Collaborative Research: Bayesian Cue Integration in Probability-Sensitive Language Processing

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。句子理解的过程包括逐步获取单个单词的含义，并将它们组合成更大的表示。在这个过程中，读者/听众使用概率线索来指导他们对即将出现的单词和这些单词将扮演的句法角色的期望。例如，先前的研究表明，人们对词频（频繁出现的单词或词义比不频繁出现的单词或词义更容易处理）、句法频率（频繁出现的规则比不频繁出现的规则更容易处理）和世界知识（更可能发生的事件比不太可能发生的事件更容易处理）很敏感。然而，一个完整的语言处理理论不仅必须确定人们敏感的线索，还必须具体说明读者或听者如何将它们组合起来的理论。为了达到这个目的，这个项目调查了读者在多大程度上？句法处理机制适合几种基于不同贝叶斯推理方法的线索组合模型。贝叶斯模型提供了一种形式，指定如何对任意一组概率信息源进行最佳加权和组合。在这项研究中，每个线索组合模型都被应用于广泛的语言线索，人们已经被证明依赖这些线索。然后将这些模型与人类阅读时间数据进行比较。该项目将使用来自先前实验的现有阅读时间数据集，以及来自语言材料的新阅读时间数据，这些材料由空上下文和支持上下文中的单句组成，系统地改变先前文献中已显示具有可测量的阅读时间效应的几个线索。这将演示哪一种推理方法能够最准确地描述人类读者为了理解句子而进行的计算。先前在句子理解领域的研究已经证实，人们在解释一个句子时使用了各种各样的概率线索。然而，仍有几个重要的问题没有得到解答，包括(a)每个线索在典型文本中的重要性；(b)在理解过程中线索是如何组合的。本研究的主要进展是采用实验和计算建模相结合的方法来回答这两个问题。所开发的技术和结果将至少在三个一般领域广泛有用：(1)认知科学；(2)工程;(3)语言研究在人类中的应用。首先，该项目将通过为大量英语文本提供一个可用的阅读时间数据库来帮助研究人员，任何研究人员都可以使用该数据库来评估语言处理理论。此外，该项目将提供开源软件，研究人员可以使用或修改以评估语言和其他认知科学领域的相关理论问题。其次，该项目将为研究语言的计算机工程师提供一种方法来调查人类读者敏感的影响，为富有成效的研究指明潜在的方向。第三，从长远来看，理解语言是如何被处理的将有助于开发更好的诊断工具和治疗患有发育性和获得性语言障碍的人。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The process of sentence comprehension involves incrementally accessing the meaning of individual words and combining them into larger representations. In this process, readers / listeners use probabilistic cues to guide their expectations of upcoming words and of the syntactic roles that the words will play. For example, previous research has demonstrated that people are sensitive to word frequency (more frequent words or word meanings are easier to process than less frequent words or word meanings), syntactic frequency (more frequent rules being easier to process than less frequent rules) and world knowledge (more likely events being easier to process than less likely events). However, a complete theory of language processing must not only identify the cues that people are sensitive to, but also has to specify a theory of how a reader or listener will combine them. Towards this end, this project investigates the extent to which readers? syntactic processing mechanisms fit several cue combination models that are based on different Bayesian inference methods. Bayesian models provide a formalism specifying how any set of probabilistic information sources can be optimally weighted and combined. In this research, each of the cue combination models is applied to a wide range of language cues which people have been shown to rely on. The models will then be compared in terms of their fit to human reading-time data. The project will use existing reading-time data sets from previous experiments, as well as new reading-time data from language materials consisting of single sentences in null contexts and in supportive contexts, systematically varying several cues that have been shown to have measurable reading time effects in previous literature. This will demonstrate which of the inference methods provides the most accurate description of the computations that human readers perform in order to understand sentences.Previous work in the field of sentence comprehension has established that people use a diverse range of probabilistic cues when they interpret a sentence. However, several important questions that remain unanswered include (a) how much each cue matters in typical texts; and (b) how the cues are combined in the course of comprehension. The main advance of this research is to use a combination of experimental and computational modeling methods to answer these two questions. The techniques and results developed will be broadly useful in at least three general areas: (1) cognitive science; (2) engineering; and (3) human applications of language research. First, this project will help researchers by providing an available database of reading times for a large corpus of English text, which any researcher will be able to use to evaluate theories of language processing. In addition, the project will provide open source software that researchers can use or modify to evaluate related theoretical questions in language and other fields of cognitive science. Second, the project will provide a way for computer engineers who research language to investigate the effects that human readers are sensitive to, indicating potential directions for fruitful research. And third, an understanding of how language is processed will in the long run aid in developing better diagnostic tools and treatments for people with developmental and acquired language disorders.