Transmission of Information in the Visual System

我们如何从我们检测到的特征中识别物体？了解大脑如何识别 物体可能会让我们了解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--上一个赠款期的成果--使我们能够 勾勒出解决方案的轮廓。四种方法，都是新的和不同的，集中在一个答案上。 AIM 1.使用拥挤，沿着与其他操作，表征三个平行的过程，在阅读， 正常和有阅读障碍的读者。 AIM 2.通过概率求和对特征进行计数。扩展了传统的概率求和， 只是检测也解释对象识别，我们获得了一个新的工具，使我们能够计算的数量， 观察者必须发现的特征。 AIM 3.通过对观察者的响应进行计算机建模来捕获观察者的分类算法， 成千上万的字母在白色噪音中。我们使用统计学习理论来构建一个分类器， 人的表现。观察者将噪声中的几千个字母图像中的每一个分类为“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，以创建一个简单的分类器，最好的模型人类的表现。 AIM 4.功能性磁共振成像：字母在大脑的哪个部位被识别？关联左侧“字母”区域的激活 梭状回和其他地方，有两个精神病理学发现的字母识别特征：快 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将揭示 当一个观察者识别一个物体时，在大脑中计算出什么。

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.