Neural mechanisms underlying categorical perception in the processing of faces

面部感知对于正常的社会互动至关重要，人类观察者擅长快速可靠地将视觉模式分类为人脸或非人脸。自闭症和其他发育障碍证明，即使是这种能力的细微损伤也会造成毁灭性的后果。尽管面部感知的神经机制一直是灵长类动物电生理学和脑成像研究的主要焦点，但大脑如何处理面部的计算机制仍然很不清楚。在美国国家科学基金会（National Science Foundation）的支持下，达特茅斯学院（Dartmouth College）的Ming b孟博士正在通过综合心理物理学、人脑成像、统计数据挖掘和计算机视觉等技术来解决这个问题。首先，这个项目是测量大脑对用计算机视觉编译的图像集的反应。这些图像在图像级别上的面部相似性各不相同，从无脸到真脸不等。计算机视觉系统可能会错误地将许多像脸一样的非脸分类为脸。相比之下，分类感知使人类观察者能够对这些图像是脸还是非脸做出明确的感知判断。其次，为了理解人脸处理神经网络，理解多个脑区之间的因果关系比理解每个单独脑区的平均反应激活更为重要。本项目采用最先进的数据挖掘技术，为各种因素之间的直接和间接关系的动态因果建模提供基础，例如，低级视觉特征，面部外观和面部/非面部分类，将神经处理阶段从初级视觉分析连接到感知决策。最后，为了确定自底向上和自顶向下对分类面部感知的调节，复杂的心理物理范式被用来研究视觉意识和分类面部分析的神经相关因素之间的潜在相互作用。通过这种方法，可以将假设独立于视觉意识的刺激驱动和前馈面部处理模型与涉及认知反馈调节和视觉意识的模型进行比较。这个项目的结果有望导致更好地理解人类大脑如何在面部感知的背景下处理视觉信息，这一领域提供了感觉组织最引人注目的例子之一。了解人类视觉系统如何分析人脸的过程可能最终导致人工人脸识别系统的成功设计。此外，连续的图像级面部相似性分析与面部感知中的二元分类判断是快速识别面部的关键社会能力的基础。将这两种分析分开，可能有助于描述涉及面部感知缺陷的发育障碍的神经病理学特征。基于这些结果，我们可以有策略地设计一种认知疗法来精确定位这些缺陷，从而帮助患有这些疾病的儿童和成人。

Face perception is crucial for normal social interactions, and human observers excel at rapidly and reliably categorizing visual patterns as faces or non-faces. Even subtle impairment in this ability can have devastating consequences, as evidenced by autism and other developmental disorders. Although the neural mechanisms underlying face perception have been a major focus of primate electrophysiology and brain-imaging research, the computational mechanisms underlying how the brain processes faces are still far from clear. With support from the National Science Foundation, Dr. Ming Meng of Dartmouth College is addressing this question by synthesizing techniques drawn from psychophysics, human brain imaging, statistical data mining, and computer vision. First of all, this project is measuring brain activation in response to image sets that have been compiled by using computer vision. These images vary in their image-level facial similarity, ranging from non-faces to genuine faces. Computer vision systems may falsely categorize many of the face-like non-faces as faces. By contrast, categorical perception enables human observers to make unambiguous perceptual judgments on whether these images are faces or non-faces. Second, to understand the face processing neural network, it is more important to understand causal relationships among several brain regions than average response activation of each separate brain region. This project is applying state-of-the-art data mining techniques to provide the basis for dynamic causal modeling of the direct and indirect relationships among various factors, such as, low-level visual features, face semblance and the face/non-face categorization, linking neural processing stages from primary visual analysis to perceptual decision. Finally, to determine bottom-up versus top-down modulation on categorical face perception, sophisticated psychophysical paradigms are being used to investigate potential interactions between visual awareness and the neural correlates of categorical facial analysis. Through this approach, stimulus-driven and feed-forward models of face processing that are assumed to be independent of visual awareness can be compared to models that involve cognitive feedback modulations and visual awareness. The results of this project are expected to lead to a better understanding of how the human brain processes visual information in the context of face perception, a domain that provides one of the most compelling examples of sensory organization. Understanding the process of how the human visual system analyzes faces may ultimately lead to the successful design of artificial face recognition systems. Moreover, continuous image-level facial similarity analysis versus binary categorical judgment in face perception underlies the crucial social ability to rapidly recognize a face. Teasing the two analyses apart may help characterize the neural pathology in the developmental disorders that involve face perception deficits. Based on the results, a cognitive therapy can be strategically designed to pinpoint such deficits, and to therefore help children and adults with these disorders.