# Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks

arXiv: https://arxiv.org/abs/2605.20529 · HTML: https://mlsanity.com/paper/2605.20529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 16 of 16)

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- [Manipulating language models' training data to study syntactic constraint learning: the case of English passivization](https://mlsanity.com/paper/2407.04593/) (2024)
- [Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models](https://mlsanity.com/paper/2505.23689/) (2025)
- [Function Words as Statistical Cues for Language Learning](https://mlsanity.com/paper/2601.21191/) (2026)

## Abstract (arXiv)

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. Taken together, these results suggest that collocational bootstrapping is a viable learning strategy for the type of input that children receive.