Neural Mechanisms of Probabilistic Prediction in Language Comprehension

该奖项是NSF社会、行为和经济科学博士后研究奖学金（SPRF）计划的一部分。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。随着世界变得更加相互联系，能够与来自不同语言背景的人进行交流变得越来越重要。在新的语言规范中学习交流不仅是幼儿语言学习的一个特征，而且会持续一生。因此，研究说话人如何在新的语言环境中融入统计知识是非常必要的。该项目迈出了理解神经机制的第一步，这些神经机制使语言学习者能够适应新的语言规范。特别是，该项目研究了预测者如何使用他们对语言和世界的统计知识（即人们倾向于谈论什么，以及他们使用什么语言来这样做）来预测即将到来的单词，以及这些预测如何适应不断变化的环境。该项目侧重于三个神经机制：1。从长期记忆中提取单词。2.通过激活一个词并抑制其他词来在竞争的备选方案之间进行选择。3.当实际语言输入违反预测时的冲突解决。该项目将通过开发这些神经机制如何适应语言环境中不断变化的统计数据的计算模型（例如，倾向于说非常可预测与非常不可预测的事情的对话者）并根据电生理数据测试这些模型来进一步理解语言理解。最终，该项目将阐明这些神经机制如何成为语言理解的基础，以及它们如何允许终身语言学习。为了在嘈杂和不断变化的世界中快速理解语言，学习者必须利用他们对语言的统计知识来解决不确定性。该项目研究神经机制如何对即将出现的单词进行概率预测，以及这些预测如何适应语言环境的变化。该项目将计算建模与认知神经科学相结合，对语言处理中不同机制的作用进行定量预测，重点是测试语言理解中的预测是一个猖獗的概率过程的假设。首先，该项目将开发语言处理中概率预测的分层生成模型，使用原则性信息理论措施对不同的神经机制进行定量预测。然后，该项目将研究大脑如何使用相对较新发现的事件相关电位（ERP）组件来实现这一计算理论，超越经典N400效应的时间窗口，关键是使用这些ERP组件来进一步理解这些神经机制如何实现计算框架假设的算法过程。最后，该项目将使用计算框架和进一步的ERP实验来研究这些机制是否以及如何适应经常发生意外事件的环境。这些实验将阐明语言理解中预测过程的神经机制，以及这些机制如何允许终身语言学习。该项目将通过统一来自计算心理语言学和认知神经科学的不同理论来促进我们对语言理解的理解。该项目包括超越当前最先进水平的实质性方法创新，使用生成模型对神经机制和语言适应过程中的学习曲线进行定量预测。研究人员还将开发用于逐个试验ERP数据分析的方法，这些方法对于研究不断变化的环境中的持续适应特别强大。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. As the world becomes more interconnected, it is increasingly important to be able to communicate with people from different linguistic backgrounds. Learning to communicate within new linguistic norms is not merely a feature of early childhood language learning but continues throughout the lifetime. It is thus essential to study how speakers incorporate statistical knowledge in new linguistic environments. This project takes a first step towards understanding the neural mechanisms that enable comprehenders to adapt to new linguistic norms. In particular, this project studies how comprehenders use their statistical knowledge about the language and the world (i.e. what things people tend to talk about, and what language they use to do so) to make predictions about upcoming words, and how these predictions adapt to a changing environment. The project focuses on three neural mechanisms: 1. Retrieval of words from long-term memory. 2. Selection between competing alternatives by activating one word and suppressing others. 3. Conflict resolution when a prediction is violated by the actual linguistic input. The project will further our understanding of language comprehension by developing computational models of how these neural mechanisms adapt to changing statistics in the linguistic environment (e.g. an interlocutor who tends to say very predictable versus very unpredictable things) and testing these models against electrophysiological data. Ultimately, the project will elucidate how these neural mechanisms underlie language comprehension and how they allow for lifelong language learning.In order to comprehend language rapidly in a noisy and ever-changing world, comprehenders must leverage their statistical knowledge about the language to resolve uncertainty. This project studies how neural mechanisms make probabilistic predictions about upcoming words and how these predictions adapt to changes in the linguistic environment. The project combines computational modeling with cognitive neuroscience to make quantitative predictions about the role of distinct mechanisms in language processing, with a focus on testing the hypothesis that prediction in language comprehension is a rampant, probabilistic process. First, the project will develop a hierarchical generative model of probabilistic prediction in language processing to make quantitative predictions about distinct neural mechanisms using principled information theoretic measures. The project will then investigate how the brain can implement this computational theory using relatively recently discovered event-related potential (ERP) components, beyond the time window of the classic N400 effect, crucially using these ERP components to further our functional understanding of how these neural mechanisms implement algorithmic processes hypothesized by the computational framework. Finally, the project will use the computational framework and further ERP experiments to investigate whether and how these mechanisms adapt to environments in which unexpected events occur frequently. These experiments will elucidate the neural mechanisms that underlie predictive processes in language comprehension, and how such mechanisms allow for lifelong language learning. The project will advance our understanding of language comprehension by unifying disparate theories from computational psycholinguistics and cognitive neuroscience. The project includes substantial methodological innovation beyond the current state-of-the-art, using generative models to make quantitative predictions both about neural mechanisms broadly and about learning curves during language adaptation. The investigators will also develop methods for trial-by-trial ERP data analysis which are particularly powerful for studying ongoing adaptation in changing environments.