CAREER: The Effect of Trial-Level Lexical Entropy on Language Processing

职业：试用级词汇熵对语言处理的影响

• 批准号: 2337698
• 负责人: Hossein Karimi
• 金额: $ 57.19万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2337698&HistoricalAwards=false
• 关键词: CAREER; Effect; Trial; Level; Lexical

Understanding and producing language is so commonplace that we may fail to appreciate the complexities involved in them. Intrinsically meaningless speech sounds and letter shapes hit our eardrums or eyeballs, and, through a series of processing steps are turned into meaningful ideas in our heads. We can do this despite the fact that the linguistic input that we receive is not perfect; we understanding language in noisy environments, when faced with idiosyncratic factors such as accents, we can adjust to ambiguous words and structures, among many other factors. These challenges are not only due to uncertainty in the input; the human cognitive system has its own limitations, with lapses in attention or failures of memory impacting how we process language. To remain successful and efficient in tasks of language processing despite this uncertainty, the human brain generates predictions about upcoming information based on the current context and/or prior knowledge. This project investigates this prediction, specifically focusing on the process of how we pick out the intended words - based on information like meaning, part of speech, and pronunciation, among a large set of competing words. Specifically, it looks at how the overall distribution of potential upcoming words - and specifically uncertainty about upcoming words - influences our ability to perceive and produce language, measuring this distribution at the level of an individual person with their own idiosyncratic experiences of the world. In terms of broader impacts, this project builds the language science community at Mississippi State University through the development of co-taught courses across different departments engaged in language science, and training students and faculty from across the university on how to use more advanced tools to answer a variety of questions about the nature of language processing. To obtain a valid, sensitive and nuanced measure of uncertainty associated with upcoming linguistic material, the investigators measure "information entropy" for each research participant in specific sentential contexts. Information entropy reflects the uncertainty associated with the next word following a sentential context, and when measured for each participant for each context, it reflects uncertainty experienced by specific individuals in specific semantic domains, thereby providing a customized entropy measure for each individual. The investigators collect behavioral data, such as reading times, as well as brain data, such as ongoing brain activity commonly known and electroencephalogram or EEG, to assess how the human brain generates predictions about upcoming input. The investigators also measure other brain functions such as working memory span and attention control to examine how prediction generation interacts with those functions. The results of this project will advance our knowledge about the role of prediction when humans produce and comprehend language, and how those skills interact with other critical human brain functions such as working memory and attention. This project is jointly funded by the Perception, Action and Cognition program, the Linguistics program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

理解和产生语言是如此普遍，以至于我们可能无法理解其中所涉及的复杂性。本质上无意义的语音和字母形状撞击我们的耳膜或眼球，并通过一系列处理步骤在我们的头脑中转化为有意义的想法。尽管我们收到的语言输入并不完美，但我们仍然可以做到这一点；我们在嘈杂的环境中理解语言，当遇到口音等特殊因素时，我们可以适应模棱两可的单词和结构以及许多其他因素。这些挑战不仅是由于输入的不确定性；人类认知系统有其自身的局限性，注意力不集中或记忆力下降会影响我们处理语言的方式。 尽管存在这种不确定性，为了在语言处理任务中保持成功和高效，人脑根据当前上下文和/或先验知识生成关于即将到来的信息的预测。该项目研究了这一预测，特别关注我们如何根据含义、词性和发音等信息从大量竞争单词中挑选出想要的单词的过程。具体来说，它着眼于潜在即将出现的单词的整体分布——特别是即将出现的单词的不确定性——如何影响我们感知和产生语言的能力，并在具有自己独特的世界体验的个人层面上衡量这种分布。就更广泛的影响而言，该项目通过在从事语言科学的不同部门开发共同教授的课程，并培训全校的学生和教师如何使用更先进的工具来回答有关语言处理本质的各种问题。为了获得与即将到来的语言材料相关的不确定性的有效、敏感和细致的测量，研究人员测量了每个研究参与者在特定句子上下文中的“信息熵”。信息熵反映了与句子上下文之后的下一个单词相关的不确定性，并且当对每个上下文的每个参与者进行测量时，它反映了特定个体在特定语义领域中经历的不确定性，从而为每个个体提供定制的熵度量。研究人员收集行为数据（例如阅读时间）以及大脑数据（例如众所周知的持续大脑活动和脑电图或脑电图），以评估人脑如何对即将到来的输入产生预测。研究人员还测量了其他大脑功能，例如工作记忆广度和注意力控制，以研究预测生成如何与这些功能相互作用。该项目的结果将增进我们对人类产生和理解语言时预测作用的认识，以及这些技能如何与工作记忆和注意力等其他关键的人脑功能相互作用。该项目由感知、行动和认知计划、语言学计划和刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和能力进行评估，认为值得支持。更广泛的影响审查标准。