The learner as statistician: three principles of computational success in language acquisition.
The learner as statistician: three principles of computational success in language acquisition.
复制标题
作为统计学家的学习者:语言习得中计算成功的三个原则。
DOI:
10.1111/j.1467-7687.2009.00827.x
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发表时间:
2009
影响因子:
3.7
通讯作者:
Morgan,James
中科院分区:
文献类型:
--
作者:
Soderstrom,Melanie;Conwell,Erin;Feldman,Naomi;Morgan,James
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, inter alia).