1887
  1. Home
  2. e-Journals
  3. The Mental Lexicon
  4. Online First Articles
  5. Online First Article
image of The effect of spelling errors on reading tasks
USD
Buy:$35.00 + Taxes

Abstract

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.

© John Benjamins Publishing Company
Loading

Article metrics loading...

/content/journals/10.1075/ml.25006.sli
2026-03-20
2026-04-20

  • From This Site

    /content/journals/10.1075/ml.25006.sli
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. 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(), –. 10.3758/BRM.40.1.278
     https://doi.org/10.3758/BRM.40.1.278 [Google Scholar]
  2. Andrews, S., & Bond, R.
    (2008b) Lexical expertise and reading skill: bottom-up and top-down processing of lexical ambiguity. Reading and Writing, 22(), –. 10.1007/s11145‑008‑9137‑7
     https://doi.org/10.1007/s11145-008-9137-7 [Google Scholar]
  3. Andrews, S., & Hersch, J.
    (2010) Lexical precision in skilled readers: Individual differences in masked neighbor priming. Journal of Experimental Psychology General, 139(), –. 10.1037/a0018366
     https://doi.org/10.1037/a0018366 [Google Scholar]
  4. (2010b) Lexical precision in skilled readers: Individual differences in masked neighbor priming. Journal of Experimental Psychology General, 139(), –. 10.1037/a0018366
     https://doi.org/10.1037/a0018366 [Google Scholar]
  5. Andrews, S., Veldre, A., & Clarke, I. E.
    (2020) Measuring lexical quality: the role of spelling ability. Behavior Research Methods, 52(), –. 10.3758/s13428‑020‑01387‑3
     https://doi.org/10.3758/s13428-020-01387-3 [Google Scholar]
  6. 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(), –. 10.1037/a0023851
     https://doi.org/10.1037/a0023851 [Google Scholar]
  7. 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(), –. 10.3758/BF03193014
     https://doi.org/10.3758/BF03193014 [Google Scholar]
  8. Bartoń, K.
    (2025) MuMIn: Multi-Model Inference. R package version 1.48.11. Available at: https://CRAN.R-project.org/package=MuMIn (accessedSeptember 2, 2025).
  9. 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: https://CRAN.R-project.org/package=lme4 (accessedAugust 28, 2024).
  10. Belikov V., Kopylov N., Piperski A., Selegey V., Sharoff S.
    (2013) Corpus as language: from scalability to register variation. InDialogue, Russian International Conference on Computational Linguistics, Bekasovo.
     [Google Scholar]
  11. 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, (), –. 10.1007/s11145‑017‑9741‑5
     https://doi.org/10.1007/s11145-017-9741-5 [Google Scholar]
  12. Brysbaert, Marc & Vander Beken, Heleen
    (2019) A Dutch and an English spelling test for Dutch-English bilingual university students. 10.31234/osf.io/xd3y7
     https://doi.org/10.31234/osf.io/xd3y7 [Google Scholar]
  13. Burt, J. S., & Fury, M. B.
    (2000) Spelling in adults: The role of reading skills and experience. Reading and Writing: An Interdisciplinary Journal, (), –. 10.1023/A:1008071802996
     https://doi.org/10.1023/A:1008071802996 [Google Scholar]
  14. Chateau, D., & Jared, D.
    (2000) Exposure to print and word recognition process. Memory & Cognition, (), –. 10.3758/BF03211582
     https://doi.org/10.3758/BF03211582 [Google Scholar]
  15. Chen, S., & Fang, S.
    (2013) Developing a Chinese version of anAuthor Recognition Testfor college students in Taiwan. Journal of Research in Reading, 38(), –. 10.1111/1467‑9817.12018
     https://doi.org/10.1111/1467-9817.12018 [Google Scholar]
  16. 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, –. 10.28995/2075‑7182‑2020‑19‑147‑159
     https://doi.org/10.28995/2075-7182-2020-19-147-159 [Google Scholar]
  17. 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(), –. 10.21638/spbu09.2023.412
     https://doi.org/10.21638/spbu09.2023.412 [Google Scholar]
  18. Grolig, L., Tiffin-Richards, S. P., & Schroeder, S.
