Visual Representation and Attention

摘要： 这项提议的目标是测试几个关于结构和语义信息如何 在人类视觉皮质中的表达，以及注意力是如何调节这些表达的。这个 该提案基于一项关键技术创新，即一种用于 从功能磁共振数据估计基于体素的感受场(VRF)模型。这些 VRF模型体现了关于视觉表示的特定假设，并且它们提供了清晰的 可以测试和评估的预测。在目标1中，我们建议研究 几个视网膜组织的视觉区域(即，V1、V2、V3、V4和侧方)中的结构信息 枕骨)。为了解决这个问题，我们将比较几个潜在的VRF模型，它们编码不同的 关于形状、运动和颜色的各种信息。在目标2中，我们建议研究语义 在枕侧前方的非视网膜视皮层中的表现。为了实现这一目标，我们将 探索一系列描述每个体素如何表示语义的语义编码模型 自然图像的内容(例如，图像是室内还是室外场景，或者它是否包含 面孔等)。我们将使用这些语义VRF模型来研究感兴趣的功能区域 在以前的研究中提出的(例如，梭形面部区域FFA)，并表征非视网膜斜视 皮质，其功能目前尚不清楚。在目标3中，我们建议研究空间和特征如何- 基于注意力会影响结构和语义信息的呈现方式。我们会 根据注意调节对反应增益、定向和空间的影响来表征注意调节 频率调谐和语义调谐。这些实验将提供关于视觉的新见解 表示，并将产生新的计算编码模型，准确地预测 视觉皮质在自然视觉过程中会做出反应。

Summary: The goal of this proposal is to test several hypotheses about how structural and semantic information is represented in human visual cortex, and how these representations are modulated by attention. The proposal rests on a key technical innovation, a nonlinear system identification framework for estimating quantitative voxel-based receptive field (VRF) models from functional MRI data. These VRF models embody specific hypotheses about visual representation, and they provide clear predictions that can be tested and evaluated. In Aim 1 we propose to investigate the representation of structural information in several retinotopically-organized visual areas (i.e., V1, V2, V3, V4 and lateral occipital). To address this issue we will compare several potential VRF models that encode different sorts of information about shape, motion and color. In Aim 2 we propose to investigate semantic representation in non-retinotopic visual cortex anterior to lateral occipital. To accomplish this we will explore a range of semantic encoding models that describe how each voxel represents the semantic content of natural images (e.g., whether an image is an indoor or outdoor scene, or whether it contains faces, etc.). We will use these semantic VRF models to investigate functional regions-of-interest proposed in previous studies (e.g., the fusiform face area FFA), and to characterize non-retinotopic cortex whose function is currently unknown. In Aim 3 we propose to examine how spatial and feature- based attention can affect the way structural and semantic information are represented. We will characterize attentional modulation in terms of its effects on response gain, orientation and spatial frequency tuning, and semantic tuning. These experiments will provide new insights about visual representations, and will produce new computational encoding models that accurately predict how visual cortex responds during natural vision.