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Acquiring Recursive Structures through Distributional Learning

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Τίτλος: Acquiring Recursive Structures through Distributional Learning
Γλώσσα: English
Συγγραφείς: Li, Daoxin (ORCID 0009-0004-6846-9689), Schuler, Kathryn D. (ORCID 0000-0003-2962-731X)
Πηγή: Language Acquisition: A Journal of Developmental Linguistics. 2023 30(3-4):323-336.
Διαθεσιμότητα: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 14
Ημερομηνία έκδοσης: 2023
Τύπος εγγράφου: Journal Articles
Reports - Research
Descriptors: Language Acquisition, Linguistic Input, Artificial Languages, Learning Processes, Sentence Structure, Comparative Analysis, Grammar, Adults, English, Native Language, Second Language Learning, Language Tests, Raw Scores
DOI: 10.1080/10489223.2023.2185522
ISSN: 1048-9223
1532-7817
Περίληψη: 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.
Abstractor: As Provided
Entry Date: 2023
Αριθμός Καταχώρησης: EJ1390906
Βάση Δεδομένων: ERIC
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 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.
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