๐ค AI Summary
This study investigates the developmental sequence of syntactic category emergence in child first-language acquisition to evaluate competing predictions from โbottom-upโ (GROWING) and โinside-outโ (INWARD) maturational theories. By employing a statistical grammar induction approach under controlled conditions of fixed linguistic input and a uniform learning algorithm, the work operationalizes these opposing theoretical frameworks into computationally explicit, comparable models of staged syntactic acquisition, thereby offering a precise characterization of category emergence. Experimental results consistently demonstrate across three evaluation metrics that the GROWING model significantly outperforms the INWARD model, lending empirical support to the view that lexical and inflectional structures develop earlier in acquisition. This research establishes a novel computational paradigm for modeling the trajectory of syntactic development in language acquisition.
๐ Abstract
This paper is concerned with what intermediate syntactic categories children acquire during first language development, and in what order. Maturational theories make different predictions. Bottom-up accounts (GROWING) propose that lexical and inflectional structure emerges first, while inward accounts (INWARD) predict early access to discourse-related categories. We computationally operationalise these hypotheses of staged syntactic emergence using statistical grammar induction, asking what each proposed ordering makes learnable when input and learning algorithm are held constant. Our framework makes category acquisition explicit and allows us to explore how different maturational orderings shape the structure that can be learned under identical conditions. Based on this operationalisation, the GROWING account significantly outperforms the INWARD account across three evaluation metrics.