Cover not available

Article published In: The Mental Lexicon: Online-First Articles

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload PDF
EPUB logo with download linkDownload EPUB
authorization required

The effect of spelling errors on reading tasks

A study on Russian

 | HSE UniversityMoscow
ORCiD logo with link | St.Petersburg UniversitySt. Petersburg
 | University of Nova GoricaNova Gorica
 | St.Petersburg UniversitySt. Petersburg
 | St.Petersburg UniversitySt. Petersburg
Published online: 20 March 2026
DOI logo with link
https://doi.org/10.1075/ml.25006.sli
Many studies on different languages analyzed how spelling errors are produced and detected. Recently, a new generalization was made for several languages: frequently misspelled words are read more slowly, even when they are written correctly and one knows how to spell them. This is explained by the lower quality of their lexical representations diluted by the exposure to recurring errors. In this study, we confirm this generalization for Russian and report several novel findings. We conducted four experiments with different participants: two lexical decision tasks and two spelling error detection tasks, to compare a task that consciously focuses on spelling to the one that does not. Firstly, the accuracy rate in the error detection task was a better predictor of response times in the lexical decision task than other factors including spelling entropy and word frequency. This further confirms the low lexical quality hypothesis. Secondly, although Russian orthography is in the middle of the transparency scale, Russian patterned with languages having transparent orthographies in these experiments — this shows which properties may be relevant. Thirdly, we tested errors of different types and showed that this factor was important for the error detection task, but not for the lexical decision task, in which only the frequencies of different spellings matter.
Article outline
  • Introduction
    • Russian orthography
    • Author Recognition Test (ART) to assess print exposure
    • Previous processing studies on the role of spelling errors
    • The present study
  • Experiment 1A
    • Participants
    • Materials
    • Procedure
    • Analysis
    • Results and discussion
  • Experiment 1B
    • Participants
    • Materials
    • Procedure
    • Results and discussion
  • Experiment 2A
    • Participants
    • Materials
    • Procedure
    • Results and discussion
  • Experiment 2B
    • Participants
    • Materials
    • Procedure
    • Results and discussion
  • Conclusions
  • Note
  • References
References (52)
References
Acheson, D. J., Wells, J. B., & MacDonald, M. C. (2008). New and updated tests of print exposure and reading abilities in college students. Behavior Research Methods, 40(11), 278–289. Google Scholar logo with link to Google Scholar
Andrews, S., & Bond, R. (2008b). Lexical expertise and reading skill: bottom-up and top-down processing of lexical ambiguity. Reading and Writing, 22(61), 687–711. Google Scholar logo with link to Google Scholar
Andrews, S., & Hersch, J. (2010). Lexical precision in skilled readers: Individual differences in masked neighbor priming. Journal of Experimental Psychology General, 139(21), 299–318. Google Scholar logo with link to Google Scholar
(2010b). Lexical precision in skilled readers: Individual differences in masked neighbor priming. Journal of Experimental Psychology General, 139(21), 299–318. Google Scholar logo with link to Google Scholar
Andrews, S., Veldre, A., & Clarke, I. E. (2020). Measuring lexical quality: the role of spelling ability. Behavior Research Methods, 52(61), 2257–2282. Google Scholar logo with link to Google Scholar
Baayen, R. H., Milin, P., Đurđević, D. F., Hendrix, P., & Marelli, M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118(31), 438–481. Google Scholar logo with link to Google Scholar
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., Neely, J. H., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39(31), 445–459. Google Scholar logo with link to Google Scholar
Bartoń, K. (2025). MuMIn: Multi-Model Inference. R package version 1.48.11. Available at: [URL] (accessed September 2, 2025).
Bates, D., Maechler, M., Bolker, B., and Walker, S. (2015). lme4: linear mixed-effects models using Eigen and S4. R package version 1.1–8. Available at: [URL] (accessed August 28, 2024).
