Doctoral Dissertation Research: The interaction of expectations and evidence in pragmatic inference and generalizations

博士论文研究：语用推理和概括中期望和证据的相互作用

• 批准号: 1727336
• 负责人: Chigusa Kurumada
• 金额: $ 1.18万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727336&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; interaction; expectations

Spoken language not only communicates information about a speaker's thoughts or desires; it also conveys information about the speaker's identity. By simply listening to speakers' voices, accents, and word choice, we can learn a great deal about them, in addition to what is being talked about. Previous studies of language processing, however, have almost exclusively focused on the linguistic signal abstracted from individual speakers, investigating what listeners think is true about the world based on what an individual speaker has said. The project aims to explore the mechanism by which listeners extract information about the speaker through processing the linguistic signal. It then addresses the question of whether, and if so how, the increased knowledge about the speaker facilitates language comprehension. This research, consequently allows researchers to build a foundation for exploring how young children may learn speaker differences, which can contribute to new pedagogical tools for helping children to better interact with, and learn from, diverse populations. Secondly, the work will likely have industry applications for artificial intelligence technology, allowing it to better adapt its functionality to an individual user's talking style. This dissertation project employs two approaches to investigating what information listeners extract from spoken utterances. First, a large-scale online survey technique will be used to solicit responses from participants from a wider variety of linguistic and cultural backgrounds than those included in previous studies. Participants are exposed to utterances produced by two speakers and subsequently answer questions that probe their sensitivity to across-speaker differences. In the second set of experiments, a combination of an artificial language learning paradigm and an eye-tracking methodology will be used to study real-time language comprehension behaviors. Listeners' eye-gaze will be used to gain fine-grained information about the real-time development of their linguistic expectations. By combining these experimental approaches, the researchers elucidate how the human language comprehension system derives fine-grained expectations for future linguistic input and how the mechanism develops as a function of increased knowledge about linguistic communication.

口语不仅传达说话者的思想或愿望的信息，还传达说话者的身份信息。通过简单地听说话者的声音、口音和用词，我们可以学到很多关于他们的东西，除了他们在谈论什么。然而，以前的语言处理研究几乎完全集中在从个体说话者那里提取的语言信号上，根据个体说话者所说的话来调查听者对世界的真实看法。该项目旨在探索听者通过处理语言信号提取说话人信息的机制。本研究还探讨了对说话人的了解是否有助于语言理解，以及如何促进语言理解。因此，本研究为探索幼儿如何学习说话人差异奠定了基础，这有助于开发新的教学工具，帮助儿童更好地与不同人群互动并向他们学习。其次，这项工作可能会有人工智能技术的工业应用，使其能够更好地适应其功能，以个人用户的谈话风格。本论文项目采用两种方法来调查什么信息听众提取从口语话语。首先，将采用大规模在线调查技术，征求来自比以往研究中所包括的更广泛的语言和文化背景的参与者的答复。参与者被暴露在两个扬声器产生的话语，随后回答问题，探测他们的敏感性跨扬声器的差异。在第二组实验中，将人工语言学习范式和眼动追踪方法相结合，用于研究实时语言理解行为。听者的眼睛注视将被用来获得关于他们的语言期望的实时发展的细粒度信息。通过结合这些实验方法，研究人员阐明了人类语言理解系统如何对未来的语言输入产生细粒度的期望，以及该机制如何随着语言交流知识的增加而发展。