Using Lexical Features to Investigate Second Language Lexical Decision Performance.

Abstract

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.