Acquiring Recursive Structures through Distributional Learning

Autor/inn/en	Li, Daoxin; Schuler, Kathryn D.
Titel	Acquiring Recursive Structures through Distributional Learning
Quelle	In: Language Acquisition: A Journal of Developmental Linguistics, 30 (2023) 3-4, S.323-336 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Li, Daoxin) ORCID (Schuler, Kathryn D.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1048-9223
DOI	10.1080/10489223.2023.2185522
Schlagwörter	Language Acquisition; Linguistic Input; Artificial Languages; Learning Processes; Sentence Structure; Comparative Analysis; Grammar; Adults; English; Native Language; Second Language Learning; Language Tests; Raw Scores + Suchen Sie Ihr Suchwort? Sprachaneignung; Spracherwerb; Sprachbildung; Learning process; Lernprozess; Satzbau; Satzstruktur; Grammatik; English language; Englisch; Zweitsprachenerwerb; Language test; Sprachtest
Abstract	Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a single language, some structures allow infinite self-embedding while others are more restricted. For example, when expressing ownership relation, English allows infinite embedding of the prenominal genitive "-s," whereas the postnominal genitive "of" is much more restricted. How do speakers learn which specific structures allow infinite embedding and which do not? The distributional learning proposal suggests that the recursion of a structure (e.g., X[subscript 1]'s-X[subscript 2]) is licensed if the X[subscript 1] position and the X[subscript 2] position are productively substitutable in non-recursive input. The present study tests this proposal with an artificial language learning experiment. We exposed adult participants to X[subscript 1]-ka-X[subscript 2] strings. In the productive condition, almost all words attested in X[subscript 1] position were also attested in X[subscript 2] position; in the unproductive condition, only some were. We found that, as predicted, participants from the productive condition were more likely to accept unattested strings at both one- and two-embedding levels than participants from the unproductive condition. Our results suggest that speakers can use distributional information at one-embedding level to learn whether or not a structure is recursive. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01