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，大脑活动的更强驱动力对应于单词的概念和基于意义的方面，而不是它们的抽象语言属性。也就是说，当把“蓝色”和“帽子”这两个词组合在一起，形成“蓝色帽子”的更复杂的心理表征时，“蓝色”(一个颜色形容词，其中它修饰的单词是蓝色)的概念意义似乎推动了与意义和成分相关的区域的大脑活动的增加，但这个词作为修饰语的语言地位并不是这一活动的主要驱动力。这一观察结果促使语言学家改编关于人们如何使用语言结构以包含概念意义的现有理论。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，同时保持概念意义相对不变，或者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义。这项由NSF资助的主要研究通过使用脑磁图(MEG)来测量单词组成，例如，比较(A)“帽子是一个非常漂亮的蓝色”和(B)“帽子靠近一个非常漂亮的蓝灯”等句子中单词‘hat’的神经表征，其中关键的区别是在(A)而不是(B)中，单词‘hat’和‘Blue’必须结合在一起才能形成‘a Blue hat’的表征。当研究人员记录参与者的大脑活动时，词汇以视觉方式一次一个地呈现在屏幕上。然后，他们使用一种基于机器学习的方法来研究在句子中的哪些点上，大脑仍在积极地代表着出现在句子中的单词“帽子”。研究人员假设，如果为了与另一个概念结合，大脑有必要激活一个单词的完整表征，那么他们应该能够准确地识别出在(A)等句子中出现“Blue”之后立即出现“hat”(帽子)这样的单词的表征，但在(B)等句子中则不然。这项研究将首次提供直接证据，证明某些句子结构可以触发这种词汇水平的重新激活，并为句子加工的心理语言学模型提供信息。该项目包括对本科生进行STEM研究的培训。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.