Artificial Grammar Learning in Children With Developmental Language Disorder.

Abstract

The purpose of the study was to evaluate implicit learning in children with developmental language disorder (DLD) by employing a visual artificial grammar learning task. Thirteen children with DLD and 24 children with typical language development between the ages of 8 and 12 years completed a visual artificial grammar learning task. During the training phase of the task, participants were presented with strings of shapes that followed the underlying structure of a finite grammar. During the testing phase, participants were asked to judge whether new strings were grammatical or nongrammatical. Grammatical judgment of new strings served to measure generalization of the underlying grammatical structure. Endorsement based on chunk strength, or similarity to training exemplars, served to evaluate the extent to which children relied on surface features to guide their task performance. As a group, children with typical development performed better on the artificial grammar learning task, compared with children with DLD, and accepted more grammatical strings regardless of their similarity to training exemplars. Task performance in both groups was not affected by surface features. Performance of children with DLD whose test accuracy exceeded the learning threshold of 0.5 was consistent with a generalization of the underlying grammatical structure that was unaffected by surface features. The study found group differences in learning outcomes between children with and without DLD. Consistent with previous reports, children with typical development correctly endorsed more grammatical strings than children with DLD, suggesting a better acquisition of the grammatical structure. However, there was no evidence to suggest that children in the DLD group (learners and nonlearners) relied on surface features (i.e., familiarity to training exemplars) in their grammatical judgment. These results refute our hypothesis that children in the DLD group would show judgment based on familiarity.