Statistical learning experiments have demonstrated that children and infants are sensitive to the types of statistical regularities found in natural language. These experiments often rely on statistical information based on linear dependencies, e.g. that x predicts y either immediately or after some intervening items, whereas learning to creatively use language relies on the ability to form grammatical categories (e.g. verbs, nouns) that share distributions. Distributional learning has not been explored in children or individuals with language impairment. The proposed research can reveal new findings regarding language acquisition and use in these populations. Proposed statistical learning deficits in individuals with language impairment (LI) are thought to have downstream effects causing poorer comprehension, but this relationship has not been experimentally shown. In this project, children with and without LI and their typically developing (TD) peers will complete an online comprehension task that employs natural language and an artificial grammar learning task that employs a made-up language. In the online comprehension task, participants use a computer mouse to choose a preferred interpretation of a sentence that is ambiguous, but that most adults would interpret a certain way due to the distributional properties of the verb, an effect termed verb bias. It has not been shown whether individuals LI are sensitive to verb bias effects, but we predict children with LI will be less sensitive than peers on the basis of previous work showing deficits with verb use and overall poorer linguistic experience in this population. In the artificial grammar learning task, participants will be tested to determine if they have learned the statistical regularities of trained stimuli and formed categories based upon these regularities. We predict TD participants will form more robust categories. It has not been shown whether individuals with LI are worse at utilizing distributional information from novel input, but poor performance on other statistical learning tasks by this population suggests a deficit. We will use measurements from both tasks to verify a relationship between them, for the additional goal of showing that language comprehension and statistical learning are related. This study will provide information about differences between children with LI and their TD peers in the ability to use distributional information from both accumulated and novel input. To this end, we will discover the role of input and experience in using distributional information in linguistic environments.
The proposed project will provide information about the extent to which children with and without language impairment utilize distributional information in the input, specifically as regards to language. Problems with language learning are correlated with long-term academic and social difficulties, and a better understanding of how statistical information like words? distribution is processed and utilized by individuals with language impairment can shed insight into the role of the input in language learning and use. This information could lead to intervention techniques that manipulate distributional information in order to facilitate language development and improve comprehension in children with language impairment.
| Hall, Jessica; Owen VAN Horne, Amanda; Farmer, Thomas (2018) Distributional learning aids linguistic category formation in school-age children. J Child Lang 45:717-735 |
| Hall, Jessica; Owen Van Horne, Amanda J; McGregor, Karla K et al. (2018) Individual and Developmental Differences in Distributional Learning. Lang Speech Hear Serv Sch 49:694-709 |
| Hall, Jessica; Owen Van Horne, Amanda; McGregor, Karla K et al. (2017) Distributional Learning in College Students With Developmental Language Disorder. J Speech Lang Hear Res 60:3270-3283 |
