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In this paper, we present a computational approach to bilingual speakers’ non-native (L2) lexical-semantic representations. Specifically, based on detailed analyses of the error patterns shown in our previous simulation results ( Zhao and Li Int. J. Bilingual. Educ. Bilingual., 2010, 13, 505–524 ;  Zhao and Li, Bilingualism, 2013, 16, 288–303 ), we aim at revealing the underlying learning factors that may affect the extent of fuzzy category boundaries within bilinguals’ L2 representation. Here, we first review computational bilingual models in the literature that have focused on simulating L2 lexical representations, including the Developmental Lexicon II (DevLex-II) model ( Zhao and Li, Int. J. Bilingual. Educ. Bilingual., 2010, 13, 505–524 ;  Zhao and Li, Bilingualism, 2013, 16, 288–303 ), on which the current study is based. The DevLex-II modeling results indicate a strong age of acquisition (AoA) effect: When the learning of L2 is early relative to that of native language (L1), functionally distinct lexical representations may be established for both languages; when the learning of L2 is significantly delayed relative to that of L1, fuzzy L2 representations may occur due to the structural consolidation (or the  entrenchment ) of the L1 lexicon. Next, we explore the error patterns shown in both lexical comprehension and production in DevLex-II. A novel contribution of the current study is that we systematically compare the computational simulation results with empirical findings. Such model-based error analyses extend our previous findings by indicating, especially in the late L2 learning condition, that fuzzy L2 semantic representations emerge and lead to processing errors, including errors in unstable phonology-semantic and semantic-phonemic mappings. The DevLex-II model provides a computational account of the development of bilinguals’ L2 representation with reference to the dynamic interaction and competition between the two lexicons. We point to future directions in which fuzzy L2 representations may be overcome, through a framework that highlights the social learning of L2 (SL2) and the embodied semantic representation of the lexicon in the new language ( Li and Jeong, Npj Sci. Learn., 2020, 5, 1–9 ;  Zhang, Yang, Wang and Li, Lang. Cogn. Neurosci., 2020, 35, 1223–1238 ).

Fuzzy or Clear? A Computational Approach Towards Dynamic L2 Lexical-Semantic Representation