How semantic biases in simple adjacencies affect learning a complex structure with non-adjacencies in AGL: a statistical account

摘要

A major theoretical debate in language acquisition research regards the learnability of hierarchical structures. The artificial grammar learning methodology is increasingly influential in approaching this question. Studies using an artificial centre-embedded A"B" grammar without semantics draw conflicting conclusions. This study investigates the facilitating effect of distributional biases in simple AB adjacencies in the input sample-caused in natural languages, among others, by semantic biases-on learning a centre-embedded structure. A mathematical simulation of the linguistic input and the learning, comparing various distributional biases in AB pairs, suggests that strong distributional biases might help us to grasp the complex A~nB~n hierarchical structure in a later stage. This theoretical investigation might contribute to our understanding of how distributional features of the input-including those caused by semantic variation-help learning complex structures in natural languages.