Representation of information across the human visual cortex

 描述（由申请人提供）：人类视觉系统被组织为并行的、分层的网络，并且视觉处理的连续阶段似乎表示形状相关的和语义信息的日益复杂的方面。然而，形状相关和语义信息在视觉层次结构中的表现方式仍然知之甚少。这个建议的主要目标是了解有关对象形状和语义类别的信息是如何在中高层视觉区域中显式表示的。为了解决这个重要问题，我们建议进行一系列的人类功能性磁共振成像（fMRI）研究，使用合成和自然的电影。数据将通过一种强大的体素建模（VM）方法进行分析，该方法是我的实验室在过去几年中开发的。在目标1中，我们建议测量人工合成的自然主义电影引起的人脑活动，并使用VM来评估和比较几个相互竞争的理论在整个视觉皮层的形状表示。在目标2中，我们建议使用VM来评估和比较语义表示的竞争理论。在目标3中，我们建议使用机器学习和VM来发现形状和语义表示的新方面。这些实验将提供关于视觉信息在视觉皮层中的表征的基本新见解。

 DESCRIPTION (provided by applicant): The human visual system is organized as a parallel, hierarchical network, and successive stages of visual processing appear to represent increasingly complicated aspects of shape-related and semantic information. However, the way that shape-related and semantic information is represented across much of the visual hierarchy is still poorly understood. The primary goal of this proposal is to understand how information about object shape and semantic category is represented explicitly across mid- and high-level visual areas. To address this important issue we propose to undertake a series of human functional MRI (fMRI) studies, using both synthetic and natural movies. Data will be analyzed by means of a powerful voxel-wise modeling (VM) approach that has been developed in my laboratory over the past several years. In Aim 1 we propose to measure human brain activity evoked by synthetic naturalistic movies, and to use VM to evaluate and compare several competing theories of shape representation across the entire visual cortex. In Aim 2 we propose to use VM to evaluate and compare competing theories of semantic representation. In Aim 3 we propose to use machine learning and and VM to discover new aspects of shape and semantic representation. These experiments will provide fundamental new insights about the representation of visual information across visual cortex.