The computational BIA+ model (Dijkstra & Van Heuven, 2002) has provided a useful account for bilingual word recognition, while the verbal (pre-quantitative) RHM (Kroll & Stewart, 1994) has often served as a reference framework for bilingual word production and translation. According to Brysbaert and Duyck (2010), a strong need is felt for a unified implemented account of bilingual word comprehension, lexical-semantic processing, and word production. With this goal in mind, we built a localist-connectionist model, called Multilink, which integrates basic assumptions of both BIA+ and RHM. It simulates the recognition and production of cognates (form-similar translation equivalents) and non-cognates of different lengths and frequencies in tasks like monolingual and bilingual lexical decision, word naming, and word translation production. It also considers effects of lexical similarity, cognate status, relative L2-proficiency, and translation direction. Model-to-model comparisons show that Multilink provides higher correlations with empirical data than both IA and BIA+ models.

Whereas most theoretical and computational models assume a continuous flow of activation from concepts to lexical items in spoken word production, one prominent model assumes that the mapping of concepts onto words happens in a discrete fashion (Bloem & La Heij, 2003). Semantic facilitation of context pictures on word translation has been taken to support the discrete-flow model. Here, we report results of computer simulations with the continuous-flow WEAVER++ model (Roelofs, 1992, 2006) demonstrating that the empirical observation taken to be in favor of discrete models is, in fact, only consistent with those models and equally compatible with more continuous models of word production by monolingual and bilingual speakers. Continuous models are specifically and independently supported by other empirical evidence on the effect of context pictures on native word production.