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

口语不仅能传达说话者的思想或愿望；它还传达了关于说话人身份的信息。通过简单地听说话人的声音、口音和用词，我们可以学到很多关于他们的知识，除了他们正在谈论什么。然而，先前的语言处理研究几乎完全集中在从说话人身上提取的语言信号上，调查听者根据说话人所说的话对世界的看法是正确的。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。然后，它解决了这样一个问题：对说话者的了解的增加是否有助于语言理解，如果是的话，又是如何促进的。因此，这项研究使研究人员为探索幼儿如何学习说话者的差异奠定了基础，这可以为帮助儿童更好地与不同人群互动和学习提供新的教学工具。其次，这项工作可能会有人工智能技术的行业应用，使其能够更好地适应个人用户的谈话风格。本论文项目采用两种方法来研究听者从口语话语中提取什么信息。首先，一项大规模的在线调查技术将被用于征求来自更广泛的语言和文化背景的参与者的回应，而不是之前的研究。参与者被暴露在两个说话者的话语中，随后回答一些问题，以探测他们对不同说话者之间差异的敏感度。在第二组实验中，将使用人工语言学习范式和眼动追踪方法相结合的方法来研究实时语言理解行为。听众的目光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。

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.