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”这两个词组合起来形成“一顶蓝色帽子”这个更复杂的心理表征时，“蓝色”（一个颜色形容词，它修饰的词是蓝色）的概念意义似乎会促使与意义和组成相关的大脑活动增加，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的理论，以包括概念意义。该项目通过开发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恒定，或者使用这些结构线索来改变哪些单词可以联合收割机组合形成新的意义。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，比较单词“帽子"在句子中的神经表征，如（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色灯”，其中关键的区别是（a）而不是（B），单词“hat”和“blue”必须联合收割机来形成“a blue hat”的表示。研究人员在记录参与者大脑活动的同时，在屏幕上一次一个地呈现单词。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，大脑仍然在积极地表示“帽子”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，以便联合收割机与另一个概念结合，那么他们应该能够在单词“蓝色”出现在句子（a）中后立即准确识别出单词“帽子”的表征，但在句子（B）中则不然。这项研究将是第一个提供直接证据，这种词级的再激活可以触发某些句子结构和通知心理语言学模型的句子加工。该奖项反映了NSF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.