Mapping the representation of shape dimensions across the visual hierarchy

在视觉层次结构中映射形状尺寸的表示

• 批准号: 8317388
• 负责人: Mark Daniel Lescroart
• 金额: $ 4.92万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8317388
• 关键词: Affect; Area; Attention; Behavior; Brain; Brain region; Categories; Collection; Color; Conflict (Psychology); Conscious; Data; Data Set; Dimensions; Functional Magnetic Resonance Imaging; Goals; Image; Individual Differences; Laboratories; Learning; Light; Maps; Medial; Mission; Modeling; Neurons; Pattern; Perception; Property; Prosthesis; Psychology; Retina; Shapes; Staging; Stimulus; Structure; System; Testing; Texture; Translating; Variant; Vision; Vision research; Visual; Visual Cortex; Visual system structure; Work; animation; base; extrastriate visual cortex; information model; insight; neurophysiology; object recognition; object shape; programs; research study; response; theories; two-dimensional; vision development; visual process; visual processing

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. PUBLIC HEALTH RELEVANCE: The proposed project is in keeping with NIH's mission to understand the normal function of the visual cortex. Activity in shape-selective visual areas correlates with subjective perceptions and causally affects behavior; thus the study of shape representation is truly the study of visual consciousness. A deeper understanding of shape representation across the visual system will contribute to the development of visual prosthetics, serve as a model for information integration in other senses, and potentially set the stage for a more thorough understanding of individual differences in perception and attention.

描述(申请人提供)：视觉研究的一个中心挑战是了解大脑如何将视网膜上的光模式转化为关于物体形状的有用信息。形状是比颜色、纹理或颜色更可靠的对象身份指示器 任何其他视觉特性，然而形状在视觉系统中的表示仍然鲜为人知。视觉心理学的理论认为，视觉系统用几何定义的部分或二维图像特征或碎片来表示物体。哪种理论最好地描述了视觉系统中形状的表现？心理学上的争论一直围绕着对形状表征的定义，但神经生理学和成像研究揭示了视觉系统中区域的层次结构。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，但许多关于形状表征的研究使用的刺激只在一到两个维度上变化。受限的刺激变化使得不可能知道神经元(或功能磁共振体素)是否可能代表在特定刺激集中不变的其他维度。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。自然场景可以作为刺激物，但自然场景中的形状变化很难参数化。在这里，我们建议使用3D图形和动画程序Blender来创建一个种类繁多的刺激集。刺激将由逼真的渲染对象组成，并且将在许多维度(例如，边界曲率、零件排列和姿势)中变化。我们将使用Blender的元数据对每个刺激图像的这些形状特征进行参数化。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大胆fMRI大脑反应中。因此，拟议的工作将提供对一般性的关键测试 对过去关于形状表示的工作的总结，以及对在哪些视觉区域中表示哪些形状维度的更完整的理解。这项建议与NIH了解正常视觉功能的使命是一致的。更深入地了解视觉层次中的形状表征将促进视觉假体的发展，而对视觉处理的洞察可以提供对其他感觉的信息整合的洞察。 与公共健康相关：拟议的项目与NIH了解视觉皮质正常功能的使命是一致的。形状选择性视觉区域的活动与主观知觉相关，并对行为产生因果影响，因此对形状表征的研究才是对视觉意识的研究。更深入地了解视觉系统中的形状表征将有助于视觉假体的发展，作为其他感觉信息整合的模型，并可能为更彻底地理解个体在感知和注意方面的差异奠定基础。