    (2020) Print exposure across the reading life span. Reading and Writing, 33(), –. 10.1007/s11145‑019‑10014‑3
     https://doi.org/10.1007/s11145-019-10014-3 [Google Scholar]
  19. Daniels, P. T., & Share, D. L.
    (2017) Writing system variation and its Consequences for reading and dyslexia. Scientific Studies of Reading, 22(), –. 10.1080/10888438.2017.1379082
     https://doi.org/10.1080/10888438.2017.1379082 [Google Scholar]
  20. Dujardin, E., Jobard, G., Vahine, T., & Mathey, S.
    (2021) Norms of vocabulary, reading, and spelling tests in French university students. Behavior Research Methods, 54(), –. 10.3758/s13428‑021‑01684‑5
     https://doi.org/10.3758/s13428-021-01684-5 [Google Scholar]
  21. Fox, J.
    (2001) car: companion to applied regression. R package version 3.1–3. Available at: https://CRAN.R-project.org/web/packages/car (accessedSeptember 2, 2025).
  22. Katz, L., & Frost, R.
    (1992) The Reading Process is Different for Different Orthographies: The Orthographic Depth Hypothesis. InAdvances in psychology (pp.–). 10.1016/S0166‑4115(08)62789‑2
     https://doi.org/10.1016/S0166-4115(08)62789-2 [Google Scholar]
  23. Kerek, E., & Niemi, P.
    (2009) Learning to read in Russian: effects of orthographic complexity. Journal of Research in Reading, 32(), –. 10.1111/j.1467‑9817.2008.01390.x
     https://doi.org/10.1111/j.1467-9817.2008.01390.x [Google Scholar]
  24. 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(), –. 10.1037/xge0001038
     https://doi.org/10.1037/xge0001038 [Google Scholar]
  25. 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(), –. 10.1037/xlm0000547
     https://doi.org/10.1037/xlm0000547 [Google Scholar]
  26. Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B.
    (2017) LMERTest Package: Tests in Linear Mixed Effects models. Journal of Statistical Software, (). 10.18637/jss.v082.i13
     https://doi.org/10.18637/jss.v082.i13 [Google Scholar]
  27. Larionova, E., Garakh, Z. & Martynova, O.
    (2023) Top-down modulation of brain responses in spelling error recognition. Acta Psychologica, , . 10.1016/j.actpsy.2023.103891
     https://doi.org/10.1016/j.actpsy.2023.103891 [Google Scholar]
  28. Larionova, E., Rebreikina, A. & Martynova, O.
    (2024) Electrophysiological signatures of spelling sensitivity development from primary school age to adulthood. Scientific Reports, 14, . 10.1038/s41598‑024‑58219‑z
     https://doi.org/10.1038/s41598-024-58219-z [Google Scholar]
  29. 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(), –. 10.1177/1747021818814461
     https://doi.org/10.1177/1747021818814461 [Google Scholar]
  30. Lenth, R.
    (2024) emmeans: estimated marginal means, aka least-squares means. R package version 1.10.5. Available at: https://CRAN.R-project.org/package=emmeans (accessedAugust 28, 2024).
  31. Liberman, A.
    (1980) Orthography and phonemics in present-day Russian. InJ. Kavanagh & R. Venezky (Eds.), Orthography, reading and dyslexia (pp.–). Baltimore, MD: University Park Press.
     [Google Scholar]
  32. Mano, Q. R., & Guerin, J. M.
    (2017) Direct and indirect effects of print exposure on silent reading fluency. Reading and Writing, 31(), –. 10.1007/s11145‑017‑9794‑5
     https://doi.org/10.1007/s11145-017-9794-5 [Google Scholar]
  33. Mar, R. A., & Rain, M.
    (2015) Narrative fiction and expository nonfiction differentially predict verbal ability. Scientific Studies of Reading, 19(), –. 10.1080/10888438.2015.1069296
     https://doi.org/10.1080/10888438.2015.1069296 [Google Scholar]
  34. 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(), –. 10.1111/j.1467‑9817.2008.00371.x
     https://doi.org/10.1111/j.1467-9817.2008.00371.x [Google Scholar]
  35. 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. InOxford University Press eBooks (pp.–). 10.1093/acprof:oso/9780199547548.003.0010
     https://doi.org/10.1093/acprof:oso/9780199547548.003.0010 [Google Scholar]
  36. Moore, M., & Gordon, P. C.
