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Taken together, these results suggest that collocational bootstrapping is a viabl","title":"Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks","url":"https://arxiv.org/abs/2605.20529","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.20529v1 Announce Type: cross \nAbstract: In what ways might statistical signals in linguistic input assist with the acquisition of syntax? Here we hypothesize a mechanism called collocational bootstrapping, in which regularities in word co-occurrence patterns can provide cues to syntactic dependencies. We investigate whether this mechanism can support the acquisition of English subject-verb agreement. First, we simulate language acquisition by training neural networks on synthetic datasets that vary in how predictable their subject-verb pairings are. We find that there is a range of variability levels at which these statistical learners robustly learn subject-verb agreement. We then analyze the variability of subject-verb pairings in child-directed language, and we find that the variability in such data falls within the range that supported robust generalization in our computational simulations. 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