Learning a Language from Inconsistent Input: Regularization in Child and Adult Learners

ABSTRACT

When linguistic input contains inconsistent use of grammatical forms, children produce these forms more consistently, a process called “regularization.” Deaf children learning American Sign Language from parents who are non-native users of the language regularize their parents’ inconsistent usages. In studies of artificial languages containing inconsistently used morphemes, children, but not adults, regularized these forms. However, little is known about the precise circumstances in which such regularization occurs. In three experiments we investigate how the type of input variation and the age of learners affects regularization. Overall our results suggest that while adults tend to reproduce the inconsistencies found in their input, young children introduce regularity: they learn varying forms whose occurrence is conditioned and systematic, but they alter inconsistent variation to be more regular. Older children perform more like adults, suggesting that regularization changes with maturation and cognitive capacities.

Acknowledgments

We are grateful to all our participants and their families for making this research possible. We thank the four anonymous reviewers and the LLD editorial team, especially the Action Editor Prof Caroline Floccia, for all their help and support during the review and proofing process.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

¹ Most of the languages in, Hudson Kam & Newport (2005, 2009) contained two classes of nouns: those that took ka (or no determiner) and those that took po (or no determiner). This subcategorization of nouns might have been difficult to learn, and the variation between presence and absence of determiners was hard to score (e.g., when participants produced no determiner, was this a failure to learn the determiner system or a regularization of the “determiner absent” condition?). In the simpler language of the present experiments, all nouns in the inconsistent languages took ka at one probability and po at another.

² The actual probabilities ranged from 63%-68% for occurrence with each noun, 64%-70% for occurrence with each transitive verb, and 63%-71% for occurrence with each intransitive verb.

³ We used simple coding for all fixed effects in our models. Throughout the paper, the first level listed in the parenthetical corresponds to the reference level for that fixed effect in the model.

⁴ In all models, Day is a continuous predictor, centered on Day 4.

⁵ In this and all future experiments, entropy was calculated using the infotheo package in R.

⁶ We attempted to conduct model comparison to find the best fitting random effects structure, but the model allowing day to vary by participant (isDominant ~ 1 + (1 + Day | Subject)) resulted in a singular fit

⁷ We applied Bonferroni correction here to correct for multiple comparisons.

⁸ We releveled the model with older children as the reference and applied Bonferroni correction here to correct for multiple comparisons.

⁹ Adult learners do not regularize substantially here, although Hudson Kam & Newport (2009) found that adults did regularize more when presented with a 60-scatter distribution. There are two important differences between the scatter distribution of Experiment 3 and that in Hudson Kam & Newport: Here the majority determiner was only present 40% of the time (rather than 60%), and here there was also a 20% minority determiner.

Additional information

Funding

This research was funded by the Canadian Department of Foreign Affairs and International Trade, Canadian Commonwealth Scholarship Program [2005-2010]. This research was also supported in part by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA grant agreement n° [PCIG11-GA-2012-322005].