CRCNS: fMRI Pattern Analysis of Neural Correlates of Natural Scene Categories

CRCNS：自然场景类别神经相关性的 fMRI 模式分析

• 批准号: 7667248
• 负责人: Fei-Fei Li
• 金额: --
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2009-08-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7667248
• 关键词: Algorithms; Attention; Behavior; Brain; Categories; Cities; Code; Commit; Communication; Complex; Computer Simulation; Computer Vision Systems; Data; Discipline; Environment; Fostering; Functional Magnetic Resonance Imaging; Future; Human; Image Analysis; Location; Mentors; Methods; Nature; Neurosciences; Pattern; Pattern Recognition; Perception; Process; Psychologist; Research; Research Personnel; Scheme; Science; Scientist; Statistical Methods; Students; Training; Vision; Visual; Woman; Working Women; design; expectation; forest; improved; insight; interdisciplinary approach; neuroimaging; relating to nervous system; research study; tool; vision science

DESCRIPTION (provided by applicant): For over half a century, vision scientists have been decomposing visual scenes into simple, more tractable components in an attempt to understand how the brain accomplishes vision. Although this endeavor has revealed much about the specialized subsystems of vision, surprisingly little is know about how, or even where in the brain, we process scenes as a whole. How is it, for instance, that the brain determines whether it is looking at a forest or a city skyline? One reason for the paucity of research on this topic may be that the neural representation of a scene is likely to be highly distributed, a coding scheme not easily identified by many traditional neuroscience methods. The objective of the proposed research is to use a new method of analyzing functional magnetic resonance imaging (fMRI) data that is designed to leverage activity patterns across the brain, in order to better understand how the brain categorizes natural scenes. In particular, the project combines expertise from computer vision and neuroimaging by applying statistical pattern recognition algorithms to fMRI data to understand how the brain distinguishes between different categories of natural scene (e.g., a beach versus a highway). The proposed project will use and develop a statistical pattern recognition approach to fMRI analysis to accomplish three more specific objectives: (i) to identify the neural representation of natural scene categories, (ii) to identify the computational principles for forming and using the neural representation of natural scene categories, and (iii) to explore the effects of attention and expectation on natural scene categorization. The insights gained from these experiments will be verified in a computational model of natural scene perception, which in turn will generate predictions for future experiments. Intellectual Merit of the Proposed Activity: Although previous research has shown that humans can quickly and effortless categorize natural scenes, there is very little understanding of how this is accomplished in the brain. The research proposed here will significantly advance our understanding of how natural scenes are represented in the brain and begin to uncover the computational strategies the brain employs in quickly and accurately extracting the gist of a scene. Broader Impacts of the Proposed Activity: The highly interdisciplinary nature of the proposed research requires intense interactions among psychologists, neuroscientists, and computer vision researchers. As such, the project not only promises to increase communication among very different disciplines but it will also to provide doctoral students with truly interdisciplinary training. The PIs are committed to providing a highly interactive research environment, mentoring students across disciplines, and fostering the interdisciplinary approach to science in general. Moreover, two of the three PIs are women working in fields in which women are traditionally underrepresented and are committed to improving the representation and visibility of women in science. Finally, the principles derived from this project are likely to have implications beyond the domain of natural scene perception. By refining the pattern recognition algorithms and their application to fMRI data, the project will expand the set of tools available to neuroscientists wishing to study a whole host of complex human behaviors that likely depend on subtle but distributed patterns of activity in the brain.

描述（由申请人提供）：超过半个世纪，视觉科学家一直在将视觉场景分解为简单的、更易处理的组件，以试图理解大脑如何实现视觉。虽然这项奋进揭示了很多关于视觉的专门子系统的信息，但令人惊讶的是，我们对整个场景是如何处理的，甚至是在大脑中的什么位置，却知之甚少。例如，大脑是如何判断它看到的是森林还是城市的天际线的？这方面研究较少的一个原因可能是场景的神经表征可能是高度分布的，许多传统的神经科学方法不容易识别这种编码方案。这项研究的目的是使用一种新的方法来分析功能性磁共振成像（fMRI）数据，该方法旨在利用大脑的活动模式，以便更好地了解大脑如何对自然场景进行分类。特别是，该项目结合了计算机视觉和神经成像的专业知识，通过将统计模式识别算法应用于fMRI数据，以了解大脑如何区分不同类别的自然场景（例如，海滩对高速公路）。拟议的项目将使用和开发一个统计模式识别方法的功能磁共振成像分析，以实现三个更具体的目标：（i）确定自然场景类别的神经表征，（ii）确定形成和使用自然场景类别的神经表征的计算原则，以及（iii）探索注意力和期望对自然场景分类的影响。从这些实验中获得的见解将在自然场景感知的计算模型中得到验证，这反过来又将为未来的实验产生预测。拟议活动的智力优点：尽管之前的研究表明人类可以快速、毫不费力地对自然场景进行分类，但人们对大脑如何完成这一任务知之甚少。这里提出的研究将大大推进我们对自然场景如何在大脑中表现的理解，并开始揭示大脑在快速准确地提取场景要点时所采用的计算策略。拟议活动的更广泛影响：拟议研究的高度跨学科性质需要心理学家，神经科学家和计算机视觉研究人员之间的密切互动。因此，该项目不仅有望增加不同学科之间的交流，而且还将为博士生提供真正的跨学科培训。PI致力于提供一个高度互动的研究环境，指导跨学科的学生，并培养跨学科的科学方法。此外，三个主要研究员中有两个是在传统上妇女代表性不足的领域工作的妇女，并致力于提高妇女在科学领域的代表性和知名度。最后，从这个项目中得出的原则可能会产生超出自然场景感知领域的影响。通过改进模式识别算法及其在功能磁共振成像数据中的应用，该项目将扩大神经科学家可用的工具集，这些工具集希望研究一系列复杂的人类行为，这些行为可能取决于大脑中微妙但分布的活动模式。