Mapping the representation of shape dimensions across the visual hierarchy

描述（由申请人提供）：视觉研究的一个核心挑战是了解大脑如何将视网膜上的光模式转化为有关物体形状的有用信息。形状是比颜色、纹理或颜色更可靠的物体标识指示器。 尽管形状在视觉系统中的表现形式还不为人所知，但它在视觉系统中的表现形式仍然是一个未知数。视觉心理学的理论表明，视觉系统以几何定义的部分或二维图像特征或片段来表示物体。哪种理论最好地描述了视觉系统中形状的表征？心理学的争论一直围绕着定义形状的表征，但神经生理学和成像研究揭示了视觉系统中的区域层次。从这个角度来看，相关的问题不是视觉表示是基于形状还是基于视图，而是在哪些区域中表示哪些特征或尺寸。在这个问题上已经取得了一些进展，但许多形状表征的研究使用的刺激，只有在一个或两个维度上的变化。受约束的刺激变化使得不可能知道神经元（或fMRI体素）是否可能代表在特定刺激集中不变化的其他维度。为了更好地描述视觉系统所代表的形状特征或维度，有必要使用在多个维度上变化的刺激。自然景物可以作为刺激物，但自然景物中的形状变化很难参数化。在这里，我们建议使用Blender，一个3D图形和动画程序来创建一个广泛变化的刺激集。刺激将由真实的渲染对象组成，并且将在许多维度上变化（例如，边界曲率，部件布置和姿势）。我们将使用来自Blender的元数据来参数化每个刺激图像的这些形状特征。我们将使用Gallant实验室的非线性系统识别（NLSI）框架来发现这些形状参数如何反映在整个视觉系统不同区域的BOLD fMRI大脑反应中。因此，拟议的工作将提供一个关键的测试的一般性 过去的工作，形状表示，以及更完整的理解，形状尺寸表示在视觉领域。这项提议符合NIH的使命，即了解正常的视觉功能。更深入地了解整个视觉层次的形状表征将促进视觉修复的发展，而对视觉处理的深入了解可以提供对其他感官信息整合的见解。

DESCRIPTION (provided by applicant): A central challenge of vision research is to understand how the brain translates patterns of light on the retina into useful information about the shape of objects. Shape is a more reliable indicator of object identity than color, texture, or any other visual property, yet the representation of shape in the visual system is still poorly understood. Theories from visual psychology have suggested that the visual system represents objects in terms of geometrically-defined parts, or in terms of two-dimensional image features or fragments. Which theory best describes the representation of shape in the visual system? The debate in psychology has revolved around defining the representation of shape, but neurophysiology and imaging studies have revealed a hierarchy of regions in the visual system. From this perspective, the relevant question is not whether the visual representation is shape-based or view-based, but which features or dimensions are represented in which areas. Some progress has been made on this question, but many studies of shape representation use stimuli that only vary in one or two dimensions. Constrained stimulus variation makes it impossible to know whether a neuron (or fMRI voxel) might represent other dimensions that do not vary in a particular stimulus set. In order to better describe what shape features or dimensions the visual system represents, it is necessary to use stimuli that vary in multiple dimensions. Natural scenes can be used as stimuli, but the shape variation in natural scenes is difficult to parameterize. Here, we propose to create a widely varied stimulus set using Blender, a 3D graphics and animation program. The stimuli will consist of realistic rendered objects, and will vary in many dimensions (e.g. boundary curvature, part arrangement, and pose). We will use meta-data from Blender to parameterize these shape features for each stimulus image. We will use the Gallant Laboratory's Non-Linear System Identification (NLSI) framework to discover how these shape parameters are reflected in BOLD fMRI brain responses in different regions throughout the visual system. The proposed work will thus provide a critical test of the generality of past work on shape representation, as well as a more complete understanding of which shape dimensions are represented in which visual areas. This proposal is in keeping with the NIH's mission to understand normal visual function. A deeper understanding of shape representation across the visual hierarchy will facilitate the development of visual prosthetics, and insight into visual processing can provide insights into information integration in other senses.