Adaptation as statistical learning: An individual differences study




Enochson, Kelly

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A rich body of research has shown that language learners can track and use distributional information in the input to acquire multiple levels of linguistic structure (see krogh et al., 2013 for a review). There is reason to believe, however, that learning about the statistics of the input is not confined to language acquisition, but is part of ongoing language experience. In particular, language processing appears to be influenced by expectations—e.g., about probable sounds, words, structures—which are dynamic and can be rapidly updated based on the current linguistic environment (e.g., fine et al., 2013). If a general mechanism like statistical learning underlies both acquisition and later processing and use, a clear prediction is made: performance on an independent measure of statistical learning should correlate with ability to adapt native language expectations based on novel information.



Linguistics, Adaptation, Amazon Mechanical Turk, Psycholinguistics, Statistical Learning