The role of word-word co-occurrence in word learning

A growing body of research on early word learning suggests that learners gather word-object co-occurrence statistics across learning situations. Here we test a new mechanism whereby learners are also sensitive to word-word co-occurrence statistics. Indeed, we find that participants can infer the likely referent of a novel word based on its co-occurrence with other words, in a way that mimics a machine learning algorithm dubbed ‘zero-shot learning’. We suggest that the interaction between referential and distributional regularities can bring robustness to the process of word acquisition.