Neural Representations of Objects Across the Human Visual Pathway

人类视觉通路中物体的神经表征

• 批准号: 0642633
• 负责人: Frank Tong
• 金额: $ 63.75万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-15 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0642633&HistoricalAwards=false
• 关键词: Neural; Representations; Objects; Across; Human

People excel at recognizing objects across changes in position, size, viewpoint, lighting and general form, whereas computer recognition systems perform poorly when faced with such variable, unpredictable situations. Exactly how the human brain solves the computational challenges of object recognition is not well understood. To recognize an object, one must integrate the features, contours, and parts of an object into an organized whole, and then match these representations of an object's shape to items stored in memory. Somehow, the brain can solve this computational problem by extracting the stable, invariant properties of objects while disregarding superficial variations in the retinal image. With support from the National Science Foundation, Dr. Frank Tong and his colleagues at Vanderbilt University will investigate the neural bases of object recognition using functional magnetic resonance imaging (fMRI) and novel pattern classification methods adapted from machine learning. These studies will determine what types of information about objects are represented by cortical activity patterns across the human visual pathway, ranging from low-level visual areas that respond best to basic features, to high-level areas that respond best to complex objects. Rather than focusing exclusively on the high-level object-selective areas, this project emphasizes a different approach to understand how invariant representations of objects are formed. Studies will characterize the neural representation of objects at each stage of the visual pathway, from the primary visual cortex to anterior inferotemporal areas, to determine how object representations are transformed from one processing stage to the next. This research will help reveal how the brain solves the problem of object recognition, by transforming the raw retinal input into increasingly more flexible representations of the object through a process spanning many successive levels of the visual pathway. Results from these studies will provide inform current theories of object recognition. Understanding these neural bases is necessary to comprehend what can go wrong with object recognition in cases of learning disability, developmental disorder or brain injury (e.g., developmental or acquired dyslexia). It is likely that computer algorithms for recognizing objects will also be improved.

人们擅长识别位置、大小、视角、照明和一般形式变化的物体，而计算机识别系统在面对这些可变的、不可预测的情况时表现不佳。人类大脑如何解决物体识别的计算挑战还没有得到很好的理解。要识别一个物体，必须将物体的特征、轮廓和部分整合成一个有组织的整体，然后将物体形状的这些表示与存储在内存中的项目相匹配。不知何故，大脑可以通过提取物体的稳定不变的属性来解决这个计算问题，同时忽略视网膜图像中的表面变化。在美国国家科学基金会的支持下，范德比尔特大学的Frank Tong博士和他的同事将利用功能性磁共振成像（fMRI）和机器学习改编的新型模式分类方法来研究物体识别的神经基础。这些研究将确定在人类视觉通路中，从对基本特征反应最好的低级视觉区域到对复杂物体反应最好的高级区域，皮层活动模式代表了关于物体的哪些类型的信息。而不是专注于高层次的对象选择领域，这个项目强调了一种不同的方法来理解如何形成不变的对象表示。研究将描述从初级视觉皮层到前颞下区的视觉通路每个阶段的对象神经表征，以确定对象表征如何从一个处理阶段转换到下一个处理阶段。这项研究将有助于揭示大脑如何解决物体识别的问题，通过跨越视觉通路的许多连续层次的过程，将原始视网膜输入转化为越来越灵活的物体表征。这些研究的结果将为当前的物体识别理论提供信息。 理解这些神经基础对于理解在学习障碍、发育障碍或脑损伤的情况下物体识别会出现什么问题是必要的（例如，发育性或获得性阅读障碍）。计算机识别物体的算法也可能得到改进。