Transmission of Information in the Visual System

描述（由申请人提供）：我们如何从我们检测到的特征中识别一个物体？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。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——上一个拨款时期的成果——使我们能够勾勒出解决方案的轮廓。四种全新而不同的方法汇聚在一个答案上。目的1。使用拥挤，连同其他操作，以表征三个平行的过程阅读正常和阅读困难的读者。目标2。通过概率求和来计数特征。将传统的概率求和从解释检测扩展到解释对象识别，我们获得了一种新的工具，允许我们计算观察者为了识别而必须检测的特征的数量。目标3。通过对观察者对数千个白噪声字母的反应进行计算机建模，捕捉观察者的分类算法。我们使用统计学习理论来建立一个分类器来解释人类的表现。观察者将噪声中的数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在做什么的数据。我们使用一个强大的统计学习算法来创建一个简单的分类器，最好地模拟人类的表现。目标4。fMRI：字母在大脑的哪个部位被识别？将左梭状回和其他地方的“字母”区域的激活与两种心理物理学发现的字母识别特征：快速学习和通道频率联系起来。因此，来自认知、知觉、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，在大脑的哪个位置计算了什么。

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.