Computational models of bilingual comprehension

This chapter reviews current computational models of bilingual word recognition. It begins with a discussion of the role of computational modeling in advancing psychological theories, highlighting the way in which the choice of modeling paradigm can influence the type of empirical phenomena to which the model is applied. The chapter then introduces two principal types of connectionist model that have been employed in the bilingual domain, localist and distributed architectures. Two main sections then assess each of these approaches. Localist models are predominantly addressed towards explaining the processing structures in the adult bilingual. Here we evaluate several models including BIA, BIMOLA, and SOPHIA. Distributed models are predominantly addressed towards explaining issues of language acquisition and language loss. This section includes discussion of BSN, BSRN, and SOMBIP. Overall, the aim of current computational models is to account for the circumstances under which the bilingual’s two languages appear to interfere with each other during recognition (for better or worse) and those circumstances under which the languages appear to operate independently. Based on the range of models available in the unilingual literature, our conclusion is that computational models have great potential in advancing our understanding of the principal issues in bilingualism, but that thus far only a few of these models have seen extension to the bilingual domain