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RI: Small:Comp Cog: Broad-coverage semantic models of human sentence processing

RI：Small：Comp Cog：人类句子处理的广泛覆盖语义模型

基本信息

批准号：
1816891
负责人：
William Schuler
金额：
$ 49.03万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816891&HistoricalAwards=false
关键词：
RI Small Comp Cog Broad

项目摘要

Humans are a successful species in large part because they can pass knowledge about the world to one another using linguistic explanations. These explanations can be quite complex, involving nested generalizations about multiple classes of objects and events. Accurate models of how these relationships are decoded from natural language could further our understanding of how the brain works, and may allow non-programmer users to explain their desired products, goals and constraints to machines. Sentence processing experiments may provide an important window into the mechanisms of idea formation in language comprehension, but the human mind is extraordinarily sensitive to the strangeness of constructed stimuli used in experimentally controlled research designs, yielding potentially confounding effects arising from unexpected words or sentence structures. A common alternative is to use designs employing naturally-occurring stimuli with statistical controls, usually using one or more probabilistic measures of surprise during sentence processing. Unfortunately, existing probabilistic measures of surprise are based on overly simple models of sentence processing that are not connected to the nested structure of generalizations that a linguistic explanation may describe, and thus have severe limits as predictors of these kinds of frequency effects. This project will therefore develop a sentence processing model that decodes sentences into meanings using a human-like incremental probabilistic process. This model will then be used to control for frequency effects in neural activation, blood oxygenation and reading time data in order to isolate effects that can be attributed to the mechanical process of constructing and storing complex ideas during language comprehension.This project constructs a model of sentence processing that bases its processing decisions on mental representations of meanings rather than on words only. This means that the model will be less surprised by repeated nouns or pronouns when these words refer to a common entity which is prominent in a discourse. The project initially focuses on the development of a statistical sentence processing model which maintains several possible analyses of a sentence after each word is processed, each of which contains explicit representations of each discourse referent involved in a sentence meaning as a set of logical predicates adjacent to that referent in a graphical representation of the meaning. A subsequent version of the model compresses these context sets into vectors, which are passed through a recurrent neural network. The predictions of these models are compared against existing neural network language models used in natural language processing applications to ensure that their linguistic predictions are accurate. Incremental probabilities generated by these models are then used to estimate probabilistic surprise as a frequency control in predicting functional magnetic resonance (fMRI), electroencephalographic (EEG), eye-tracking, and reading-time observations in existing datasets, in order to isolate effects due to memory usage and other mechanistic factors.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.

人类在很大程度上是一个成功的物种，因为他们可以通过语言解释将关于世界的知识传递给彼此。这些解释可能相当复杂，涉及对多个对象和事件类的嵌套概括。这些关系是如何从自然语言中解码出来的准确模型，可以进一步加深我们对大脑工作方式的理解，并可能允许非程序员用户向机器解释他们想要的产品、目标和约束。句子处理实验可能为语言理解中思想形成的机制提供了一个重要的窗口，但人类的大脑对实验控制研究设计中使用的构造刺激的陌生性非常敏感，产生了意想不到的单词或句子结构的潜在混淆效应。一种常见的替代方法是使用带有统计控制的自然发生刺激，通常在句子处理过程中使用一个或多个惊喜概率测量。不幸的是，现有的惊喜概率测量是基于过于简单的句子处理模型，而不是与语言解释可能描述的概括的嵌套结构相关联，因此在预测这些频率效应方面有严重的局限性。因此，该项目将开发一个句子处理模型，该模型使用类似人类的增量概率过程将句子解码为含义。然后，该模型将用于控制神经激活、血液氧合和阅读时间数据中的频率效应，以便分离出语言理解过程中构建和存储复杂思想的机械过程所产生的影响。这个项目构建了一个句子处理模型，它的处理决策基于对意义的心理表征，而不是仅仅基于单词。这意味着当重复的名词或代词指代一个在语篇中突出的共同实体时，模型就不会那么惊讶了。该项目最初侧重于开发一个统计句子处理模型，该模型在处理每个单词后对句子进行几种可能的分析，每种分析都包含句子含义中涉及的每个话语所指的显式表示，作为一组逻辑谓词，在意义的图形表示中与该所指相邻。该模型的后续版本将这些上下文集压缩成向量，这些向量通过循环神经网络传递。将这些模型的预测与自然语言处理应用中使用的现有神经网络语言模型进行比较，以确保其语言预测的准确性。然后使用这些模型产生的增量概率来估计概率惊喜，作为预测功能磁共振（fMRI）、脑电图（EEG）、眼动追踪和阅读时间观察中现有数据集的频率控制，以便隔离由于记忆使用和其他机制因素造成的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。