Simulating cross-language competition with the bilingual interactive activation model

The BIA model is an implemented language-nonselective access model for bili ngual word recognition which assumes an integrated lexicon for words of dif ferent languages. In this paper, simulations with the BIA model are present ed that account for the effects of lexical competitors from the same and an other language on target word recognition in two different paradigms. First , the model simulates recent lexical decision results obtained with French/ English bilinguals in two masked orthographic priming experiments (Bijeljac -Babie, Biardeau, gr Grainger, 1997). Differences in the result patterns fo r subject groups varying in second-language (L2) proficiency (monolinguals, beginning, and more advanced bilinguals) are simulated by assuming a reduc ed frequency range for L2-words. In demonstrating that the monolingual part icipants in this study very likely knew some L2-words, the model displays c onsiderable heuristic value, Second, the BIA model simulates the effects of intra- and interlingual neighborhood density on target word recognition in blocked and mixed progressive demasking (PDM) with Dutch/English bilingual s (Van Heuven, Dijkstra, & Grainger, 1998). The model accounts for a shift in result patterns observed in high-proficiency subjects over parts of the blocked PDM experiment by varying the degree of asymmetric cross-language i nhibition from language node to word level. Finally, the model simulates th e over-all result pattern in the mixed PDM experiment by manipulation of bo th cross-language inhibition and the frequency range for L2-items. A compar ison with some other nonselective and selective access models indicates tha t the BIA model produces better overall simulation results because the two mechanisms allow the model to capture the asymmetric cross-language effects observed in unbalanced bilingual participants.