Features and Objects in the Visual System

视觉系统被分为专门用于对象识别的腹侧流和专门用于运动处理和视觉运动指导(例如抓取对象)的背侧流。由于这种功能分离，在腹侧流中存在许多客体表征模式，在背侧流中也有许多运动加工模型，但关于背侧流中客体表征的性质或存在的研究大多缺乏研究。根据功能上的不同，这两个流使用这些对象的方式不同，因此表示它们的方式也不同。也就是说，腹侧流将是与物体识别相关的处理特征，包括形状、颜色、纹理等。而背侧流处理与动作相关的特征，例如空间位置、运动、用于抓取的3D形状等。因此，将相关特征正确地整合或绑定到每一流中变得非常重要。这两个流都处理特征，这些特征选择神经元需要连接或集成到分布式网络中，以形成对象的表示。特征整合，也被称为绑定问题，是神经科学中一个长期突出的问题。*本提案中的项目研究了特征整合和背流中对象表征的性质。他们将确定哪些特征被整合到背流中，这些特征如何被整合到背流对象表征中，背流中依赖于特征的处理阶段与那些作用于对象表征的阶段，以及如何在背流中选择和处理对象表征。该方案中的研究是重要的，因为他们的结果将产生新的可测试的背流处理模型，以及测试和约束现有的特征集成和对象表征模型。这项研究对视觉科学家、认知和系统神经科学家、计算建模和计算机视觉研究人员很重要，这些领域都是加拿大的强项。

The visual system is divided into a ventral stream specialized for object recognition and a dorsal stream specialized for motion processing and visuomotor guidance such as grasping objects. Because of this functional separation, there are many models of object representation in the ventral stream and many models of motion processing in the dorsal stream, but research has been mostly devoid of investigations as to the nature or existence of object representations in the dorsal stream. Based on the difference in functionality, the two streams would use these objects differently and therefore represent them differently too. That is, the ventral stream would be processing features relevant for object recognition, including shape, color, texture, etc. While the dorsal stream processes features relevant for action, such as spatial location, motion, 3d shape for grasping, etc. Thus, it becomes very important to correctly integrate or bind' the relevant features into each stream. Both streams process features, and these feature-selective neurons need to be connected or integrated into a distributed network to form a representation of the object. Feature integration, aka the Binding Problem, is a long outstanding problem in neuroscience.******The projects in this proposal investigate the nature of feature integration and object representations in the dorsal stream. They will determine which features are integrated into the dorsal stream, how those features are integrated into a dorsal stream object representation, the stages of processing in the dorsal stream that are dependent on features versus those that work on object representations, and how object representations are selected and attended to within the dorsal stream. The studies in the proposal are important in that their results will produce new testable models for dorsal stream processing as well as test and constrain current models of feature integration and object representation. This research is important to vision scientists, cognitive and systems neuroscientists, computational modeling and computer vision researchers, which are all areas of strength in Canada.