Optimizing the input for learning of L2-specific constructions: The roles of Zipfian and balanced input, explicit rules and working memory

Abstract

Usage-based theory has proposed that learning of linguistic constructions is facilitated by input that contains few high-frequency exemplars, in what is known as a skewed (or Zipfian) input distribution. Early empirical work provided support to this idea, but subsequent L2 research has provided mixed findings. However, previous approaches have not explored the impact that cognitive traits (e.g., working memory) have on the effectiveness of skewed or balanced input. The experiment reported here tested learners' ability to develop new L2 categories of adjectives that guide lexical selection in Spanish verbs of becoming. The results showed that, when explicit rules are provided, low-working memory learners benefitted from reduced variability in skewed input, while high-working memory individuals benefitted from balanced input, which better allows for rule-based hypothesis testing. The findings help clarify the mixed findings in previous studies and suggest a way forward for optimizing the L2 input based on individual traits.