LAUSR

Significance A difficult problem in describing language acquisition is knowing when children go beyond their input to produce novel, structured utterances—that is, to achieve linguistic productivity, the hallmark of human language. We address this problem by detailing onsets and trajectories of 64 English-learning children producing determiner–noun combinations (the dog, a dog) and by capturing these behaviors with a computational model. Because we know the model’s input, we can determine when it predicts combinations not in its training set. We find parallels between child and model in the timing of novel combinations, suggesting productivity in the children. Marrying behavioral observations and computational modeling provides an approach that can be used to assess productivity in any language, spoken or signed.