Determining the independence of face dimensions through an extended multidimensional signal detection theory

人们可以很容易地感知到人脸的几个独立属性，例如其身份，情感表达，年龄和运动。一个重要的问题是，大脑是否独立地处理这些特性，因此，例如，很容易识别一个朋友，不管她是高兴还是生气，移动她的眼睛，摇头，或者即使我们多年没有见过她。 虽然有影响力的人脸识别理论提出，一些人脸属性确实是独立于其他处理，文献回顾表明，它是充满矛盾的结果，可能是由于缺乏关键的实验控制。本项目旨在确定是否面临的身份，表情和运动的独立处理，使用行为和大脑的独立性措施。更好地了解健康个体如何感知面部属性，反过来又可以更好地了解某些疾病（如抑郁症、焦虑症、精神分裂症和自闭症）中面部感知障碍的原因。为了解决以前研究中相互矛盾的结果，研究人员提出在一系列实验中使用一种新颖的方法，该实验同时使用行为和大脑测量面部感知。首先，使用计算机图形学实现对呈现给参与者的面部属性的严格控制：创建面部和表情的三维模型，并从这些模型中获得图像和视频。由于计算机图形学的进步，这些图像和视频看起来非常自然，但它们的形状和运动可以高精度控制。其次，使用感知的数学模型来分析数据。这些模型对独立性等知觉加工特征进行了严格的定义，并提供了测量这些特征的方法，同时将其与参与者在行为任务中使用的决策策略分开。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

People can easily perceive several separate properties of a face, such as its identity, emotional expression, age, and movement. An important question is whether the brain processes each of those properties independently, so that, for example, it is easy to identify a friend regardless of whether she is happy, angry, moving her eyes, shaking her head, or even if we have not seen her in years. Although influential theories of face recognition propose that some face properties are indeed processed independently from others, a review of the literature shows that it is full of contradictory results, probably due to lack of critical experimental controls. The present project seeks to determine whether face identity, expression, and motion are processed independently, using both behavioral and brain measures of independence. A better understanding of how face properties are perceived in healthy individuals could in turn lead to a better understanding of the reason behind impairments in face perception in some disorders, such as depression, anxiety, schizophrenia, and autism.To resolve the contradictory findings from previous research, the investigator proposes to use a novel methodology in a series of experiments that use both behavioral and brain measures of face perception. First, tight control over the properties of faces presented to participants is achieved using computer graphics: three-dimensional models of faces and expressions are created, and images and videos are obtained from those models. Because of advances in computer graphics, such images and videos look quite naturalistic, but their shape and movements can be controlled with high precision. Second, mathematical models of perception are used to analyze the data. These models provide rigorous definitions of features of perceptual processing such as independence, and ways to measure those features while separating them from decision-making strategies used by participants in behavioral tasks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.