The purpose of the proposed research is to provide a comprehensive account of the factors that affect how infants, children, and adults learn the categories of their native language from distributional information in linguistic input. The categories of a language consist of sets of words (e.g., noun, verb) that play a functionally equivalent role in grammatical sentences. Distributional information refers to the patterning of elements in a large corpus of sentences and includes how frequently those elements occur, what position they occupy in a sentence, and the context provided by neighboring elements. Our longstanding program of research on statistical learning in word segmentation (how learners determine which sound sequences form words) has documented the power, rapidity, and robustness of infants, children, and adults sensitivity to complex distributional information. Here we extend that program of research to a crucial aspect of learning higher-level structures of language. In our proposed studies, we use a miniature artificial language paradigm that affords us complete control over all the distributional cues in the input, something that is virtually impossible using real languages. Participants listen to a sample of utterances and make judgments about their acceptability. Crucially, during a learning phase, they do not hear all possible utterances that are """"""""legal"""""""" in the artificial language;some are withheld for use in a later post-test. The post-test utterances either conform to the distributional patterns present in the learning phase, or they violate those patterns. The key test is whether participants judge novel-but-legal utterances to be acceptable, thereby showing the ability to generalize correctly beyond the input to which they were exposed. Studies of children provide additional support for learning the distributional cues by pairing utterances with videos of simple events. Studies of adults will be used for comparison, and will also present them with learning materials in the visual-motor domain to assess the detailed time-course of learning and the specificity of the results to auditory linguistic materials. Taken together, the results of these studies of infants, children, and adults will document the key structural variables in language learning that enable a distributional mechanism of category formation to operate and will highlight the ways these mechanisms may differ over age and domain.

Public Health Relevance

Language development is one of the hallmarks of the human species, yet it is difficult to study because of the huge variation in early exposure to different amounts of linguistic input. The use of artificial languages that are acquired in the lab over a few hours provides a window on the mechanisms of language development. We will study language learning in the lab to gain a unique perspective on how the categories (noun, verb, etc) are formed from listening to the patterns of words in a small set of sentences. These studies will not only reveal a basic mechanism of language learning, but also establish benchmarks against which language delay can be compared. Moreover, understanding the mechanisms that lead to successful acquisition in normal children can help to identify loci of language disorders and design methods for remediating disorders.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
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Language and Communication Study Section (LCOM)
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Mccardle, Peggy D
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University of Rochester
Other Basic Sciences
Schools of Arts and Sciences
United States
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Aslin, Richard N (2017) Statistical learning: a powerful mechanism that operates by mere exposure. Wiley Interdiscip Rev Cogn Sci 8:
Bergelson, Elika; Aslin, Richard (2017) Semantic Specificity in One-Year-Olds' Word Comprehension. Lang Learn Dev 13:481-501
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Bergelson, Elika; Aslin, Richard N (2017) Nature and origins of the lexicon in 6-mo-olds. Proc Natl Acad Sci U S A 114:12916-12921
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