Doctoral Dissertation Research: Statistical Learning of Predictive Dependencies of Tense-Aspect System in the Artificial Language by English and Thai L1 Adults

博士论文研究：英语和泰语 L1 成人人工语言中时态系统预测依赖性的统计学习

• 批准号: 1844445
• 负责人: Lourdes Ortega
• 金额: $ 1.45万
• 依托单位: Georgetown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844445&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Statistical; Learning

While many adults are keen to learn a second (L2) or additional (Ln) language because of academic, economic, or cultural reasons, their success may not come as easily as that when they acquired their first language (L1) as children. One of the reasons for this variable success is prior linguistic experience from different L1s. Moreover, the presence of multilingualism, that many adults will learn several languages over their lifetime, makes it harder to assess prior knowledge. Learning a language means becoming attuned to and developing expectations for systematic linguistic patterns, from sound sequences to grammatical relations. Successfully learning a novel language requires that adults abstract new systematic patterns from the input while using existing ones from their learned language(s). This research will help us understand language prediction and multilingual learning processes.This dissertation research project employs an artificial grammar learning paradigm to investigate how a particular kind of probabilistic systematic pattern, predictive dependencies, in adults' existing tense-aspect system(s) can promote or prevent adaptation to novel tense-aspect regularities. Adult participants from two L1 backgrounds, English and Thai, will learn a miniature language that expresses temporal meaning involving both English- and Thai-analogous predictive dependencies at different levels: between lexical and sub-lexical items (e.g., walk+ed) for English and between lexical items (e.g., a pair of aspect markers such as sed leew ["already completed"]) for Thai. Because the same meaning in the miniature language is equally likely to appear with both sets of dependencies, this research will investigate (1) whether participants are better able to learn predictive dependencies that are consistent with their L1-analogous dependencies and (2) whether the findings will be true under bilingual learning conditions for the English participants, who have perfect knowledge of one set of dependencies and zero knowledge of the other, and under trilingual learning conditions for the Thai-English participants, who have both sets of dependencies available for use during learning but with varying degrees of statistical precision in their L2, English.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

尽管由于学术，经济或文化原因，许多成年人渴望学习第二种（L2）或其他（LN）语言，但他们的成功可能不会像孩子那样获得第一语言（L1）时那样容易。这种可变成功的原因之一是不同L1的语言经验。此外，许多成年人一生都会学到多种语言的多语言主义的存在，这使得更难评估先验知识。学习语言意味着要对系统语言模式（从声音序列到语法关系）进行调整和发展。成功学习一种新的语言要求成年人从输入中抽象新的系统模式，同时使用他们学到的语言的现有语言。这项研究将有助于我们了解语言预测和多语言学习过程。本论文研究项目采用人工语法学习范式来研究成人现有的紧张态度系统中特定类型的概率系统模式，预测依赖性如何促进或防止适应新型时态规律。来自两个L1背景的成年参与者，英语和泰语，将学习一种微型语言，表达时间含义，涉及不同级别的英语和泰式预测依赖性：在词汇和亚文化项目（例如，步行+ED）之间在词汇项目（例如，泰语中的一对诸如SED余地[已经完成的]之类的方面标记）之间。因为在微型语言中同样有可能与两组依赖关系一起出现相同的含义，所以这项研究将调查（1）参与者是否能够更好地学习与其L1-Analogous依赖性一致的预测依赖性和（2）在双语学习条件下，对英国参与者的发现将是正确的，他们对一套依赖关系有完美的了解，对另一种依赖性有零知识，以及在泰国 - 英语参与者的三语学习条件下，他们都有两种可供使用的依赖项在学习期间，但在其L2中具有不同程度的统计精度，该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。