Modeling color naming in bilinguals: Computational mechanisms of crosslinguistic influence

When bilingual speakers name stimuli such as colors or objects, their naming patterns can differ from those of monolingual speakers. Three accounts have been proposed to explain these differences – conceptual change, online lexical coactivation, and L1 footprint – yet these have not been empirically evaluated against each other. In this study, we propose a novel computational cognitive model which operationalizes each of these proposals as a mechanism of crosslinguistic influence, such that we can study their individual and combined effects on the model’s behavior. We focus on the domain of color in which we model existing experimental data collected from Navajo and English monolinguals and Navajo–English bilinguals. Our color learning model extends a statistical learning procedure for mixture models to the acquisition of labelled categories, and achieves bilingual learning by maintaining two sets of color categories and associated color words, which are connected in varying ways according to the three crosslinguistic mechanisms. We test the combinations of mechanisms in a color naming task, and analyze the match between the naming patterns of the model and the differences between bilingual and monolingual human speakers. Our results suggest that gradual conceptual change following crosslinguistic transfer at the initial learning stage can best capture the observed differences in human color naming patterns. While lexical coactivation combined with initial transfer can account for some of the empirical data, this mechanism consistently performs less well than that of conceptual change.