Testing the Universal Grammar Hypothesis

语言学理论中最具争议的主张之一是存在语言学习的归纳法问题：儿童在学习语言时接触到的数据不足以产生儿童最终获得的语言知识。这个问题的一个解决方案是，儿童天生就有一些专门关于语言的知识，即普遍语法(UG)，这允许他们从可用的数据中假设正确的语言知识。最近语言学习文献中的争论既质疑归纳法问题的存在，也质疑否定普遍语法存在的可能性。该项目通过借鉴实验和计算技术的最新进展，并结合理论句法、心理语言学实验和贝叶斯建模等方面，为这两个方面指定了具体的方法。该项目的第一阶段量化了儿童对各种语言现象的投入，使用儿童指导的言语的抄本来准确评估儿童遇到某些结构的频率。第二阶段量化儿童获得的语言知识，使用实验性的句法技术来衡量成人的语言知识。潜在的诱导问题的标志是，在儿童指导的言语中出现频率相对较低的结构被发现相对可以被成年人接受，因为这意味着儿童必须决定结构是他们语言的一部分，而不是经常(或有时根本不会)遇到它。该项目的第三阶段使用心理动机的贝叶斯语言学习模型来评估解决这些潜在的归纳问题所需的条件，以及重要的是，语言特有的知识是否是这样做的必要知识。该项目从UG倡导者明确同意必须是UG的一部分的一系列现象开始寻找归纳问题：对远距离依赖的限制，也称为岛屿限制。本项目提供了一种解决语言和学习中的核心辩论之一的方法，并演示了如何将这种方法应用于语言理论的核心复杂现象，如岛屿限制。通过将语言学的理论和实验工作与计算模型相结合，该项目将产生这些子领域中每个子领域无法单独实现的结果，并影响语言习得的理论句法和计算模型的方法论规范。随着这项研究细化对语言学习的理解和必然的先天知识，该项目有望激发UCI内部和整个领域内跨理论、实验和计算的传统部门的富有成效的合作。反过来，这将帮助我们了解儿童如何克服语言学习中固有的困难。该项目的另一个特点是创建了一个会话语音数据库，其中明确标记了理论感兴趣的结构，这无疑将是理论语言学家和计算模型师在未来几年的宝贵研究工具。这一工具的使用将促进对语言学理论核心现象的卓有成效的科学研究，这些现象以前由于缺乏可用数据而很难或不可能做到。此外，该项目将研究和教育融为一体，每年向本科生提供语言学方面的实践研究经验，从而促进从一所大型公立大学的经济和种族多样化的学生中招聘未来的语言学家。

One of the most controversial claims in linguistic theory is the existence of an induction problem for language learning: the data that children are exposed to in learning language is inadequate to yield the knowledge of language children eventually attain. One solution to this problem is that children are born with some knowledge specifically about language, known as Universal Grammar (UG), that allows them to hypothesize the correct linguistic knowledge from the available data. Recent debates in the language learning literature have questioned both the existence of induction problems and the possibility of ever disproving the existence of UG. This project specifies a concrete methodology for doing both of these, by drawing from recent advances in experimental and computational technologies, and incorporating aspects of theoretical syntax, psycholinguistic experimentation, and Bayesian modeling. The first stage of the project quantifies the input available to children for various linguistic phenomena, using transcripts of child-directed speech to assess exactly how often children encounter certain constructions. The second stage quantifies the knowledge of language children attain, using experimental syntax techniques to gauge adult linguistic knowledge. The mark of a potential induction problem is when a construction that appears relatively infrequently in child-directed speech is found to be relatively acceptable for adults, since this means children must decide that construction is part of their language without encountering it very often (or sometimes, at all). The third stage of the project uses psychologically-motivated Bayesian models of language learning to evaluate what is required to solve these potential induction problems, and importantly whether language-specific knowledge is necessary to do so. The project begins the search for induction problems with a set of phenomena that UG advocates unequivocally agree must be part of UG: constraints on long distance dependencies, also known as island constraints.This project provides a methodology for addressing one of the central debates in language and learning and demonstrates how to apply this methodology to complex phenomena central to linguistic theory, such as island constraints. By connecting theoretical and experimental work in linguistics with computational modeling, this project will yield results not achievable from each of these sub-fields individually and impact the methodological norms in both theoretical syntax and computational models of language acquisition. As this research refines understanding of language learning and necessarily innate knowledge, this project will hopefully inspire productive collaboration across the traditional divides of theory, experimentation, and computation, both within UCI and across the field as a whole. This, in turn, will help us understand how children overcome the difficulties inherent in language learning. An additional feature of the project is the creation of a database of conversational speech that has structures of theoretical interest explicitly marked, which will undoubtedly be a valuable research tool for both theoretical linguists and computational modelers for years to come. The availability of this tool will stimulate fruitful scientific research on phenomena central to linguistic theory that has been difficult or impossible to do previously, due to lack of available data. Moreover, this project integrates research and education by providing hands-on research experience in linguistics to undergraduate students each year, thereby fueling the recruitment of future linguists from the economically and ethnically diverse population of students at a large public university.