Proceedings of the Annual Meeting of the Cognitive Science Society

Some researchers claim that language acquisition is critically dependent on experiencing linguistic input in order of in-creasing complexity. We tested this hypothesis using a simple recurrent neural network (SRN) trained to predict wordsequences in CHILDES, a 5-million-word corpus of speech directed to children. First, we demonstrated that age-orderedCHILDES exhibits a gradual increase in linguistic complexity. Next, we compared the performance of two groups ofSRNs trained on CHILDES which had either been age-ordered or not. Specifically, we assessed learning of grammaticaland semantic structure and showed that training on age-ordered input facilitates learning of semantic, but not of sequentialstructure. Follow-up analyses suggest that higher noun-density in speech to younger children combined with weight en-trenchment could account for this effect. The persistent learning improvement is consistent with the neural commitmenthypothesis in the second language acquisition literature, which asserts that L1 representation reduces neural resourcesavailable for L2 learning. Similarly, exposure to noun-rich input first but not last (age-ordered CHILDES), may induce arepresentational advantage for lexical semantic acquisition.