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A Self-Organizing Connectionist Model of Bili ngual Processing

Current connectionist models of bili ngual language processing have been largely restricted to localist stationary models. To fully capture the dynamics of bili ngual processing, we present SOMBIP, a self-organizing model of bili ngual processing that has learning characteristics. SOMBIP consists of two interconnected selforganizing neural networks, coupled with a recurrent neural network that computes lexical co-occurrence constraints. Simulations with our model indicate that (1) the model can account for distinct patterns of the bili ngual lexicon without the use of language nodes or language tags, (2) it can develop meaningful lexical-semantic categories through self-organizing processes, and (3) it can account for a variety of priming and interference effects based on associative pathways between phonology and semantics in the lexicon, and (4) it can explain lexical representation in bili nguals with different levels of proficiency and working memory capacity. These capabiliti es of our model are due to its design characteristics in that (a) it combines localist and distributed properties of processing, (b) it combines representation and learning, and (c) it combines lexicon and sentences in bili ngual processing. Thus, SOMBIP serves as a new model of bili ngual processing and provides a new perspective on connectionist bili ngualism. It has the potential of explaining a wide variety of empirical and theoretical issues in bili ngual research.