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This study compared the comprehension of syntactically simple with more complex sentences in Italian-English adult bilinguals and monolingual controls in the presence or absence of sentence-level interference. The task was to identify the agent of the sentence and we primarily examined the accuracy of response. The target sentence was signalled by the gender of the speaker, either a male or a female, and this varied over trials, where the target was spoken in a male voice the distractor was spoken in a female voice and vice versa. In contrast to other work showing a bilingual disadvantage in sentence comprehension under conditions of noise, we show that in this task, where voice permits selection of the target, adult bilingual speakers are in fact better able than their monolingual Italian peers to resist sentence-level interference when comprehension demands are high. Within bilingual speakers we also found that degree of proficiency in English correlated with the ability to resist interference for complex sentences both when the target and distractor were in Italian and when the target was in English and the distractor in Italian. (Contain 1 footnote, 3 tables, and 4 figures.)

The target article represents a significant advance in the level of sophistication applied to models of bilingual word recognition, and Dijkstra and van Heuven are to be congratulated on this endeavour. Bearing in mind the success of the (computational) BIA model in capturing detailed patterns of experimental data, I look forward to future simulation results from the BIA+ when the proposals of this new framework are implemented. It is an essential step to draw a distinction between recognition systems and the decision mechanisms that drive responses, and the authors have provided a novel way of apportioning empirical evidence of context effects in bilingual word recognition across this divide. Given the explanatory weight now being placed on decision mechanisms rather than the word recognition system itself, perhaps indeed it is now time to make some simplifying assumptions about the recognition system and start building detailed computational models of the decision component of the system. Implementation will provide the clarity of theorisation and evaluation of theory viability that have been the hallmark of the BIA model thus far.