This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

The use of loanwords is generally attributed to a social feature, like social prestige, and to semantic features, like the need to fill a lexical gap. However, few studies take into account variation in the use of loanwords within a speech community, and directly compare the frequency of loanwords from more than one source language. This paper contributes to research on lexical borrowing by comparing the distribution of loanwords from three different source languages in two large databases of dialect data. We take an onomasiological perspective, which allows us to gauge the frequency of borrowed lexical items vis-à-vis alternative expressions. Using Generalized Additive Mixed Modeling, we show that the usage of loanwords can only be explained by taking into account the interaction between semantics and geographical diffusion. Our analysis confirms that the patterns that occur almost exclusively reflect changes in socio-cultural history.