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- Statistical learning is the ability for humans and other animals to extract statistical regularities from the world around them to learn about the environment. Although statistical learning is now thought to be a generalized learning mechanism, the phenomenon was first identified in human infant language acquisition. The earliest evidence for these statistical learning abilities comes from a study by Jenny Saffran, Richard Aslin, and Elissa Newport, in which 8-month-old infants were presented with nonsense streams of monotone speech. Each stream was composed of four three-syllable "pseudowords" that were repeated randomly. After exposure to the speech streams for two minutes, infants reacted differently to hearing "pseudowords" as opposed to "nonwords" from the speech stream, where nonwords were composed of the same syllables that the infants had been exposed to, but in a different order. This suggests that infants are able to learn statistical relationships between syllables even with very limited exposure to a language. That is, infants learn which syllables are always paired together and which ones only occur together relatively rarely, suggesting that they are parts of two different units. This method of learning is thought to be one way that children learn which groups of syllables form individual words. Since the initial discovery of the role of statistical learning in lexical acquisition, the same mechanism has been proposed for elements of phonological acquisition, and syntactical acquisition, as well as in non-linguistic domains. Further research has also indicated that statistical learning is likely a domain-general and even species-general learning mechanism, occurring for visual as well as auditory information, and in both primates and non-primates. (en)
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