Modeling Face Perception

描述（由申请人提供）：在理解复杂的视觉对象（特别是人脸）如何由皮层处理方面已经取得了很大进展。与此同时，复杂的神经网络模型已经开发出来，可以完成这些皮层区域所需的许多相同任务。这个建议的目的是扩展这些简化模型的理解，在何种程度上面部表情处理是“普遍的”，与它的程度上是经验介导的。特别是，我们专注于阐明以下问题，通过建模：（1）理解表情识别的文化差异：对比其他种族效应与文化展示规则的影响;（2）理解我们如何成为面孔专家。“为什么梭状回的同一区域会被用于面部以及我们可能擅长的其他视觉任务？(3)了解面部专业知识的动态：如何规划眼球运动以进行有效的特征提取？在每种情况下，我们已经开发或将开发一个神经计算模型的过程。文化和其他种族的经验将由我们模型的内部表征和训练信号的组成来建模。面部专业知识被建模为不同的任务要求（所需的分类水平不同）和训练时间的组合。眼动建模将基于新颖和传统的方法，用于提取每个任务的面部信息位置。我们将制定一个理论标准眼跳目标的脸之间的互信息特征值和任务所需的类别的基础上。我们还将进行行为实验来测试我们模型的预测。该项目将阐明面部的表现和大脑处理的方式，并应提供对面部处理缺陷（如面容失认症）潜在问题的见解。

DESCRIPTION (provided by applicant): There has been a great deal of progress in understanding how complex visual objects, in particular, human faces, are processed by the cortex. At the same time, sophisticated neural network models have been developed that do many of the same tasks required by these cortical areas. The aim of this proposal is to extend these simplifying models toward an understanding of the extent to which facial expression processing is "universal," versus the extent to which it is experientially mediated. In particular, we focus upon elucidating the following issues through modeling: (1) Understanding cultural variation in expression recognition: contrasting the influence of other race effects versus cultural display rules; (2) Understanding how we become face "experts." Why does the same region of the Fusiform Gyrus get recruited for faces as well as other visual tasks we may be expert in? (3) Understanding the dynamics of facial expertise: How are eye movements planned for efficient feature extraction? In each case, we have developed or will develop a neurocomputational model of the process. Cultural and other-race experience will be modeled by the composition of the internal representations and the training signals of our model. Facial expertise is modeled as a combination of different task requirements (varying the level of categorization required) and length of training. Eye movement modeling will be based on novel and traditional methods for extracting the informative locations on the face for each task. We will develop a theoretical criterion for saccade targets the face based upon mutual information between feature values and the categories required for the task. We also will be performing behavioral experiments to test the predictions of our model. The project will shed light on the way faces are represented and processed by the brain, and should give insights into the problems underlying deficits in face processing such as prosopagnosia.