Abstract and Keywords
This article explains role of statistical learning in understanding language development and the interaction between human learning mechanisms and human languages. Infants' statistical learning abilities were first investigated to understand the mechanisms used to segment words from fluent speech, an important first step in acquiring new words. Studies of the connection between statistical learning and lexical acquisition examined regularities in adjacent elements. Statistical learning must be capable of additional levels of analysis if it is a significant component of syntactic development. Recent investigations demonstrate that human learners are not limited to simple adjacent probabilities, but seem to track the types of patterns necessary to exploit distributional cues to syntactic structures. Studies of statistical learning of syntax indicate that the powerful learning capacities include important constraints that help to address the problem of the potentially overburdened distributional learner. The constraints or biases also appear to act on non-linguistic input such as computer alert sounds, and visual nonsense shapes. The constraints that are highly suited to discovering linguistic structure may not be specific to language, but general characteristics of human learning. Statistical learning accounts of language acquisition present an alternative to the traditional innate universal grammar explanation for syntactic acquisition and for the similarities in organization across the world's languages.
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