Newport Elissa L
Georgetown University.
Lang Cogn. 2016 Sep;8(3):447-461. doi: 10.1017/langcog.2016.20. Epub 2016 Jul 28.
Our research on statistical language learning shows that infants, young children, and adults can compute, online and with remarkable speed, how consistently sounds co-occur, how frequently words occur in similar contexts, and the like, and can utilize these statistics to find candidate words in a speech stream, discover grammatical categories, and acquire simple syntactic structure in miniature languages. However, statistical learning is not merely learning the patterns presented in the input. When their input is inconsistent, children sharpen these statistics and produce a more systematic language than the one to which they are exposed. When input languages inconsistently violate tendencies that are widespread in human languages, learners shift these languages to be more aligned with language universals, and children do so much more than adults. These processes explain why children acquire language (and other patterns) more effectively than adults, and also may explain how systematic language structures emerge in communities where usages are varied and inconsistent. Most especially, they suggest that usage-based learning approaches must account for differences between adults and children in how usage properties are acquired, and must also account for substantial changes made by adult and child learners in how input usage properties are represented during learning.
我们对统计语言学习的研究表明，婴儿、幼儿和成人能够在线且以惊人的速度计算声音同时出现的一致性、单词在相似语境中出现的频率等，并能利用这些统计数据在语音流中找到候选单词、发现语法类别以及在微型语言中习得简单的句法结构。然而，统计学习不仅仅是学习输入中呈现的模式。当输入不一致时，儿童会强化这些统计数据，并创造出一种比他们所接触的语言更具系统性的语言。当输入语言不一致地违背人类语言中普遍存在的倾向时，学习者会将这些语言转变为更符合语言共性的形式，而且儿童比成人做得更多。这些过程解释了为什么儿童比成人更有效地习得语言（以及其他模式），也可能解释了在用法多样且不一致的社群中系统的语言结构是如何出现的。最特别的是，它们表明基于用法的学习方法必须考虑成人和儿童在获取用法属性方式上的差异，还必须考虑成人和儿童学习者在学习过程中对输入用法属性的表征方式所做的重大改变。
