Using Machine Learning and Cognitive Modeling to Understand the fMRI-measured Brain Activation Underlying the Representations of Words and Sentences

使用机器学习和认知模型来了解单词和句子表示背后的功能磁共振成像测量的大脑激活

• 批准号: 0423070
• 负责人: Tom Mitchell
• 金额: $ 22.46万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0423070&HistoricalAwards=false
• 关键词: Using; Machine; Learning; Cognitive; Modeling

Using machine learning and cognitive modeling to understand the fMRI-measuredbrain activation underlying the representations of words and sentencesTom M. Mitchell and Marcel A. Just Project AbstractA number of recent fMRI studies have reported significant and repeatable differences in fMRI brain activation when human subjects perceive pictures or words describing objects from different semantic categories (e.g., pictures or words that describe tools, buildings, or people). It is currently possible to determine with good accuracy which of several semantic categories a person is thinking about, based on their brain activation.We propose new research that builds on these recent discoveries, and seeks to understand (1) human brain activity associated with different semantic categories of objects and actions (nouns and verbs); (2) whether the brain activity associated with semantic categories can be partitioned into more primitive semantic components (e.g., does the brain activity associated with words about tools factor into one component characterizing the tool's visual appearance and a second component characterizing the motor actions involved in using the tool?); and (3) how brain activity associated with individual words is combined into more complex patterns when reading word pairs or simple phrases and sentences.This research involves:(1) applying machine learning algorithms to discover cortex-wide brain activation patterns associated with particular semantic domains, (2) developing a computational model of human language processing that instantiates the representational principles discovered and that makes specific, testable predictions, and (3) conducting new fMRI studies to obtain novel data about human semantic category representations.The intellectual merit of the proposed research is multifaceted. If successful, our research will lead to new scientific insights into how the brain organizes information about meanings of words, objects, and actions. It will also lead to new methods for fMRI data analysis, especially for discovering complex temporal-spatial patterns of fMRI activation that accurately distinguish different mental states. The research will also lead toward a new paradigm for developing computational cognitive models and fitting them to empirical data obtained from fMRI and from behavioral measures.The broader impacts of the proposed research will be amplified by specific outreach activities to several communities. In addition to publishing our scientific results in the cognitive and computational neuroscience literature, we will also actively engage this community by disseminating our new experimental fMRI data through the NSF-funded fMRI Data Center, and by documenting and publishing our new data analysis algorithms on the internet. We will proactively engage the statistical machine learning community, which has much to contribute to development of new fMRI analysis methods, and will develop and disseminate teaching materials for the undergraduate and graduate educational community,including fMRI data sets. Finally, our proposed research has potential impact on the medical research community, especially regarding the study of neurological conditions such as Alzheimer's disease, dyslexia and high-functioning autism - three areas entailing a language disturbance in which we already have active research collaborations, providing a direct conduit for transferring new scientific insights that may arise from this research.

使用机器学习和认知建模来理解单词和单词表征背后的fMRI测量的大脑激活Tom M.米切尔和马塞尔A.最近的一些fMRI研究报告了当人类受试者感知描述不同语义类别对象的图片或文字时，fMRI大脑激活的显著和可重复的差异（例如，描述工具、建筑物或人的图片或文字）。 目前，基于大脑活动，可以准确地确定一个人正在思考的几个语义类别中的哪一个。我们提出了新的研究，建立在这些最新发现的基础上，并试图了解（1）与物体和动作的不同语义类别相关的人类大脑活动（名词和动词）;（2）与语义类别相关联的大脑活动是否可以被划分为更原始的语义成分（例如，与关于工具的词语相关的大脑活动是否分为表征工具的视觉外观的一个成分和表征使用工具所涉及的运动动作的第二个成分？以及（3）当阅读单词对或简单短语和句子时，与单个单词相关的大脑活动如何组合成更复杂的模式。这项研究涉及：（1）应用机器学习算法来发现与特定语义域相关联的皮层范围的大脑激活模式，（2）开发人类语言处理的计算模型，该计算模型实例化所发现的表征原则并做出具体的、可测试的预测，以及（3）进行新的功能磁共振成像研究，以获得有关人类语义类别表征的新数据。拟议研究的智力价值是多方面的 如果成功的话，我们的研究将为大脑如何组织有关单词，物体和动作的意义的信息带来新的科学见解。 这也将为功能磁共振成像数据分析带来新的方法，特别是发现功能磁共振成像激活的复杂时空模式，以准确区分不同的精神状态。这项研究还将为开发计算认知模型提供一种新的范式，并将其与从功能磁共振成像和行为测量中获得的经验数据相匹配。 除了在认知和计算神经科学文献中发表我们的科学成果外，我们还将通过NSF资助的fMRI数据中心传播我们新的实验fMRI数据，并在互联网上记录和发布我们新的数据分析算法，积极参与这个社区。 我们将积极参与统计机器学习社区，这对开发新的fMRI分析方法有很大贡献，并将为本科生和研究生教育社区开发和传播教材，包括fMRI数据集。最后，我们提出的研究对医学研究界有潜在的影响，特别是关于神经系统疾病的研究，如阿尔茨海默病，阅读障碍和高功能自闭症-这三个领域涉及语言障碍，我们已经有积极的研究合作，为转移可能从这项研究中产生的新的科学见解提供了直接的渠道。