Visual Representation and Attention

描述（由申请人提供）：本提案的目的是测试关于结构和语义信息如何在人类视觉皮层中表征，以及这些表征如何被注意力调节的几个假设。该提案基于一项关键技术创新，即从功能MRI数据中估计定量基于体素的感受野（VRF）模型的非线性系统识别框架。这些VRF模型体现了关于视觉表现的具体假设，它们提供了可以测试和评估的明确预测。在Aim 1中，我们建议研究几个视网膜组织视觉区域（即V1， V2, V3， V4和枕侧）结构信息的表示。为了解决这个问题，我们将比较几种潜在的VRF模型，它们编码关于形状、运动和颜色的不同类型的信息。在第二项研究中，我们建议研究枕骨外侧前部非视网膜异位视觉皮层的语义表征。为了实现这一目标，我们将探索一系列语义编码模型，这些模型描述每个体素如何表示自然图像的语义内容（例如，图像是室内还是室外场景，或者它是否包含人脸等）。我们将使用这些语义VRF模型来研究先前研究中提出的功能区（例如，梭状回面部区域FFA），并表征目前功能未知的非视网膜皮质。在目标3中，我们建议研究空间和基于特征的注意力如何影响结构和语义信息的表示方式。我们将根据其对响应增益、方向和空间频率调谐以及语义调谐的影响来描述注意调制。这些实验将为视觉表征提供新的见解，并将产生新的计算编码模型，准确预测视觉皮层在自然视觉过程中的反应。

DESCRIPTION (provided by applicant): 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.