1887
  1. Home
  2. e-Journals
  3. The Mental Lexicon
  4. Volume 14, Issue 3
  5. Article
Volume 14, Issue 3
  • ISSN 1871-1340
  • E-ISSN: 1871-1375
USD
Buy:$35.00 + Taxes

Abstract

Abstract

In this study we analyze a large database of lexical decision times for English content words made by speakers of English as an additional language residing in the United States. Our first goal was to test whether the use of statistical measures better able to model variation associated with participants and items would replicate findings of a previous analysis of this data (Berger, Crossley, & Skalicky, 2019). Our second goal was to determine whether variables related to experiences using and learning English would interact with linguistic features of the target words. Results from our statistical analysis suggest affirmative answers to both of these questions. First, our results included significant effects for linguistic features related to contextual diversity and contextual distinctiveness, providing a replication of findings from the original study in that words appearing in more textual and lexical contexts were responded to quicker. Second, a measure of length of English learning and a measure of daily English use interacted with a measure of orthographic similarity. Our study provides further evidence regarding how a large, crowdsourced database can be used to obtain a better understanding of second language lexical recognition behavior and provides suggestions for further research.

© John Benjamins Publishing Company
Loading

Article metrics loading...

/content/journals/10.1075/ml.19028.ska
2020-05-13
2025-04-25

  • From This Site

    /content/journals/10.1075/ml.19028.ska
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Adelman, J. S., Brown, G. D. A., & Quesada, J. F.
    (2006) Contextual diversity, not word frequency, determines word-naming and lexical decision Times. Psychological Science, 17(9), 814–823. 10.1111/j.1467‑9280.2006.01787.x
     https://doi.org/10.1111/j.1467-9280.2006.01787.x [Google Scholar]
  2. Andrews, S.
    (1997) The effect of orthographic similarity on lexical retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin & Review, 4(4), 439–461. 10.3758/BF03214334
     https://doi.org/10.3758/BF03214334 [Google Scholar]
  3. Baayen, R. H., Davidson, D. J., & Bates, D. M.
    (2008) Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412. 10.1016/j.jml.2007.12.005
     https://doi.org/10.1016/j.jml.2007.12.005 [Google Scholar]
  4. Baayen, R. H., & Milin, P.
    (2015) Analyzing reaction times. International Journal of Psychological Research, 3(2), 12–28. 10.21500/20112084.807
     https://doi.org/10.21500/20112084.807 [Google Scholar]
  5. Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., … Treiman, R.
    (2007) The English Lexicon Project. Behavior Research Methods, 39(3), 445–459. 10.3758/BF03193014
     https://doi.org/10.3758/BF03193014 [Google Scholar]
  6. Berger, C. M., Crossley, S. A., & Skalicky, S.
    (2019) Using lexical features to investigate second language lexical decision performance. Studies in Second Language Acquisition, 41(5), 911–935. 10.1017/S0272263119000019.
     https://doi.org/10.1017/S0272263119000019 [Google Scholar]
  7. Brysbaert, M., & New, B.
    (2009) Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41(4), 977–990. 10.3758/BRM.41.4.977
     https://doi.org/10.3758/BRM.41.4.977 [Google Scholar]
  8. Brysbaert, M., Warriner, A. B., & Kuperman, V.
    (2014) Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904–911. 10.3758/s13428‑013‑0403‑5
     https://doi.org/10.3758/s13428-013-0403-5 [Google Scholar]
  9. Cop, U., Keuleers, E., Drieghe, D., & Duyck, W.
    (2015) Frequency effects in monolingual and bilingual natural reading. Psychonomic Bulletin & Review, 22(5), 1216–1234. 10.3758/s13423‑015‑0819‑2
     https://doi.org/10.3758/s13423-015-0819-2 [Google Scholar]
  10. Crawford, J. T., Jussim, L., & Pilanski, J. M.
    (2014) How (not) to interpret and report main effects and interactions in multiple regression: Why Crawford and Pilanski did not actually replicate Lindner and Nosek (2009): Reply to Nosek and Lindner (2014). Political Psychology, 35(6), 857–862. 10.1111/pops.12050
     https://doi.org/10.1111/pops.12050 [Google Scholar]
  11. Crossley, S. A., & Skalicky, S.
    (2017) Making sense of polysemy relations in first and second language speakers of English. International Journal of Bilingualism, 23(2), 400–416. 10.1177/1367006917728396
     https://doi.org/10.1177/1367006917728396 [Google Scholar]
  12. (2019) Examining lexical development in second language learners: An approximate replication of Salsbury, Crossley & McNamara (2011). Language Teaching, 52(3), 385–405. 10.1017/S0261444817000362
     https://doi.org/10.1017/S0261444817000362 [Google Scholar]
  13. Crossley, S. A., Skalicky, S., Kyle, K., & Monteiro, K.
    (2019) Absolute frequency effects in second language lexical acquisition. Studies in Second Language Acquisition, 41(1), 721–744. 10.1017/S0272263118000268
     https://doi.org/10.1017/S0272263118000268 [Google Scholar]
  14. Davies, M.
    (2008) The corpus of contemporary American English. BYE, Brigham Young University.
     [Google Scholar]
  15. Devitto, Z., & Burgess, C.
    (2004) Theoretical and methodological implications of language experience and vocabulary skill: Priming of strongly and weakly associated words. Brain and Cognition, 55(2), 295–299. 10.1016/j.bandc.2004.02.018
     https://doi.org/10.1016/j.bandc.2004.02.018 [Google Scholar]
  16. Diependaele, K., Lemhöfer, K., & Brysbaert, M.
    (2013) The word frequency effect in first- and second-language word recognition: A lexical entrenchment account. Quarterly Journal of Experimental Psychology, 66(5), 843–863. 10.1080/17470218.2012.720994
     https://doi.org/10.1080/17470218.2012.720994 [Google Scholar]
  17. Frenck-Mestre, C., & Prince, P.
    (1997) Second language autonomy. Journal of Memory and Language, 37(4), 481–501. 10.1006/jmla.1997.2526
     https://doi.org/10.1006/jmla.1997.2526 [Google Scholar]
  18. Gollan, T. H., Montoya, R. I., Cera, C., & Sandoval, T. C.
    (2008) More use almost always means a smaller frequency effect: Aging, bilingualism, and the weaker links hypothesis. Journal of Memory and Language, 58(3), 787–814. 10.1016/j.jml.2007.07.001
     https://doi.org/10.1016/j.jml.2007.07.001 [Google Scholar]
  19. Gollan, T. H., Slattery, T. J., Goldenberg, D., Van Assche, E., Duyck, W., & Rayner, K.
    (2011) Frequency drives lexical access in reading but not in speaking: The frequency-lag hypothesis. Journal of Experimental Psychology: General, 140(2), 186–209. 10.1037/a0022256
     https://doi.org/10.1037/a0022256 [Google Scholar]
  20. Hamrick, P., & Pandža, N. B.
    (2019) Contributions of semantic and contextual diversity to the word frequency effect in L2 lexical access. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale. 74(1), 25–34. 10.1037/cep0000189
     https://doi.org/10.1037/cep0000189 [Google Scholar]
  21. Harrington, M.
    (2018) Lexical Facility. London: Palgrave Macmillan UK. 10.1057/978‑1‑137‑37262‑8
     https://doi.org/10.1057/978-1-137-37262-8 [Google Scholar]
  22. Hoffman, P., Lambon Ralph, M. A., & Rogers, T. T.
    (2013) Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words. Behavior Research Methods, 45(3), 718–730. 10.3758/s13428‑012‑0278‑x
     https://doi.org/10.3758/s13428-012-0278-x [Google Scholar]
  23. Hulstijn, J. H., Van Gelderen, A., & Schoonen, R.
    (2009) Automatization in second language acquisition: What does the coefficient of variation tell us?Applied Psycholinguistics, 30(04), 555–582. 10.1017/S0142716409990014
     https://doi.org/10.1017/S0142716409990014 [Google Scholar]
  24. Kiss, G. R., Armstrong, C., Milroy, R., & Piper, J.
    (1973) An associative thesaurus of English and its computer analysis. InA. J. Aitken, R. W. Bailey, & N. Hamilton-Smith (Eds.), The computer and literary studies (pp.153–165). Edinburgh: Edinburg University Press.
     [Google Scholar]
  25. Kyle, K., Crossley, S. A., & Berger, C. M.
    (2018) The tool for the automatic analysis of lexical sophistication (TAALES): Version 2.0. Behavior Research Methods, 50(3), 1030–1046. 10.3758/s13428‑017‑0924‑4
     https://doi.org/10.3758/s13428-017-0924-4 [Google Scholar]
  26. Lo, S., & Andrews, S.
    (2015) To transform or not to transform: Using generalized linear mixed models to analyse reaction time data. Frontiers in Psychology, 6. doi:  10.3389/fpsyg.2015.01171
     https://doi.org/10.3389/fpsyg.2015.01171 [Google Scholar]
  27. McDonald, S. A., & Shillcock, R. C.
    (2001) Rethinking the word frequency effect: The neglected role of distributional information in lexical processing. Language and Speech, 44(3), 295–322. 10.1177/00238309010440030101
     https://doi.org/10.1177/00238309010440030101 [Google Scholar]
  28. Nelson, D. L., McEvoy, C. L., & Schreiber, T. A.
    (1998) The University of South Florida word association, rhyme, and word fragment norms [Database]. Retrieved fromw3.usf.edu/FreeAssociation
     [Google Scholar]
  29. Paivio, A.
    (1990) Mental representations: A dual coding approach. Oxford University Press. 10.1093/acprof:oso/9780195066661.001.0001
     https://doi.org/10.1093/acprof:oso/9780195066661.001.0001 [Google Scholar]
  30. Ratcliff, R.
    (1993) Methods for dealing with reaction time outliers. Psychological Bulletin, 114(3), 510–532. 10.1037/0033‑2909.114.3.510
     https://doi.org/10.1037/0033-2909.114.3.510 [Google Scholar]
  31. Tokowicz, N.
    (2014) Lexical processing and second language acquisition. New York, NY: Routledge. 10.4324/9780203551387
     https://doi.org/10.4324/9780203551387 [Google Scholar]
  32. Tremblay, A., & Ransijn, J.
    (2015) LMERConvenienceFunctions: Model selection and post-hoc analysis for (G)LMER models. R Package Version 2.10. Retrieved fromhttps://CRAN.R-project.org/package=LMERConvenienceFunctions
     [Google Scholar]
  33. Van Hell, J. G., & De Groot, A. M. B.
    (1998) Conceptual representation in bilingual memory: Effects of concreteness and cognate status in word association. Bilingualism: Language and Cognition, 1(3), 193–211. 10.1017/S1366728998000352
     https://doi.org/10.1017/S1366728998000352 [Google Scholar]
  34. Whitford, V., & Titone, D.
    (2012) Second-language experience modulates first- and second-language word frequency effects: Evidence from eye movement measures of natural paragraph reading. Psychonomic Bulletin & Review, 19(1), 73–80. 10.3758/s13423‑011‑0179‑5
     https://doi.org/10.3758/s13423-011-0179-5 [Google Scholar]
  35. Yarkoni, T., Balota, D., & Yap, M.
    (2008) Moving beyond Coltheart’s N: A new measure of orthographic similarity. Psychonomic Bulletin & Review, 15(5), 971–979. 10.3758/PBR.15.5.971
     https://doi.org/10.3758/PBR.15.5.971 [Google Scholar]
/content/journals/10.1075/ml.19028.ska
Loading
Predictors of second language English lexical recognition
The Mental Lexicon 14, 333 (2019); https://doi.org/10.1075/ml.19028.ska
/content/journals/10.1075/ml.19028.ska
/content/journals/10.1075/ml.19028.ska
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): crowdsource; lexical decision task; lexical recognition; lexical semantics; linguistic features; second language

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