Big data suggest strong constraints of linguistic similarity on adult language learning

Abstract

When adults learn new languages, their speech often remains noticeably non-native even after years of exposure. These non-native variants (‘accents’) can have far-reaching socio-economic consequences for learners. Many factors have been found to contribute to a learners’ proficiency in the new language. Here we examine a factor that is outside of the control of the learner, linguistic similarities between the learner’s native language (L1) and the new language (Ln). We analyze the (open access) speaking proficiencies of about 50,000 Ln learners of Dutch with 62 diverse L1s. We find that a learner’s L1 accounts for 9–22% of the variance in Ln speaking proficiency. This corresponds to 28–69% of the variance explained by a model with controls for other factors known to affect language learning, such as education, age of acquisition and length of exposure. We also find that almost 80% of the effect of L1 can be explained by combining measures of phonological, morphological, and lexical similarity between the L1 and the Ln. These results highlight the constraints that a learner’s native language imposes on language learning, and inform theories of L1-to-Ln transfer during Ln learning and use. As predicted by some proposals, we also find that L1-Ln phonological similarity is better captured when subcategorical properties (phonological features) are considered in the calculation of phonological similarities.