Simulating cross-language priming with a dynamic computational model of the lexicon

Abstract

Cross-language priming is a widely used experimental paradigm in psycholinguistics to study how bilinguals’ two languages are represented and organized. Researchers have observed a number of interesting patterns from the priming effects of both translation equivalents and semantically related word pairs across languages. In this study, we implement a self-organizing neural network model, DevLex–II, to simulate these two types of priming effects across Chinese and English. Specifically, our model incorporates a computational mechanism for simulating spreading activation based on the distance between bilingual words in the semantic space. The model also considers additional factors that modulate priming effects, such as the initial activation level of the prime words and the degree to which the target word can be recognized. Our model reveals differences with respect to the priming effects as a function of bilingual type (early versus late L2 learners), directions of priming (L1 to L2 versus L2 to L1), and types of priming (translation versus semantic priming). These simulated differences are compared with empirical findings from previous studies and discussed in the light of interactive and developmental theories of bilingual lexical representation.