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date: 15 October 2019

Abstract and Keywords

How well does statistical learning address the challenges of real-world language learning? This chapter presents progress in the domain of statistical learning since Saffran et al. (1996) conducted their seminal work. It highlights the extension of statistical learning to acquisition of natural language, and reviews investigations of how infants and adults segment words from speech, acquire word forms, and form abstract grammatical categories through tracking of transitional probabilities and non-adjacent dependencies. It also focuses on research demonstrating the influence of individual differences on statistical learning ability along with neuroimaging studies that reveal cognitive processes supporting statistical learning. The chapter ends by suggesting avenues of research that would further extend the application of statistical learning to natural language acquisition.

Keywords: statistical learning, transitional probability, non-adjacent dependency, grammatical category acquisition, individual differences, language acquisition

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