Face Impressions: Neuroscience & VR Training and Research

描述（由申请人提供）：面部外貌强烈影响第一印象，具有显著的一致性，并产生显著的社会后果。长期目标是解释正常个体之间的合意印象，以及自闭症等疾病个体之间的不合意印象。有一种假设是，那些通过婴儿、不健康或身份的面部特征准确揭示的品质，被过度概括为那些面部结构与婴儿、特定健康水平或特定身份相似的人。具体研究目的有四个：1)连接主义模型检验了面部身份过度概括假说（FIOH），即对陌生人的反应随其与已知个体的面部相似性的变化而变化的趋势有助于社会分类效应；2)功能性磁共振成像（fMRI）研究了从每个过度概括假设中预测的人脸的大脑激活模式；3)广义单纯暴露范式考察熟悉一个类别的面部原型是否会改变新类别成员的印象，正如FIOH预测的那样；4)虚拟现实方法通过不同社会情境下对人脸的行为反应来检验人脸过度概括假说。四个训练目标是：1)增加神经系统计算机建模的概念基础知识和用于实现神经网络仿真的计算机软件；2)增加大脑结构和功能以及功能磁共振成像技术的知识；3)学习使用虚拟环境来评估不同面部品质的感知行为特征；4)整合各种方法，为面部过度泛化假设提供更复杂的检验。研究和培训受益于优秀的实验室设施，最先进的设备，以及与布兰代斯大学和MGH-NMR中心的杰出科学家的互动。整合社会心理学、计算机科学和神经科学有望推进对社会感知的理解。对面部信息的关注提供了一个新的理论视角，表明面部的内在属性有助于社会偏见，而社会偏见在很大程度上被视为社会结构。它还提供了一个研究基础，可以阐明社会缺陷的神经基础，如自闭症，并开发干预技术来改变对面孔的反应。

DESCRIPTION (provided by applicant): Facial appearance strongly influences first impressions, which show remarkable consensus and yield significant social consequences. The long-range objective is to explain consensual impressions among normal individuals and their absence in individuals with disorders such as autism. It is hypothesized that qualities that are accurately revealed by facial features that mark babies, unfitness, or identity are overgeneralized to people whose facial structure resembles that of babies, a particular level of fitness, or a particular identity. Four specific research aims are: 1)connectionist modeling tests the facial identity overgeneralization hypothesis (FIOH) that the tendency for responses to strangers to vary with their facial resemblance to known individuals contributes to social categorization effects; 2)functional Magnetic Resonance Imaging (fMRI) investigates brain activation patterns in response to faces that are predicted from each of the overgeneralization hypotheses; 3)generalized mere exposure paradigms investigate whether familiarization with the facial prototype of a category changes impressions of new category members, as predicted by the FIOH; 4)virtual reality methods test face overgeneralization hypotheses through behavioral responses to faces in various social contexts. Four training aims are: 1) increased knowledge of the conceptual foundations of computer modeling of neural systems and the computer software used to implement neural network simulations; 2) increased knowledge of brain structure and function and fMRI techniques; 3) learning to use virtual environments to assess the perceived behavioral affordances of different facial qualities; 4) integrating various methods to provide more sophisticated tests of the face overgeneralization hypotheses. The research and training benefit from excellent laboratory facilities, state of the art equipment, and interactions with outstanding scientists at Brandeis University and the MGH-NMR Center. Integrating social psychology, computer science, and neuroscience promises to advance the understanding of social perception. The focus on facial information offers a novel theoretical perspective, demonstrating that intrinsic properties of faces contribute to social biases that have been largely viewed as social constructions. It also provides a foundation for research that may elucidate neural bases of social deficits, as in autism, and develop intervention techniques to modify reactions to faces.