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.

基于使用的理论提出，包含少量高频样本的输入可以促进语言结构的学习，即所谓的倾斜（或齐普夫）输入分布。早期的实证研究为这一想法提供了支持，但随后的 L2 研究提供了不同的结果。然而，以前的方法尚未探讨认知特征（例如工作记忆）对倾斜或平衡输入的有效性的影响。这里报告的实验测试了学习者开发新的 L2 形容词类别的能力，这些形容词指导西班牙语动词“成为”的词汇选择。结果表明，当提供明确的规则时，低工作记忆的学习者受益于倾斜输入的变异性的减少，而高工作记忆的学习者受益于平衡输入，这更好地允许基于规则的假设检验。这些发现有助于澄清之前研究中的混合结果，并提出了一种根据个体特征优化 L2 输入的方法。