Belikov V., Kopylov N., Piperski A., Selegey V., Sharoff S. (2013). Corpus as language: from scalability to register variation. In Dialogue, Russian International Conference on Computational Linguistics, Bekasovo.Google Scholar logo with link to Google Scholar
Borleffs, E., Maassen, B. A. M., Lyytinen, H., & Zwarts, F. (2017). Measuring orthographic transparency and morphological-syllabic complexity in alphabetic orthographies: a narrative review. Reading and writing, 301(81), 1617–1638. Google Scholar logo with link to Google Scholar
Brysbaert, Marc & Vander Beken, Heleen. (2019). A Dutch and an English spelling test for Dutch-English bilingual university students. Google Scholar logo with link to Google Scholar
Burt, J. S., & Fury, M. B. (2000). Spelling in adults: The role of reading skills and experience. Reading and Writing: An Interdisciplinary Journal, 13(1–2), 1–30. Google Scholar logo with link to Google Scholar
Chateau, D., & Jared, D. (2000). Exposure to print and word recognition process. Memory & Cognition, 281(11), 143–153. Google Scholar logo with link to Google Scholar
Chen, S., & Fang, S. (2013). Developing a Chinese version of anAuthor Recognition Testfor college students in Taiwan. Journal of Research in Reading, 38(41), 344–360. Google Scholar logo with link to Google Scholar
Chernova, D. A., Alexeeva, S. V., & Slioussar, N. A. (2020). Chemu nas uchat oshibki: trudnosti pri obrabotke slov s chastotnymi orfograficheskimi oshibkami (in Russian, ‘What do we learn from mistakes: processing difficulties with frequently misspelled words’). Computational Linguistics and Intellectual Technologies, 19, 147–159. Google Scholar logo with link to Google Scholar
Chernova, D. A., & Bakhturina, P. V. (2023). Method of print exposure assessment: Application in psycholinguistics and adaptation for the Russian language. Vestnik of Saint Petersburg University Language and Literature, 20(41), 872–887. Google Scholar logo with link to Google Scholar
Grolig, L., Tiffin-Richards, S. P., & Schroeder, S. (2020). Print exposure across the reading life span. Reading and Writing, 33(61), 1423–1441. Google Scholar logo with link to Google Scholar
Daniels, P. T., & Share, D. L. (2017). Writing system variation and its Consequences for reading and dyslexia. Scientific Studies of Reading, 22(11), 101–116. Google Scholar logo with link to Google Scholar
Dujardin, E., Jobard, G., Vahine, T., & Mathey, S. (2021). Norms of vocabulary, reading, and spelling tests in French university students. Behavior Research Methods, 54(41), 1611–1625. Google Scholar logo with link to Google Scholar
Fox, J. (2001). car: companion to applied regression. R package version 3.1–3. Available at: [URL] (accessed September 2, 2025).
Katz, L., & Frost, R. (1992). The Reading Process is Different for Different Orthographies: The Orthographic Depth Hypothesis. In Advances in psychology (pp. 67–84). Google Scholar logo with link to Google Scholar
Kerek, E., & Niemi, P. (2009). Learning to read in Russian: effects of orthographic complexity. Journal of Research in Reading, 32(21), 157–179. Google Scholar logo with link to Google Scholar
Kuperman, V., Bar-On, A., Bertram, R., Boshra, R., Deutsch, A., Kyröläinen, A., Mathiopoulou, B., Oralova, G., & Protopapas, A. (2021). Prevalence of spelling errors affects reading behavior across languages. Journal of Experimental Psychology General, 150(101), 1974–1993. Google Scholar logo with link to Google Scholar
Kuperman, V., Matsuki, K., & Van Dyke, J. A. (2018). Contributions of reader- and text-level characteristics to eye-movement patterns during passage reading. Journal of Experimental Psychology Learning Memory and Cognition, 44(111), 1687–1713. Google Scholar logo with link to Google Scholar
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). LMERTest Package: Tests in Linear Mixed Effects models. Journal of Statistical Software, 82(13). Google Scholar logo with link to Google Scholar
Larionova, E., Garakh, Z. & Martynova, O. (2023). Top-down modulation of brain responses in spelling error recognition. Acta Psychologica, 2351, 103891. Google Scholar logo with link to Google Scholar
Larionova, E., Rebreikina, A. & Martynova, O. (2024). Electrophysiological signatures of spelling sensitivity development from primary school age to adulthood. Scientific Reports, 14, 7585. Google Scholar logo with link to Google Scholar
Lee, H., Seong, E., Choi, W., & Lowder, M. W. (2018). Development and assessment of the Korean Author Recognition Test. Quarterly Journal of Experimental Psychology, 72(71), 1837–1846. Google Scholar logo with link to Google Scholar
