Visual Representation and Attention

视觉表现和注意力

• 批准号: 8448730
• 负责人: JACK L GALLANT
• 金额: $ 32.87万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8448730
• 关键词: Address; Affect; Anterior; Area; Attention; Categories; Classification; Code; Color; Complex; Computational algorithm; Computer Simulation; Data; Data Set; Devices; Dimensions; Disease; Face; Frequencies; Functional Magnetic Resonance Imaging; Goals; Hippocampus (Brain); Human; Image; Laboratories; Lateral; Literature; Measures; Modeling; Motion; Neurons; Paper; Population; Process; Psychophysics; Publishing; Quality of life; Response to stimulus physiology; Rest; Semantics; Shapes; Signal Transduction; Sorting - Cell Movement; Space Perception; Stimulus; System; Testing; Training; Vision; Visual; Visual Cortex; area V1; attentional modulation; base; blood oxygen level dependent; color processing; extrastriate visual cortex; fusiform face area; hemodynamics; improved; innovation; insight; interest; movie; neurophysiology; novel; novel strategies; predictive modeling; receptive field; relating to nervous system; research study; response; retinotopic; therapy design; visual information

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.

概括： 该提案的目标是测试关于结构和语义信息如何 是在人类视觉皮层中表示的，以及这些表示是如何通过注意力来调节的。这 该提案基于一项关键技术创新，即非线性系统识别框架 根据功能 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，我们建议调查 几个视网膜局部组织的视觉区域（即 V1、V2、V3、V4 和横向区域）的结构信息 枕骨）。为了解决这个问题，我们将比较几种编码不同的潜在 VRF 模型 有关形状、运动和颜色的各种信息。在目标 2 中，我们建议研究语义 枕外侧前非视网膜专题视觉皮层的表征。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，图像是室内还是室外场景，或者是否包含 脸等）。我们将使用这些语义 VRF 模型来研究功能感兴趣区域 先前研究中提出的（例如，梭形面部区域FFA），并表征非视网膜专题 皮质，其功能目前未知。在目标 3 中，我们建议研究空间和特征如何 基于注意力可以影响结构和语义信息的表示方式。我们将 根据注意力调节对响应增益、方向和空间的影响来表征注意力调节 频率调整和语义调整。这些实验将为视觉提供新的见解 表示，并将产生新的计算编码模型，准确预测如何 视觉皮层在自然视觉期间做出反应。