Visual Representation and Attention

总结： 这个提议的目的是检验几个关于结构和语义信息如何 在人类视觉皮层中的表现，以及这些表现如何被注意力调节。的 建议依赖于一个关键的技术创新，非线性系统识别框架， 从功能性MRI数据估计基于体素的定量感受野（VRF）模型。这些 VRF模型体现了关于视觉表征的特定假设，它们提供了清晰的 可以测试和评估的预测。在目标1中，我们提出研究的代表性 在几个视网膜定位组织的视觉区域中的结构信息（即，V1、V2、V3、V4和外侧 枕骨）。为了解决这个问题，我们将比较几个潜在的VRF模型， 关于形状，运动和颜色的信息在目标2中，我们提出研究语义 在枕外侧前部的非视网膜定位视觉皮层中的代表。为了实现这一目标，我们将 探索一系列语义编码模型，描述每个体素如何表示语义 自然图像的内容（例如，图像是室内场景还是室外场景，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，梭形面区FFA），并表征非视网膜定位的 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- 基于注意力可以影响结构和语义信息的表示方式。我们将 描述注意调制对反应增益、方向和空间的影响 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，准确地预测如何 视觉皮层在自然视觉时会做出反应。

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.