A Simple Recurrent Network Model of Bilingual Memory

This paper draws on previous research that strongly suggests that bilingual memory is organized as a single distributed lexicon rather than as two separately accessible lexicons corresponding to each language. Interactive-activation models provide an effective means of modeling many of the cross-language priming and interference effects that have been observed. However, one difficulty with these models is that they do not provide a plausible way of actually cquiring such an organization. This paper shows that a simple recurrent connectionist network (SRN) (Elman, 1990) might provide an insight into this problem. An SRN is first trained on two micro-languages and the hidden-unit representations corresponding to those languages are studied. A cluster analysis of these highly distributed, overlapping representations shows that they accurately reflect the overall separation of the two languages, as well as the word categories in each language. In addition, random and extensive lesioning of the SRN hidden layer is shown, in general, to have little effect on this organization. This is in general agreement with the observation that most bilinguals who suffer brain damage do not lose their ability to distinguish their two languages. On the other hand, an example is given where the removal of a single node does radically disrupts this internal representational organization, similar to rare clinical cases of bilingual language mixing and bilingual aphasia following brain trauma. The issue of scaling-up is also discussed briefly.