Exploring language acquisition in children with a miniature artificial language: Effects of item and pattern frequency, arbitrary subclasses, and correction
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Very little of the artificial language experimentation in the literature has been done with children, although children are the primary target population to which experimenters wish to generalize. For this reason, we used children (7–10 years old) to explore the acquisition of a miniature artificial language with rules, patterns, subpatterns, and exceptions that are quite like those found in inflectional systems of natural languages. The first goal of the study was to explore two aspects of linguistic input—how frequency of presentation affects the relative roles of rote and rule or pattern learning, and the learnability of arbitrary subclasses. A second goal was to determine the effects of immediate corrective feedback on acquisition; and a third goal was to examine adult-child differences in learning. Overwhelmingly, the children learned via rules rather than by rote, but failed to distinguish the arbitrary subclasses. Frequency of presentation had specific effects on the acquisition of items and patterns. The immediacy of feedback had no significant effect on learning. Although there was a small (though significant) difference between the performance of the children and that of some adult subjects, overall, the results support the existence of a class of pattern learning skills that is common to children and adults and includes some aspects of syntax acquisition.
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In L1 acquisition, high token frequency surface forms are produced earlier and more accurately than other forms and words inflected in lower token frequency forms.
“In L1 acquisition, high token frequency surface forms are produced earlier and more accurately in those forms compared to production in other forms and compared to production of other words that are inflected in lower token frequency forms (Aguado-Orea, 2004;Braine et al., 1990;Finley, 2018;Marchman, 1997).”
From Effects of Availability, Contingency, and Formulaicity on the Accuracy of English Grammatical Morphemes in Second Language Writing (2022) · cites this paper as Braine et al., 1990
The Discussion (page not available in our copy) reports that high-frequency suffixes were learned faster than low-frequency suffixes. The Children, Generalization Phase results also report greater accuracy for primary endings, but individual noun–suffix frequency improved ending accuracy only in a limited condition. Because the children learned a miniature artificial language, these results do not establish the claim as a general pattern of L1 acquisition. (LLM, conf=0.88)
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Murakami, A., & Ellis, N. C. (2022) · Language Learning · doi:10.1111/lang.12500 · Cited by 26 · see citing works
- Authors
- Braine, M. D. S., Brody, R. E., Brooks, P. J., Sudhalter, V., Ross, J. A., Catalano, L., & Fisch, S. M.
- Affiliations
- New York University (US)
- Corresponding author
- Braine, Martin D.S
- ORCID
- Brooks, Patricia J
- Venue
- Journal of Memory and Language
- Publisher
- Elsevier BV
- Published
- 1990-10
- Volume
- 29
- Issue
- 5
- Pages
- 591-610
- ISSN
- 0749-596X (ISSN-L)
- OpenAlex
- W2054180118
- Cited by
- 111
- References
- 1
- Citations by year
- 2026: 3 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 7 · 2020: 6 · 2019: 3 · 2018: 8 · 2017: 5 · 2016: 3 · 2015: 3 · 2014: 4 · 2013: 6 · 2012: 3
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Memory and Language (publishedVersion, cc-by-nc-nd)
- Funders
- York University
- National Institute of Child Health and Human Development
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- artificial language learning, child language acquisition, pattern frequency, item frequency, corrective feedback, rule induction, rote learning, syntax acquisition, inflectional morphology, language input
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