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

博士论文研究：动词开头语言中关系从句的期望和噪声通道处理

• 批准号: 2235106
• 负责人: Emily Morgan
• 金额: $ 1.86万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235106&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Expectations; Noisy

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.

对句子的语法结构和该句子的含义的期望强烈影响人类的过程和解释语言。在某些情况下，句子结构和含义与我们的期望和句子保持一致：人们可以更轻松地理解它们，并且更有可能正确地解释它们。但是，有时我们的期望与句子的实际解释相抵触，并使句子更难处理。研究调查了大脑如何产生这些期望以及如何用于理解句子。该项目在实验研究中以一种理解的语言对此进行了研究，并在人们阅读句子时监测了他们的眼睛运动，其中有一组在美国和国外的母语人士。这项研究提高了我们对影响人类处理语言方式相互作用的各种因素的理解，这有助于告知技术和医学等领域的发展。这个博士学位论文项目专门回答了这个问题，语言综合如何根据句子结构的统计期望来鉴于输入冲突（例如，使用较少常见的句子结构输入）？该项目通过进一步测试语言处理的现有理论，例如记忆和基于期望的难度理论，以及其他语言理解理论，尤其是良好的和噪声通道处理理论来研究这个问题。该项目通过在阅读过程中记录眼动来测试这些理论，并确定这些眼睛运动在阅读困难句子时如何变化，而与简单的句子相比。研究人员还通过理解问题来测试读者对每个句子的理解，评估读者对句子的结构和含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发现。该奖项反映了NSF的法规任务，并通过评估基金会的知识绩效和广泛的范围来诚实地通过评估来诚实地通过评估来进行评估。