Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks
The article presents a hypothesis called collocational bootstrapping, which explores how statistical signals in language input can aid in learning subject-verb agreement. Researchers simulated language acquisition using neural networks and found that variability in subject-verb pairings supports robust learning. The findings suggest that collocational bootstrapping is a viable strategy for children acquiring language.
- ▪The hypothesis focuses on how word co-occurrence patterns can provide cues to syntactic dependencies.
- ▪Neural networks were trained on synthetic datasets to simulate language acquisition.
- ▪The variability in child-directed language aligns with the levels that supported robust generalization in simulations.
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Computer Science > Computation and Language arXiv:2605.20529 (cs) [Submitted on 19 May 2026] Title:Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks Authors:Claire Hobbs, R. Thomas McCoy View a PDF of the paper titled Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks, by Claire Hobbs and R. Thomas McCoy View PDF HTML (experimental) Abstract: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.
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