Doctoral Dissertation Research: Expectations and Noisy-Channel Processing of Relative Clauses in a verb-initial language

对句子的语法结构和句子的意义的期望强烈地影响着人类处理和解释语言的方式。在某些情况下，句子的结构和含义与我们的期望一致，句子更容易处理：人们更容易理解它们，更有可能正确地解释它们。然而，有时我们的期望与句子的实际解释相冲突，使句子更难处理。这项研究调查了大脑如何产生这些期望，以及它们如何用于理解句子。该项目在实验研究中以一种未被充分研究的语言进行研究，监测人们在阅读句子时的眼球运动，在美国和国外有各种各样的母语人士。这项研究提高了我们对各种因素的理解，这些因素相互作用，影响人类如何处理语言，这有助于为技术和医学等领域的发展提供信息。这个博士论文项目具体回答了这个问题，语言学习者如何根据冲突的输入（例如，使用不太常见的句子结构输入）？该项目通过进一步测试现有的语言处理理论来研究这个问题，例如基于记忆和期望的处理难度理论，以及其他语言理解理论，特别是足够好和噪音通道处理理论。该项目通过记录阅读过程中的眼球运动来测试这些理论，并确定这些眼球运动在阅读困难句子时与简单句子相比的变化。研究人员还用理解问题测试读者对每个句子的理解，评估读者对句子结构和含义的期望如何影响他们对句子的最终解释，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

Expectations about the grammatical structure of a sentence and the meaning of that sentence strongly affect how humans process and interpret language. In some cases, sentence structures and meanings align with our expectations and sentences are easier to process: people can understand them more easily and are more likely to interpret them correctly. However, sometimes our expectations conflict with the actual interpretation of the sentence and make the sentence more difficult to process. This research investigates how the brain generates these expectations and how they are used in understanding sentences. The project studies this in an understudied language in experimental research, monitoring people’s eye movement as they read sentences, with a diverse set of native speakers in the U.S. and abroad. This research improves our understanding of the various factors that interact to affect how humans process language, which helps inform developments in areas such as technology and medicine. This doctoral dissertation project specifically answers the question, how do language comprehenders make use of statistical expectations about sentence structure in light of conflicting input (e.g., input using less common sentence structures)? The project investigates this question by further testing existing theories of language processing, such as memory- and expectation-based theories of processing difficulty, alongside other theories of language comprehension, in particular good-enough and noisy-channel processing theories. The project tests these theories by recording eye movements during reading, and identifying how these eye movements change while reading a difficult sentence as compared to an easy sentence. The researchers additionally test the readers’ understanding of each sentence with comprehension questions, evaluate how a reader’s expectations about the structure and the meaning of a sentence affect their final interpretation of the sentence, and evaluate findings through Bayesian mixed effects regression models.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.