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，句子结构和意思与我们的期望一致，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，有时我们的期望与对句子的实际解释相冲突，从而使句子更难处理。这项研究调查了大脑是如何产生这些期望的，以及它们是如何用于理解句子的。该项目在实验研究中用一种未被研究的语言来研究这一点，监测人们在阅读句子时的眼球运动，对象是来自美国和国外的不同母语人士。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，影响人类处理语言的方式，这有助于为技术和医学等领域的发展提供信息。这一博士论文项目具体回答了这样一个问题，即语言理解者如何根据相互冲突的输入(例如，使用不常见的句子结构输入)来利用关于句子结构的统计期望？该项目通过进一步检验现有的语言加工理论，如基于记忆和期望的加工难度理论，以及其他语言理解理论，特别是足够好和有噪音的通道加工理论，来研究这个问题。该项目通过记录阅读过程中的眼球运动来测试这些理论，并确定与阅读简单句子相比，阅读困难句子时这些眼球运动是如何变化的。研究人员还用理解问题测试读者对每一句话的理解，评估读者对句子结构和意义的期望如何影响他们对句子的最终解释，并通过贝叶斯混合效应回归模型评估结果。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.