Doctoral Dissertation Research: The interaction between conceptual combination and linguistic structure

过去十年对大脑如何构成意义的研究表明，大脑活动的一个更强大的驱动力对应于词汇的概念和基于意义的方面，而不是抽象的语言属性。也就是说，当把“蓝色”和“帽子”这两个词结合起来形成更复杂的“蓝色帽子”的心理表征时，“蓝色”的概念意义（一种颜色形容词，它修饰的词是蓝色）似乎推动了与意义和构成相关的大脑区域活动的增加，但这个词作为修饰语的语言地位并不是这种活动的主要驱动力。这一观察结果促使语言学家调整现有的关于人们如何使用语言结构来包含概念意义的理论。该项目通过开展一系列研究来检验这一点，这些研究改变了英语句子的结构，要么保持概念意义相对恒定，要么使用这些结构线索来改变哪些单词可以组合成一个新的意思。主要研究由NSF词组成的格兰特措施使用脑磁图描记术(MEG)比较,例如,“帽子”这个词的神经表征在(a)等在句子“这顶帽子是一个非常漂亮的蓝色”和(b)”附近的帽子是一个很漂亮的蓝灯”,关键的区别是,在(a)但不是(b),单词“帽子”和“蓝”必须结合“蓝帽子”的表示形式。研究人员在屏幕上逐个显示单词，同时记录参与者的大脑活动。然后，他们使用一种基于机器学习的方法来研究，在句子中出现“帽子”这个词后，大脑在哪个点上仍在积极地表征这个词。研究人员假设，如果大脑激活一个单词的完整表征是必要的，以便与另一个概念结合起来，那么他们应该能够准确地识别出像“帽子”这样的单词的表征，紧接着在(a)这样的句子中出现“蓝色”这个单词，而不是在(b)这样的句子中出现。这项研究将首次提供直接证据，证明这种词级再激活可以由某些句子结构触发，并为句子处理的心理语言学模型提供信息。该项目包括培养从事STEM研究的本科生。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。

The last decade of research into how the brain composes meaning has shown that a stronger driver of brain activity corresponds to the conceptual and meaning-based aspects of words rather than their abstract linguistic properties. That is, when combining the words 'blue' and 'hat' to form the more complex mental representation of 'a blue hat', the conceptual meaning of 'blue' (a color adjective, where the word it modifies is blue) seems to drive an increase in brain activity in regions associated with meaning and composition, but the word's linguistic status as a modifier is not the primary driver of this activity. This observation invites linguists to adapt existing theories about how people use language structure to include conceptual meaning. The project examines this by developing a series of studies that vary the structure of English sentences either while keeping the conceptual meaning relatively constant or while using those structural cues to vary which words can combine to form a new meaning. The primary study funded by this NSF grant measures word composition by using magnetoencephalography (MEG) to compare, for example, the neural representation of the word 'hat' in across sentences like (a) "The hat is a really pretty blue color" and (b) "The hat is near a really pretty blue lamp", where the key difference is that in (a) but not (b), the words 'hat' and 'blue' must combine to form the representation of 'a blue hat'. Words are presented visually one-at-a-time on a screen while the researchers record participants' brain activity. They then use a machine learning based approach to study at which points in the sentence the brain is still actively representing the word 'hat' after it has been presented. The researchers hypothesize that if it is necessary that the brain activates the full representation of a word in order to combine with another concept, then they should be able to accurately identify the representation for a word like 'hat' immediately after the word 'blue' is presented in sentences like (a), but not in sentences like (b). This study would be the first to provide direct evidence that such word-level re-activation can be triggered by certain sentence structures and inform psycholinguistic models of sentence processing. The project includes the training of undergraduate students in STEM research.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.