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The learner as statistician: three principles of computational success in language acquisition

The learner as statistician: three principles of computational success in language acquisition Statistical learning is the new paradigm of language acquisition. A perusal of recent conference programs or journal contents reveals much work advocating – or criticizing – statistical learning. Language acquisition will continue to benefit from a variety of theories and methods, but, as the articles in this issue exemplify, statistical learning has progressed from being a minor player towards a central role. To ensure a lasting impact, statistical approaches must now move from piecemeal demonstrations towards a general theory of language learning. Statistical learning stands in contrast to the predominant paradigm that it succeeded. The principles and parameters approach ( Chomsky, 1981 ) assumed rich innate endowment, limited processing abilities and impoverished input, whereas statistical learning assumes that input is rich and that learners possess sufficient computational sophistication to extract relevant linguistic patterns. Statistical learning models are attractive because in principle they recruit powerful, task‐general machinery to solve difficult problems of language acquisition. Furthermore, behavioural findings with both adults and infants suggest that humans use statistical learning in language‐like tasks ( Gómez, 2002 ; Goodsitt, Morgan & Kuhl, 1992 ; Maye, Werker & Gerken, 2002 ; Saffran, Aslin & Newport, 1996 , Saffran, Newport & Aslin, 1996 , http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Developmental Science Wiley

The learner as statistician: three principles of computational success in language acquisition

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References (25)

Publisher
Wiley
Copyright
Copyright © 2009 Wiley Subscription Services
ISSN
1363-755X
eISSN
1467-7687
DOI
10.1111/j.1467-7687.2009.00827.x
pmid
19371364
Publisher site
See Article on Publisher Site

Abstract

Statistical learning is the new paradigm of language acquisition. A perusal of recent conference programs or journal contents reveals much work advocating – or criticizing – statistical learning. Language acquisition will continue to benefit from a variety of theories and methods, but, as the articles in this issue exemplify, statistical learning has progressed from being a minor player towards a central role. To ensure a lasting impact, statistical approaches must now move from piecemeal demonstrations towards a general theory of language learning. Statistical learning stands in contrast to the predominant paradigm that it succeeded. The principles and parameters approach ( Chomsky, 1981 ) assumed rich innate endowment, limited processing abilities and impoverished input, whereas statistical learning assumes that input is rich and that learners possess sufficient computational sophistication to extract relevant linguistic patterns. Statistical learning models are attractive because in principle they recruit powerful, task‐general machinery to solve difficult problems of language acquisition. Furthermore, behavioural findings with both adults and infants suggest that humans use statistical learning in language‐like tasks ( Gómez, 2002 ; Goodsitt, Morgan & Kuhl, 1992 ; Maye, Werker & Gerken, 2002 ; Saffran, Aslin & Newport, 1996 , Saffran, Newport & Aslin, 1996 ,

Journal

Developmental ScienceWiley

Published: Jan 1, 2009

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