The long-term aim of the proposed research is to provide an account of how children learn the grammatical structure of their native language from distributional information in linguistic input, and also how these learning mechanisms may differ from those of adult learners. Distributional information is the patterning of elements in a large corpus of sentences. We hypothesize that learners acquire aspects of language structure from the statistics arising from this distributional information, such as which elements co-occur, what positions they regularly occupy in a word or sentence, and with what neighboring elements they frequently occur. Our program of research to date has focused on word segmentation (how learners determine which sound sequences form words) and on word categories (how learners determine which words form grammatical categories such as noun and verb). This work has documented the power and robustness of infants?, children?s, and adults? ability to use complex distributional information to discover these aspects of language. We now propose to extend our research in new directions, to examine two crucial aspects of learning higher- level linguistic structure. In Part 1 we study the factors that lead learners to generalize a novel inflectional morpheme (like ?s for noun plurals) to novel words. In Part 2 we examine how learners acquire phrases and simple hierarchical structure in sentences, and we ask what leads learners to prefer the types of phrase and hierarchical structures that are most common in natural languages. In our proposed studies we test our hypotheses using miniature artificial language paradigms that afford control over the distributional cues in the input, something that is virtually impossible using only data from natural language learning. In each experiment, participants listen to utterances in a miniature language and then produce their own utterances or make judgments about their acceptability. Crucially, during the learning phase they hear only a sample of the possible utterances that are legal in the artificial language; some are withheld for use in a later post-test, to determine whether learners generalize what they have observed to novel instances (and if so, to which types of novel instances). We have developed highly successful paradigms for engaging young children in miniature language studies, and we have demonstrated important differences between child and adult language learners in these studies. We will also present children and adults with comparable learning paradigms in the visual-motor domain, to assess the time-course of the learning process and the specificity or generality of the results using auditory linguistic materials. Taken together, the results of these studies will document the key variables that enable a distributional learning mechanism to acquire the structure of words (inflectional morphology) and sentences (phrase and hierarchical structure) and will highlight the ways these mechanisms may differ over age and stimulus domain.
The study of age effects in language acquisition can help to determine the timing of optimal language input for bilingual children, deaf children, and children with communicative disabilities. Our findings on statistical learning of words, word categories, morphology, and sentence structure are also highly relevant to understanding language disorders, and our paradigms are widely used for identifying and treating children with difficulties, delays, and disorders of language acquisition. As research in language acquisition has moved from measuring stages of acquisition to understanding the processes by which languages are learned, our proposed studies will make important contributions to understanding where these processes break down and how principles of statistical learning can be used for treatment and rehabilitation.
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