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 in individuals with developmental language disorder.
Proposed statistical learning deficits in individuals with developmental language delay (DLD) are thought to have downstream effects related to poorer comprehension, but this relationship has not been experimentally shown. In this project, children and adults with and without DLD and their same-age typically developing (TD) peers complete an artificial grammar learning task that employs a made-up language and an online comprehension task that employs real language. In the artificial grammar learning task, participants are tested to determine if they have learned the statistical regularities of trained stimuli and formed categories based upon these regularities. We hypothesize that if individuals with DLD have difficulty utilizing distributional information from novel input, then they will show less evidence of forming new categories than TD peers. Our second hypothesis is that if regularities are learned based on experience, then adults and children will show similar learning because they will have the same exposure to the artificial 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 interpret a certain way due to linguistic experience. We hypothesize that if individuals with DLD have overall poorer linguistic experience compared to TD individuals, then they will show weaker effects of biases than peers. Finally, we use measurements from both tasks to verify correlation between them, for the additional goal of showing that language comprehension and statistical learning are related. This study provides information about differences between individuals with DLD and their TD peers and between adults and children in the ability to use distributional information from both accumulated and novel input. To this end, we reveal the role of input and experience in using distributional information in linguistic environments.