, lexical frequency, visual word recognition, Korean learners of English

Acknowledgments

This work was supported by Kyungpook National University Research Fund, 2023.

References

  • 강범모·김흥규(Kang, B.-M and H.-G Kim). 2009. 『한국어 사용 빈도: 1500만 어절 세종 형태 의미 분석 말뭉치 기반』(Korean Word Frequency: Based on a 15-million-word Sejong Morpho-Semantic Corpus). 서울: 한국문화사(Seoul: Hankookmunhwasa).
  • 국립국어원(National Institute of Korean Language). 2016. 『함께 만들고 모두 누리는 새 국어사전 우리말샘』(Urimalsaem, the Open Korean Dictionary). 서울: 국립국어원(Seoul: NIKL)
  • Anwyl-Irvine, A. L., J. Massonnié, A. Flitton, N. Kirkham and J. K. Evershed. 2020. Gorilla in our midst: An online behavioral experiment builder. Behavior Research Methods 52, 388-407. [https://doi.org/10.3758/s13428-019-01237-x]
  • Baek, H., J. Tae, Y. Lee and W. Choi. 2023. Effects of native translation frequency and L2 proficiency on L2 word recognition: Evidence from Korean speakers of English as a foreign language. Journal of Psycholinguistic Research 52(5), 1325-1343. [https://doi.org/10.1007/s10936-022-09912-5]
  • Balota, D. A., M. J. Yap, K. A. Hutchison, M. J. Cortese, B. Kessler, B, Loftis, J. H. Neely and R. Treiman. 2007. The English lexicon project. Behavior Research Methods 39(3), 445-459. [https://doi.org/10.3758/BF03193014]
  • Bassetti, B. 2008. Orthographic input and second language phonology. In T. T. Piske and M. Young-Scholten, eds., Input Matters in SLA. 191-206. Clevedon, UK: Multilingual Matters. [https://doi.org/10.2307/jj.27195479.15]
  • Bates, D., M. Maechler, B. Bolker and S. Walker. 2015. lme4: Linear Mixed-effects Models Using Eigen and S4 (R package version 1.1-12). Available online at https://cran.r-project.org/package=lme4
  • Bloomfield, L. 1933. Language. New York: Henry Holt and Company.
  • Bolker, B., D. Robinson, D. Menne, J. Gabry, P. Buerkner, C. Hau W. and Petry. 2022. Broom.mixed: Tidying Methods for Mixed Models. Available online at https://cran.r-project.org/package=broom.mixed
  • Brysbaert, M. and W. Duyck. 2010. Is it time to leave behind the Revised Hierarchical Model of bilingual language processing after fifteen years of service? Bilingualism: Language and Cognition 13(3), 359-371. [https://doi.org/10.1017/S1366728909990344]
  • De Groot, A. M. 2011. Language and Cognition in Bilinguals and Multilinguals: An introduction. New York: Psychology Press. [https://doi.org/10.4324/9780203841228]
  • Dijkstra, T. and W. J. van Heuven. 2002. The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and Cognition 5(3), 175-197. [https://doi.org/10.1017/S1366728902003012]
  • Edwards, J. 2004. Bilingualism: Contexts, constraints, and identities. Journal of Language and Social Psychology 23(1), 135-141. [https://doi.org/10.1177/0261927X03260811]
  • Edwards, A. A., V. M. Rigobon, L. M. Steacy and D. L. Compton. 2024. Spelling-to-pronunciation transparency ratings for the 20,000 most frequently written English words. Behavior Research Methods 56(4), 2828-2841. [https://doi.org/10.3758/s13428-023-02205-2]
  • Grainger, J. 2018. Orthographic processing: A ‘mid-level’vision of reading: The 44th Sir Frederic Bartlett Lecture. Quarterly Journal of Experimental Psychology 71(2), 335-359. [https://doi.org/10.1080/17470218.2017.1314515]
  • Hahn, H. R. 2014. Korean EFL learners’ lexical development: Evidence from a word association test. Journal of the Modern English Education 15(1), 27-48.