    (2014) Reading ability and print exposure: item response theory analysis of the author recognition test. Behavior Research Methods, 47(), –. 10.3758/s13428‑014‑0534‑3
     https://doi.org/10.3758/s13428-014-0534-3 [Google Scholar]
  37. Ocal, T., & Ehri, L.
    (2016) Spelling ability in college students predicted by decoding, print exposure, and vocabulary. Journal of College Reading and Learning, 47(), –. 10.1080/10790195.2016.1219242
     https://doi.org/10.1080/10790195.2016.1219242 [Google Scholar]
  38. Perfetti, C. A.
    (1985) Reading ability. Oxford University Press.
  39. Perfetti, C. A., & Hart, L.
    (2002) The lexical quality hypothesis. InStudies in written language and literacy (pp.–). 10.1075/swll.11.14per
     https://doi.org/10.1075/swll.11.14per [Google Scholar]
  40. Perfetti, C.
    (2007) Reading Ability: Lexical Quality to Comprehension. Scientific Studies of Reading, (), –. 10.1080/10888430701530730
     https://doi.org/10.1080/10888430701530730 [Google Scholar]
  41. Piperski A.
    (2013) Big and diverse is beautiful: A large corpus of Russian to study linguistic variation //Proc. 8th Web as Corpus Workshop (WAC-8), –.
     [Google Scholar]
  42. R Core Team
    R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing. — Vienna, Austria. URLhttps://www.R-project.org/
  43. Rahmanian, S., & Kuperman, V.
    (2017) Spelling errors impede recognition of correctly spelled word forms. Scientific Studies of Reading, 23(), –. 10.1080/10888438.2017.1359274
     https://doi.org/10.1080/10888438.2017.1359274 [Google Scholar]
  44. 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(), –. 10.1111/j.1551‑6709.2009.01092.x
     https://doi.org/10.1111/j.1551-6709.2009.01092.x [Google Scholar]
  45. Ramscar, M., Dye, M., & McCauley, S. M.
    (2013) Error and expectation in language learning: The curious absence of mouses in adult speech. Language, 89(), –. 10.1353/lan.2013.0068
     https://doi.org/10.1353/lan.2013.0068 [Google Scholar]
  46. Ratcliff, R.
    (1993) Methods for dealing with reaction time outliers. Psychological Bulletin, 114(), –. 10.1037/0033‑2909.114.3.510
     https://doi.org/10.1037/0033-2909.114.3.510 [Google Scholar]
  47. Recorla, R. A., & Wagner, A. R.
    (1972) A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement. InA. H. Black, & W. F. Prokasy (Eds.), Classical Conditioning II: Current Research and Theory (pp.–). New York: Appleton-Century-Crofts.
     [Google Scholar]
  48. Schmalz, X., Marinus, E., Coltheart, M., & Castles, A.
    (2015) Getting to the bottom of orthographic depth. Psychonomic Bulletin & Review, 22(), –. 10.3758/s13423‑015‑0835‑2
     https://doi.org/10.3758/s13423-015-0835-2 [Google Scholar]
  49. Stanovich, K. E., & West, R. F.
    (1989) Exposure to print and orthographic processing. Reading Research Quarterly, (), . 10.2307/747605
     https://doi.org/10.2307/747605 [Google Scholar]
  50. Taylor, J. N., & Perfetti, C. A.
    (2016) Eye movements reveal readers’ lexical quality and reading experience. Reading and Writing: An Interdisciplinary Journal, (), –. 10.1007/s11145‑015‑9616‑6
     https://doi.org/10.1007/s11145-015-9616-6 [Google Scholar]
  51. 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), (), –. 10.1111/j.1467‑9868.2010.00749.x
     https://doi.org/10.1111/j.1467-9868.2010.00749.x [Google Scholar]
  52. Zehr, J., and Schwarz, F.
    (2018) PennController for Internet based experiments (IBEX). https://www.pcibex.net/reference/ (AccessedAugust 28, 2024).
/content/journals/10.1075/ml.25006.sli
Loading
The effect of spelling errors on reading tasks
John Benjamins ; https://doi.org/10.1075/ml.25006.sli
/content/journals/10.1075/ml.25006.sli
/content/journals/10.1075/ml.25006.sli
Loading

Data & Media loading...

  • Article Type: Research Article
Keywords: spelling entropy ; orthographic transparency ; lexical quality ; spelling ; print exposure ; error detection

Most Cited

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error