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When regularization gets it wrong: children over-simplify language input only in production

Published online by Cambridge University Press:  21 February 2018

Jessica F. SCHWAB ,
Casey LEW-WILLIAMS  and
Adele E. GOLDBERG
Jessica F. SCHWAB*
Affiliation:
Department of Psychology, Peretsman-Scully Hall, Princeton University, Princeton, NJ 08540
Casey LEW-WILLIAMS
Affiliation:
Department of Psychology, Peretsman-Scully Hall, Princeton University, Princeton, NJ 08540
Adele E. GOLDBERG
Affiliation:
Department of Psychology, Peretsman-Scully Hall, Princeton University, Princeton, NJ 08540
*
*Corresponding author. Email: [email protected]
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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 classifier. 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.

Keywords

language acquisitiongeneralizationprobability boosting
Type
Articles
Information
Journal of Child Language , Volume 45 , Issue 5 , September 2018 , pp. 1054 - 1072
Copyright
Copyright © Cambridge University Press 2018 

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