Transmission of Information in the Visual System

描述(由申请人提供)：如何从我们检测到的特征中识别对象？了解大脑是如何识别物体的，可能会让我们从总体上洞察大脑是如何解决问题的。物体识别问题经受住了一个世纪的尝试，但我们带来了新的工具--上一次拨款期间的成果--使我们能够勾勒出解决方案的轮廓。四种新的、不同的方法汇聚在一个答案上。目的1.使用拥挤和其他操作来描述正常读者和阅读困难读者在阅读过程中的三个平行过程。目标2.用概率求和法计算特征数。将传统的概率求和从仅解释检测扩展到也解释目标识别，我们获得了一个新的工具，允许我们计算观察者为了识别而必须检测的特征的数量。目的3.通过计算机模拟观察者对白噪声中数千个字母的反应来获取观察者的分类算法。我们使用统计学习理论来构建一个能够解释人类表现的分类器。观察者将噪声中一个字母的数千幅图像中的每一幅分类为“a”、“b”或“c”等。这些分类是可以告诉我们观察者正在做什么的数据。我们使用强大的统计学习算法来创建一个简单的分类器，该分类器对人类的表现进行了最佳模拟。目的4.功能磁共振成像：字母在大脑的什么地方被识别？将左侧梭状回和其他区域的“字母”区域的激活与字母识别的两个心理生理特征相关联：快速学习和频道频率。因此，来自认知、感知、统计学习理论和生理学的技术结合在一起，将揭示当观察者识别一个对象时，大脑中哪里计算出什么。

DESCRIPTION (provided by applicant): How do we identify an object from the features that we detect? Understanding how the brain recognizes objects might give insight into how the brain solves problems in general. The object recognition problem has withstood a century of attempts, but we bring new tools - fruits of the last grant period - that allow us to sketch the outlines of a solution. Four approaches, all new and different, converge on one answer. AIM 1. Use crowding, along with other manipulations, to characterize three parallel processes in reading by normal and dyslexic readers. AIM 2. Count features by probability summation. Extending traditional probability summation from explaining just detection to also explain object identification, we acquire a new tool, allowing us to count the number of features the observer must detect in order to identify. AIM 3. Capture the observer's classification algorithm by computer modeling of the observer's responses to thousands of letters in white noise. We use statistical learning theory to build a classifier that accounts for human performance. The observer classifies each of several thousand images of a letter in noise as "a", "b", or "c", etc. These classifications are data that can tell us what the observer is doing. We use a powerful statistical learning algorithm to create a simple classifier that best models human performance. AIM 4. fMRI: Where in the brain are letters identified? Correlate the activation of the "letter" area in the left fusiform gyrus, and elsewhere, with two psychophysically-discovered signatures of letter identification: fast learning and channel frequency. Thus techniques from cognition, perception, statistical learning theory, and physiology together will reveal what is computed where, in the brain, when an observer identifies an object.