Linguistics Program, English Department, George Mason University, Fairfax, VA 22030.
Proc Natl Acad Sci U S A. 2014 Apr 22;111(16):5842-7. doi: 10.1073/pnas.1320525111. Epub 2014 Mar 31.
Although it is widely agreed that learning the syntax of natural languages involves acquiring structure-dependent rules, recent work on acquisition has nevertheless attempted to characterize the outcome of learning primarily in terms of statistical generalizations about surface distributional information. In this paper we investigate whether surface statistical knowledge or structural knowledge of English is used to infer properties of a novel language under conditions of impoverished input. We expose learners to artificial-language patterns that are equally consistent with two possible underlying grammars--one more similar to English in terms of the linear ordering of words, the other more similar on abstract structural grounds. We show that learners' grammatical inferences overwhelmingly favor structural similarity over preservation of superficial order. Importantly, the relevant shared structure can be characterized in terms of a universal preference for isomorphism in the mapping from meanings to utterances. Whereas previous empirical support for this universal has been based entirely on data from cross-linguistic language samples, our results suggest it may reflect a deep property of the human cognitive system--a property that, together with other structure-sensitive principles, constrains the acquisition of linguistic knowledge.
虽然人们普遍认为学习自然语言的语法涉及到获取依赖于结构的规则，但最近关于习得的研究仍试图主要根据关于表面分布信息的统计概括来描述学习的结果。在本文中，我们研究了在输入匮乏的情况下，是否使用表面统计知识或英语的结构知识来推断一种新语言的属性。我们让学习者接触到与两种可能的基础语法同样一致的人工语言模式——一种在词的线性顺序上与英语更相似，另一种在抽象结构上更相似。我们表明，学习者的语法推断压倒性地倾向于结构相似性，而不是保持表面顺序。重要的是，相关的共享结构可以用从意义到话语映射的同构偏好来描述。虽然以前基于跨语言语言样本的数据完全支持这种普遍性，但我们的结果表明，它可能反映了人类认知系统的一个深层特性——一种与其他结构敏感原则一起限制语言知识习得的特性。
