Statistical learning in language acquisition: Difference between revisions
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'''Statistical learning''' is the ability for humans to extract commonalities and transitional probabilities of the world around them to learn about the environment.<ref name=Turk-Browne et al.>{{cite journal|last=Turk-Browne|first=Nicholas B.|coauthors=Junge, J.A., Scholl, B.J.|title=The Automaticity of Visual Statistical Learning|journal=Journal of Experimental Psychology: General|year=2005|volume=134|issue=4|pages=552-564|doi=10.1037/0096-3445.134.4.552}}</ref> 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 paper by Jenny Saffran and colleagues,<ref>{{cite journal|last=Saffran|first=Jenny R.|coauthors=Aslin, R.A., Newport, E.L.|title=Statistical learning by 8-month-old infants|journal=Science|date=131|year=1996|month=December|volume=274|pages=1926-1928}}</ref> in which 8-month-old infants were presented with nonsense streams of monotonous speech. Each stream was composed of four three-syllable “words” that were repeated randomly. After exposure to the speech streams for two minutes, infants reacted differently to hearing “words” 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 extract transitional probabilities of language even with very limited exposure. This method of learning is thought to be one way that children locate word boundaries in streams of continuous speech. |
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==References== |
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