Neural representations underlying visual perception of objects and faces

视觉对象在人脑中是如何表现和识别的？我的研究计划旨在回答这个广泛的问题。在过去的40年里，心理物理学、电生理学以及最近的神经成像研究已经破译了视觉处理早期阶段的许多方面。我们现在知道，视觉感知始于将图像分解成它们最基本的属性，如空间频率和方向。对于能够识别复杂视觉对象的视觉处理的后期阶段，我们所知的要少得多。我们建议使用在低级视觉研究中起作用的方法的等价物来检查涉及不变视觉对象识别的高级过程。*识别对象而忽略与其身份无关的变化的能力被称为不变性。例如，人类观察者在识别字母时是尺度不变的。因此，他们能够阅读大范围视觉尺寸的文本，从非常大的例如广告牌上的文本到非常小的例如小字的文本。没有不变，就不可能有真正的承认。人脑完成不变识别的精湛能力与最先进的人工识别系统的能力形成了鲜明对比。因此，它是神经科学中最令人兴奋的开放问题之一，发现如何在人脑中实现不变和稳健的对象识别。这一领域的突破将对与医学界合作开发可能扰乱面部处理的疾病(如面孔失认症和自闭症)的康复计划以及与工程界合作实现强大的自动识别系统产生深远影响。******

How are visual objects represented and recognized in the human brain? My research program is targeted to answer this broad question. In the last 40 years, psychophysics, electrophysiology and, more recently, neural imaging studies have deciphered many aspects of the early stages of visual processing. We now know that visual perception starts with a decomposition of images into their most basic properties such as spatial frequency and orientation. A lot less is known about the later stages of visual processing that enable recognition complex visual objects. We propose to use methodological equivalents of approaches that have been instrumental in studies of lower-level vision to examine higher-level processes involving invariant visual object recognition.****The ability to recognize an object while ignoring variations that do not pertain to its identity is referred to as invariance. For example, human observers are scale-invariant in recognizing letters. Thus they are able to read text at a wide range of visual sizes, from very large, e.g., on a billboard, to very small, e.g., fine-print. Without invariance, there can be no real recognition. The exquisite ability of the human brain to accomplish invariant recognition stands in stark contrast to the capabilities of state-of-the-art artificial recognition systems. It is therefore one of the most exciting open problems in neuroscience to discover how invariant and robust object recognition is achieved in the human brain. Breakthroughs in this area will have far-reaching implications for collaborative efforts with the medical community to develop rehabilitation programs for diseases that may disrupt face processing such as prosopagnosia (face blindness) and autism, and with the engineering community towards realizing robust automated recognition systems. ******