A growing body of research on early word learning suggeststhat learners gather word-object co-occurrence statistics acrosslearning situations. Here we test a new mechanism wherebylearners are also sensitive to word-word co-occurrence statis-tics. Indeed, we find that participants can infer the likely ref-erent of a novel word based on its co-occurrence with otherwords, in a way that mimics a machine learning algorithmdubbed ‘zero-shot learning’. We suggest that the interactionbetween referential and distributional regularities can bring ro-bustness to the process of word acquisition.
