Growing trees: The acquisition of the left periphery

2021 
We suggest here a Growing Trees approach for the description of the acquisition of various syntactic structures in Hebrew, based on the main results reported in Friedmann and Reznick (this volume) and on our own research on a corpus of natural productions. The heart of our account is that stages of acquisition follow the geometry of the syntactic tree, along the lines of the cartographic analysis of the clause, with early stages of acquisition corresponding to small portions of the adult syntactic tree, which keeps growing with the growth of the child. The lower parts of the tree are acquired first, and higher parts are acquired later. We propose three stages of acquisition connected to the development of functional layers of the syntactic tree. In the first stage, the IP is acquired, including the lexical and inflectional layers. This allows for the appearance of A-movement structures, including SV/VS alternations with unaccusative verbs, alongside SV sentences with unergative/transitive verbs. The second stage involves the acquisition of the lower part of the left periphery, up to QP, which allows for the acquisition of subject and object Wh questions, some adjunct questions, yes/no questions, and sentence-initial adverbs. In the third stage, the rich structure of the left periphery is completely acquired, including the higher CP field. This is the stage in which sentential embedding (of finite declarative and interrogative clauses), subject and object relative clauses, why questions, and topicalization appear. A further, different type of stage, which occurs on the already-grown tree and which is independent of structure building, is the acquisition of intervention configurations, allowing for the mastery of structures involving movement of a lexically-restricted object across an intervening lexically-restricted subject. The paper illustrates the fruitful dialogue between the science of syntax acquisition and the cartography of syntactic structures.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    73
    References
    0
    Citations
    NaN
    KQI
    []