A Study of the Computational Space of Facial Expressions of Emotion

描述(申请人提供)：过去的研究非常成功地定义了面部表情是如何产生的，包括哪些肌肉运动创造了最常见的表情。然后，这些面部表情被我们的视觉系统解释。然而，人们对这些面部表情是如何识别的知之甚少。这项提议的首要目标是定义在视觉识别中使用的认知(计算)空间的形式和维度。特别是，这项提议将研究以下三个假设：虽然面部表情是由一组复杂的肌肉运动产生的，但通常很容易在不同的空间和时间分辨率下识别表情。然而，目前还不知道这些限制是什么。我们的第一个假设(H1)是，面部表情的识别可以在低分辨率和短时间曝光后实现。在目标1中，我们定义了实验来确定成功识别不同情绪需要多少像素和毫秒(Ms)。在低分辨率下可以快速识别情感表情的事实表明，使用了对图像处理具有鲁棒性的简单特征。我们的第二个假设(H2)是，面部表情的识别部分是通过对结构特征的分析来完成的。众所周知，结构线索在其他面孔识别任务中起着重要作用，但它们在情绪表达加工中的作用还不是很清楚。目标2将识别一些这样的配置线索。我们将使用人脸的真实图像、这些人脸图像的经过处理的版本和示意图。我们还知道，形状特征对面部表情(例如，快乐时嘴巴的弯曲)起到了作用。在目标3中，我们定义了一个基于形状的计算模型。我们的假设(H3)是，结构和形状特征被定义为与平均(或范数)脸的偏差，而不是被描述为一组独立的样本(诺斯替神经元)。这种计算空间的重要性不仅是为了进一步证明先前目标的结果，而且是为了做出新的预测，这些预测可以通过人类受试者的额外实验来验证。 与公共健康相关：了解面部表情是如何被我们的认知系统处理的，对于研究精神分裂症、自闭症和亨廷顿病的异常面部和情绪视觉处理将是重要的。此外，受虐待的儿童在识别情绪方面更敏锐，这表明他们对某些图像特征有更高的专业水平。确定认知系统使用了哪些功能将有助于开发减少其有害影响的协议。了解空间和时间分辨率的限制对低视力(敏锐度)的研究也很重要，低视力是几种眼病和正常衰老过程中的典型问题。

DESCRIPTION (provided by applicant): Past research has been very successful in defining how facial expressions of emotion are produced, including which muscle movements create the most commonly seen expressions. These facial expressions of emotion are then interpreted by our visual system. Yet, little is known about how these facial expressions are recognized. The overarching goal of this proposal is to define the form and dimensions of the cognitive (computational) space used in this visual recognition. In particular, this proposal will study the following three hypotheses: Although facial expressions are produced by a complex set of muscle movements, expressions are generally easily identified at different spatial and time resolutions. However, it is not know what these limits are. Our first hypothesis (H1) is that recognition of facial expressions of emotion can be achieved at low resolutions and after short exposure times. In Aim 1, we define experiments to determine how many pixels and milliseconds (ms) are needed to successfully identify different emotions. The fact that expressions of emotion can be recognized quickly at low resolution indicates that simple features robust to image manipulation are employed. Our second hypothesis (H2) is that the recognition of facial expressions of emotion is partially accomplished by an analysis of configural features. Configural cues are known to play an important role in other face recognition tasks, but their role in the processing of expressions of emotion is not yet well understood. Aim 2 will identify a number of these configural cues. We will use real images of faces, manipulated versions of these face images, and schematic drawings. It is also known that shape features play a role in facial expressions (e.g., the curvature of the mouth in happiness). In Aim 3, we define a shape-based computational model. Our hypothesis (H3) is that the configural and shape features are defined as deviations from a mean (or norm) face as opposed to being described as a set of independent exemplars (Gnostic neurons). The importance of this computational space is not only to further justify the results of the previous aims, but to make new predictions that can be verified with additional experiments with human subjects. PUBLIC HEALTH RELEVANCE: Understanding how facial expressions of emotion are processed by our cognitive system will be important for studies of abnormal face and emotion visual processing in schizophrenia, autism and Huntington's disease. Also, abused children are more acute at recognizing emotions, suggesting a higher degree of expertise to some image features. Identifying which features are used by the cognitive system will help develop protocols for reducing their unwanted effects. Understanding the limits in spatial and time resolution will also be important for studies of low vision (acuity), which are typical problems in several eye diseases and in the normal process of aging.