  • Harrington, M. 2006. The lexical decision task as a measure of L2 lexical proficiency. EUROSLA Yearbook 6(1), 147-168. [https://doi.org/10.1075/eurosla.6.10har]
  • Keuleers, E., P. Lacey, K. Rastle and M. Brysbaert. 2012. The British Lexicon Project: Lexical decision data for 28,730 monosyllabic and disyllabic English words. Behavior Research Methods 44(1), 287-304. [https://doi.org/10.3758/s13428-011-0118-4]
  • Kim, J. and C. Davis. 2003. Task effects in masked cross-script translation and phonological priming. Journal of Memory and Language 49(4), 484-499. [https://doi.org/10.1016/S0749-596X(03)00093-7]
  • Kroll, J. F. and E. Stewart. 1994. Category interference in translation and picture naming: Evidence for asymmetric connections between bilingual memory representations. Journal of Memory and Language 33(2), 149-174. [https://doi.org/10.1006/jmla.1994.1008]
  • Lee, Y., E. Jang and W. Choi. 2018. L2-L1 translation priming effects in a lexical decision task: Evidence from low proficient Korean-English bilinguals. Frontiers in Psychology 9, 267. [https://doi.org/10.3389/fpsyg.2018.00267]
  • Lemhöfer, K. and M. Broersma. 2012. Introducing LexTALE: A quick and valid lexical test for advanced learners of English. Behavior Research Methods 44(2), 325-343. [https://doi.org/10.3758/s13428-011-0146-0]
  • Longtin, C. M. and F. Meunier. 2005. Morphological decomposition in early visual word processing. Journal of Memory and Language 53(1), 26-41. [https://doi.org/10.1016/j.jml.2005.02.008]
  • Pallier, C., N. Boris and J. Bourgin. 2019. Openlexicon, GitHub repository, Available online at https://github.com/chrplr/openlexicon
  • Perea, M., H. Winskel and P. Gomez. 2018. How orthographic-specific characteristics shape letter position coding: The case of Thai script. Psychonomic Bulletin & Review 25(1), 416-422. [https://doi.org/10.3758/s13423-017-1279-7]
  • R Core Team. 2024. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available online at https://www.R-project.org/
  • Tamaoka, K. and Y. Miyaoka. 2003. The cognitive processing of Japanese loanwords in katakana. Japanese Psychological Research 45(2), 69-79. [https://doi.org/10.1111/1468-5884.t01-1-00035]
  • Veivo, O., J. Järvikivi., V. Porretta and J. Hyönä. 2016. Orthographic activation in L2 spoken word recognition depends on proficiency: Evidence from eye-tracking. Frontiers in Psychology 7, 1120. [https://doi.org/10.3389/fpsyg.2016.01120]
  • Wickham, H. 2016. Data analysis. In ggplot2: Elegant Graphics for Data Analysis, 189-201. New York: Springer. [https://doi.org/10.1007/978-3-319-24277-4_9]
  • Wickham, H., M. Averick, J. Bryan, W. Chang, L. D. A. McGowan, R. François, G. Grolemund, A. Hayes, L. Henry, J. Hester, M. Kuhn, T. L. Pedersen, E. Miller, S. M. Bache, K. Müller, J. Ooms, D. Robinson, D. P. Seidel, V. Spinu, ... and H. Yutani. 2019. Welcome to the tidyverse. Journal of Open Source Software 4(43), 1686. [https://doi.org/10.21105/joss.01686]
  • Yap, M. J., D. A. Balota, M. J. Cortese and J. M. Watson. 2006. Single-versus dual-process models of lexical decision performance: insights from response time distributional analysis. Journal of Experimental Psychology: Human Perception and Performance 32(6), 1324-1344. [https://doi.org/10.1037/0096-1523.32.6.1324]