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

虽然许多成年人热衷于学习第二语言（L2）或其他语言（Ln），因为学术，经济或文化的原因，他们的成功可能不像他们在儿童时期获得第一语言（L1）那样容易。造成这种差异的原因之一是不同母语者的语言经验。此外，多语制的存在，即许多成年人在一生中将学习几种语言，使得评估先前的知识变得更加困难。学习一门语言意味着适应并发展对系统语言模式的期望，从声音序列到语法关系。成功地学习一种新的语言需要成年人从输入中提取新的系统模式，同时使用他们所学语言中的现有模式。本研究采用人工语法学习范式，探讨成人时体系统中的一种特殊的概率系统模式--预测依赖性如何促进或阻止对新时体结构的适应。来自两种L1背景（英语和泰语）的成年参与者将学习一种微型语言，该语言表达时间意义，涉及不同层次的英语和泰语类似的预测依赖关系：词汇和子词汇项目之间（例如，walk+艾德）用于英语以及在词汇项之间（例如，一对体标记，例如泰语的sed leew [“已经完成”]）。由于微型语言中相同的意义同样可能出现在两组依存关系中，因此本研究将调查（1）参与者是否能够更好地学习与他们的L1相似依存关系一致的预测依存关系，以及（2）在双语学习条件下，英语参与者的发现是否属实。他们对一组依赖关系有完美的知识，对另一组依赖关系没有任何知识，而在三语学习条件下，泰语-英语参与者在学习期间有两组依赖关系可供使用，但在他们的L2中具有不同程度的统计精度，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.