Lenth, R. (2024). emmeans: estimated marginal means, aka least-squares means. R package version 1.10.5. Available at: [URL] (accessed August 28, 2024).
Liberman, A. (1980). Orthography and phonemics in present-day Russian. In J. Kavanagh & R. Venezky (Eds.), Orthography, reading and dyslexia (pp. 51–55). Baltimore, MD: University Park Press.Google Scholar logo with link to Google Scholar
Mano, Q. R., & Guerin, J. M. (2017). Direct and indirect effects of print exposure on silent reading fluency. Reading and Writing, 31(21), 483–502. Google Scholar logo with link to Google Scholar
Mar, R. A., & Rain, M. (2015). Narrative fiction and expository nonfiction differentially predict verbal ability. Scientific Studies of Reading, 19(61), 419–433. Google Scholar logo with link to Google Scholar
Martin-Chang, S. L., & Gould, O. N. (2008). Revisiting print exposure: exploring differential links to vocabulary, comprehension and reading rate. Journal of Research in Reading, 31(31), 273–284. Google Scholar logo with link to Google Scholar
Milin, P., Kuperman, V., Kostić, A., & Baayen, H. R. (2009). 10 Words and paradigms bit by bit: An information-theoretic approach to the processing of inflection and derivation. In Oxford University Press eBooks (pp. 214–252). Google Scholar logo with link to Google Scholar
Moore, M., & Gordon, P. C. (2014). Reading ability and print exposure: item response theory analysis of the author recognition test. Behavior Research Methods, 47(41), 1095–1109. Google Scholar logo with link to Google Scholar
Ocal, T., & Ehri, L. (2016). Spelling ability in college students predicted by decoding, print exposure, and vocabulary. Journal of College Reading and Learning, 47(11), 58–74. Google Scholar logo with link to Google Scholar
Perfetti, C. A. (1985). Reading ability. Oxford University Press.Google Scholar logo with link to Google Scholar
Perfetti, C. A., & Hart, L. (2002). The lexical quality hypothesis. In Studies in written language and literacy (pp. 189–213). Google Scholar logo with link to Google Scholar
Perfetti, C. (2007). Reading Ability: Lexical Quality to Comprehension. Scientific Studies of Reading, 11(4), 357–383. Google Scholar logo with link to Google Scholar
Piperski A. et al. (2013). Big and diverse is beautiful: A large corpus of Russian to study linguistic variation // Proc. 8th Web as Corpus Workshop (WAC-8), 24–29.Google Scholar logo with link to Google Scholar
R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. — Vienna, Austria. URL [URL]
Rahmanian, S., & Kuperman, V. (2017). Spelling errors impede recognition of correctly spelled word forms. Scientific Studies of Reading, 23(11), 24–36. Google Scholar logo with link to Google Scholar
Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K. (2010). The effects of Feature-Label-Order and their implications for symbolic learning. Cognitive Science, 34(61), 909–957. Google Scholar logo with link to Google Scholar
Ramscar, M., Dye, M., & McCauley, S. M. (2013). Error and expectation in language learning: The curious absence of mouses in adult speech. Language, 89(41), 760–793. Google Scholar logo with link to Google Scholar
Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114(31), 510–532. Google Scholar logo with link to Google Scholar
Recorla, R. A., & Wagner, A. R. (1972). A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement. In A. H. Black, & W. F. Prokasy (Eds.), Classical Conditioning II: Current Research and Theory (pp. 64–99). New York: Appleton-Century-Crofts.Google Scholar logo with link to Google Scholar
Schmalz, X., Marinus, E., Coltheart, M., & Castles, A. (2015). Getting to the bottom of orthographic depth. Psychonomic Bulletin & Review, 22(61), 1614–1629. Google Scholar logo with link to Google Scholar
Stanovich, K. E., & West, R. F. (1989). Exposure to print and orthographic processing. Reading Research Quarterly, 24(4), 402. Google Scholar logo with link to Google Scholar
Taylor, J. N., & Perfetti, C. A. (2016). Eye movements reveal readers’ lexical quality and reading experience. Reading and Writing: An Interdisciplinary Journal, 291(61), 1069–1103. Google Scholar logo with link to Google Scholar
Wood S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B), 73(1), 3–36. Google Scholar logo with link to Google Scholar
Zehr, J., and Schwarz, F. (2018). PennController for Internet based experiments (IBEX). [URL] (Accessed August 28, 2024).
Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue