Cover not available
Article published In:
The Mental Lexicon
Vol. 14:3 (2019) ► pp.333–356

Predictors of second language English lexical recognition

Further insights from a large database of second language lexical decision times

| Victoria University of Wellington
| Georgia State University
| Duolingo
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.
Keywords: second language, crowdsource, lexical decision task, lexical recognition, linguistic features, lexical semantics
Article outline
  • L2 lexical processing and development
  • Linguistic features and L2 lexical processing
    • Contextual diversity and distinctiveness
    • Neighborhoods
    • Word concreteness
  • The Berger at al. (2019) Study
  • Research questions
  • Method
    • Online data collection
    • Language use survey
    • Data preparation and variable selection
      • Linguistic features
      • Language use survey answers
      • Coefficient of variation
      • Accuracy and reaction time outliers
      • Multicollinearity
    • Statistical analysis
  • Results
    • Participant data
    • Lexical decision time data
    • Linear mixed effects regression model: Reaction times
  • Discussion
    • Significant interactions with OLD20
  • Conclusion
  • Notes
  • References
Published online: 13 May 2020
DOI logo
https://doi.org/10.1075/ml.19028.ska
References (35)
References
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. DOI logoGoogle Scholar
Andrews, S. (1997). The effect of orthographic similarity on lexical retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin & Review, 4(4), 439–461. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Baayen, R. H., & Milin, P. (2015). Analyzing reaction times. International Journal of Psychological Research, 3(2), 12–28. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logo.Google Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Cop, U., Keuleers, E., Drieghe, D., & Duyck, W. (2015). Frequency effects in monolingual and bilingual natural reading. Psychonomic Bulletin & Review, 22(5), 1216–1234. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
(2019). Examining lexical development in second language learners: An approximate replication of Salsbury, Crossley & McNamara (2011). Language Teaching, 52(3), 385–405. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Davies, M. (2008). The corpus of contemporary American English. BYE, Brigham Young University.Google Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Frenck-Mestre, C., & Prince, P. (1997). Second language autonomy. Journal of Memory and Language, 37(4), 481–501. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Harrington, M. (2018). Lexical Facility. London: Palgrave Macmillan UK. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Kiss, G. R., Armstrong, C., Milroy, R., & Piper, J. (1973). An associative thesaurus of English and its computer analysis. In A. J. Aitken, R. W. Bailey, & N. Hamilton-Smith (Eds.), The computer and literary studies (pp. 153–165). Edinburgh: Edinburg University Press.Google Scholar
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. DOI logoGoogle Scholar
Lo, S., & Andrews, S. (2015). To transform or not to transform: Using generalized linear mixed models to analyse reaction time data. Frontiers in Psychology, 61. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (1998). The University of South Florida word association, rhyme, and word fragment norms [Database]. Retrieved from [URL]
Paivio, A. (1990). Mental representations: A dual coding approach. Oxford University Press. DOI logoGoogle Scholar
Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114(3), 510–532. DOI logoGoogle Scholar
Tokowicz, N. (2014). Lexical processing and second language acquisition. New York, NY: Routledge. DOI logoGoogle Scholar
Tremblay, A., & Ransijn, J. (2015). LMERConvenienceFunctions: Model selection and post-hoc analysis for (G)LMER models. R Package Version 2.10. Retrieved from [URL]
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
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. DOI logoGoogle Scholar
Cited by (6)

Cited by six other publications

Wild, Heather Ann & Victor Kuperman
2024. Word learning in the wild: App-based evidence for valence and concreteness effects. Applied Psycholinguistics ► pp. 1 ff. DOI logo
Alzahrani, Alaa
2023. LexArabic: A receptive vocabulary size test to estimate Arabic proficiency. Behavior Research Methods 56:6 ► pp. 5529 ff. DOI logo
Monteiro, Kátia, Scott Crossley, Robert-Mihai Botarleanu & Mihai Dascălu
2023. L2 and L1 semantic context indices as automated measures of lexical sophistication. Language Testing 40:3 ► pp. 576 ff. DOI logo
Porkoláb, Ádám & Tamás Fekete
2023. A mesterséges intelligencia alkalmazása a nyelvtanulásban. Iskolakultúra 33:8 ► pp. 67 ff. DOI logo
Yang, Yi & Yue Song
2022. Exploring the similarity between Han’s and non-Han’s Yuan poetry: Resistance distance metrics over character co-occurrence networks. Digital Scholarship in the Humanities 37:3 ► pp. 880 ff. DOI logo
Wang, Jue & Baoguo Chen
2020. A Database of Chinese-English Bilingual Speakers: Ratings of the Age of Acquisition and Familiarity. Frontiers in Psychology 11 DOI logo

This list is based on CrossRef data as of 25 october 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.