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Published online by Cambridge University Press:  29 April 2019

Cynthia Berger*
Georgia State University
Scott Crossley
Georgia State University
Stephen Skalicky
Georgia State University
*Correspondence concerning this article should be addressed to Cynthia Berger. E-mail:


A large dataset of word recognition behavior from nonnative speakers (NNS) of English was collected using an online crowdsourced lexical decision task. Lexical features were used to predict NNS lexical decision latencies and accuracies. Predictors of NNS latencies and accuracy included contextual diversity, age of acquisition, and contextual distinctiveness, while length moderated the impact of contextual diversity and neighborhood size on accuracy. Results have implications for second language word recognition and demonstrate that NNS behavioral data collected through large crowdsourcing projects can afford a rich source for SLA research.

Research Article
Copyright © Cambridge University Press 2019 

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