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When Regularization Gets It Wrong: Children Over-Simplify Language Input Only in Production

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Title: When Regularization Gets It Wrong: Children Over-Simplify Language Input Only in Production
Language: English
Authors: Schwab, Jessica F., Lew-Williams, Casey, Goldberg, Adele E.
Source: Journal of Child Language. Sep 2018 45(5):1054-1072.
Availability: Cambridge University Press. 100 Brook Hill Drive, West Nyack, NY 10994. Tel: 800-872-7423; Tel: 845-353-7500; Fax: 845-353-4141; e-mail: subscriptions_newyork@cambridge.org; Web site: https://journals.cambridge.org
Peer Reviewed: Y
Page Count: 19
Publication Date: 2018
Document Type: Journal Articles
Reports - Research
Descriptors: Language Acquisition, Linguistic Input, Language Processing, Semantics, Generalization, Task Analysis, Linguistic Performance, Expressive Language
DOI: 10.1017/S0305000918000041
ISSN: 0305-0009
Abstract: Children tend to regularize their productions when exposed to artificial languages, an advantageous response to unpredictable variation. But generalizations in natural languages are typically conditioned by factors that children ultimately learn. In two experiments, adult and six-year-old learners witnessed two novel classifiers, probabilistically conditioned by semantics. Whereas adults displayed high accuracy in their productions -- applying the semantic criteria to familiar and novel items -- children were oblivious to the semantic conditioning. Instead, children regularized their productions, over-relying on only one classier. However, in a two-alternative forced-choice task, children's performance revealed greater respect for the system's complexity: they selected both classifiers equally, without bias toward one or the other, and displayed better accuracy on familiar items. Given that natural languages are conditioned by multiple factors that children successfully learn, we suggest that their tendency to simplify in production stems from retrieval difficulty when a complex system has not yet been fully learned.
Abstractor: As Provided
Number of References: 32
Entry Date: 2018
Accession Number: EJ1188326
Database: ERIC
FullText Text:
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  Data: When Regularization Gets It Wrong: Children Over-Simplify Language Input Only in Production
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Child+Language%22"><i>Journal of Child Language</i></searchLink>. Sep 2018 45(5):1054-1072.
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  Data: Cambridge University Press. 100 Brook Hill Drive, West Nyack, NY 10994. Tel: 800-872-7423; Tel: 845-353-7500; Fax: 845-353-4141; e-mail: subscriptions_newyork@cambridge.org; Web site: https://journals.cambridge.org
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  Data: 19
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  Data: <searchLink fieldCode="DE" term="%22Language+Acquisition%22">Language Acquisition</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistic+Input%22">Linguistic Input</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Processing%22">Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistic+Performance%22">Linguistic Performance</searchLink><br /><searchLink fieldCode="DE" term="%22Expressive+Language%22">Expressive Language</searchLink>
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  Data: 10.1017/S0305000918000041
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  Data: 0305-0009
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  Data: Children tend to regularize their productions when exposed to artificial languages, an advantageous response to unpredictable variation. But generalizations in natural languages are typically conditioned by factors that children ultimately learn. In two experiments, adult and six-year-old learners witnessed two novel classifiers, probabilistically conditioned by semantics. Whereas adults displayed high accuracy in their productions -- applying the semantic criteria to familiar and novel items -- children were oblivious to the semantic conditioning. Instead, children regularized their productions, over-relying on only one classier. However, in a two-alternative forced-choice task, children's performance revealed greater respect for the system's complexity: they selected both classifiers equally, without bias toward one or the other, and displayed better accuracy on familiar items. Given that natural languages are conditioned by multiple factors that children successfully learn, we suggest that their tendency to simplify in production stems from retrieval difficulty when a complex system has not yet been fully learned.
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  Data: 2018
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