实验室成员陈小聪、董燕萍、余秀芬近日在Behavior Research Methods（影响因子为3.623）上发表文章，文章标题为On the predictive validity of various corpus-based frequency norms in L2 English lexical processing。
Lab members Xiaocong Chen, Yanping Dong and Xiufen Yu recently published a paper in Behavior Research Methods (impact factor 3.623). The paper is entitled "On the predictive validity of various corpus-based frequency norms in L2 English lexical processing", which can be found at the following link: http://rdcu.be/ESVT.
Abstract The predictive validity of various corpus-based frequency norms in first-language lexical processing has been intensively investigated in previous research, but less attention has been paid to this issue in second-language (L2) processing. To bridge the gap, in the present study we took English as a case in point and compared the predictive power of a large set of corpus-based frequency norms for the performance of an L2 English visual lexical decision task (LDT). Our results showed that, in general, the frequency norms from SUBTLEX-US and WorldLex–Blog tended to predict L2 performance better in reaction times, whereas the frequency norms from corpora with a mixture of written and spoken genres (CELEX, WorldLex–Blog, BNC, ANC, and COCA) tended to predict L2 accuracy better. Although replicated in both low- and high-proficiency L2 English learners, these patterns were not exactly the same as those found in LDT data from native English speakers. In addition, we only observed some limited advantages of the lemma frequency and contextual diversity measures over the wordform frequency measure in predicting L2 lexical processing. The results of the present study, especially the detailed comparisons among the different corpora, provide methodological implications for future L2 lexical research.