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When children learn more than what they are taught : regularization in child and adult learners

URL to cite or link to: http://hdl.handle.net/1802/12686

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Thesis (Ph. D.)--University of Rochester. Dept. of Brain & Cognitive Sciences, 2010.
When linguistic input provides inconsistent evidence for grammatical structures, children tend to regularize. For example, children learning languages from parents who are imperfect users of the language regularize their parents’ inconsistent usages (Singleton & Newport, 2004). Previous studies (Hudson Kam & Newport, 2005, 2009) have examined this phenomenon using artificial languages and have found that children, but not adults, regularized this type of variable input. However, little is known about precisely how and when such regularization occurs. In a series of 5 experiments, we investigate how various aspects of the distribution of the input probabilities affect such behavior, as well as whether this behavior varies by age. Adults and children were exposed for 5 days to one of five artificial languages. Languages differed in the probability with which determiners were used. After exposure, subjects produced novel sentences; determiner use was scored. The first two experiments investigated the role of the type of variability present in the input. When taught a language with unpredictable, inconsistent variability, five- and six- year old children regularized the variability in their own language productions, always using the most frequent determiner more often than occurred during exposure. When taught a language with predictable, lexically consistent variability, children did not regularize, but instead learned the pattern responsible for the variability, and used this pattern in their own productions. In both cases, adult’s productions matched those present in their input. The second set of experiments investigated which feature of the distribution children are sensitive to, as well as whether this effect varies by age. Adults and 5-8 year old children were exposed to one of three artificial languages, differing in the distribution of determiners. Each language had a dominant determiner that was used more frequently than any of the other determiners, and a minority determiner that was used less often. The probability with which these determiners were used, was systematically varied such that a given probability of usage (40%) was the dominant determiner in one language, but the minority determiner in the other. In line with previous results, the 5-6 year old children in these experiments regularized the dominant determiner in their productions; adult productions reflected the probabilities present in their input. Children ages 7-8 regularized less than younger children, but more than adults. This suggests that the tendency to regularize begins to drop off at an early age, but does so gradually. Moreover, though a determiner is used equally frequently across two languages (40%), children regularize it only when it is the dominant determiner. It is thus the relationships between alternating forms – and not simple frequency – that affects the youngest learners.
Contributor(s):
Alison C. Austin (1980 - ) - Author

Elissa L. Newport - Thesis Advisor

Primary Item Type:
Thesis
Language:
English
Subject Keywords:
Language acquisition; Language development; Artificial language; Child language learning; Determiners
Sponsor - Description:
David & Lucile Packard Foundation -
National Institutes of Health (NIH) - Grant DC00167 to Elissa L. Newport and Ted Supalla; Training Grant T32 MH019942 to the University of Rochester
Office of Naval Research (ONR) - MURI-ONR
First presented to the public:
9/27/2010
Originally created:
2010
Original Publication Date:
2010
Previously Published By:
University of Rochester
Citation:
Extents:
Number of Pages - x, 98 leaves
License Grantor / Date Granted:
Marcy Strong / 2010-09-27 10:19:38.211 ( View License )
Date Deposited
2010-09-27 10:19:38.211
Date Last Updated
2012-09-26 16:35:14.586719
Submitter:
Marcy Strong

Copyright © This item is protected by copyright, with all rights reserved.

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