Transmission of Information in the Visual System

我们如何从我们检测到的特征中识别出一个物体？了解大脑如何识别 物体可能会让我们深入了解大脑是如何解决一般问题的。目标识别问题具有 经受住了一个世纪的尝试，但我们带来了新的工具--上一次赠款时期的成果--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。 目标1.使用拥挤和其他操作来描述阅读过程中的三个平行过程 正常读者和阅读困难的读者。 目标2.用概率求和法计算特征数。从解释引申出传统概率求和 为了还能解释检测对象的识别，我们获得了一个新的工具，可以让我们计算出 观察者必须检测到的特征才能识别。 目标3.通过对观察者的反应进行计算机建模来获取观察者的分类算法 数以千计的白噪声字母。我们使用统计学习理论来构建一个分类器，它解释了 人类的表现。观察者将噪声中的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。我们使用一种强大的 统计学习算法，创建一个简单的分类器，最好地模拟人类的表现。 目的4.功能磁共振成像：字母在大脑的什么地方被识别？关联左侧“Letter”区域的激活 梭状回和其他地方，有两个通过心理生理发现的字母识别签名：FAST 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共同揭示 当观察者识别一个物体时，什么是在大脑中计算出来的。

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.