权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Neural Mechanisms of Face Recognition

人脸识别的神经机制

基本信息

批准号：
10018284
负责人：
Winrich Freiwald
金额：
$ 7.2万
依托单位：
ROCKEFELLER UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-30 至 2020-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10018284
关键词：
Architecture Area Biological Models Blindness Brain Cells Characteristics Code Cognitive Complex Computer Analysis Development Dwarfism Electrophysiology (science)Experimental Models Eye Face Face Processing Fragile X Syndrome Functional Magnetic Resonance Imaging Funding Goals Human Image Impairment Intelligence Knowledge Laboratories Life Light Macaca Mental disorders Mission Motion Neurodevelopmental Disorder Neurons Outcome Output Persons Plant Roots Population Prevalence Process Property Prosopagnosia Public Health Research Role Series Shapes Social Perception Social Well-Being Standard Model Stimulus Structure Support System Syndrome System Testing United States National Institutes of Health Vision Visual Well in self Williams Syndrome Work active vision autism spectrum disorder design disability information processing innovation insight neural circuit neuromechanism object recognition operation organizational structure perceptual organization relating to nervous system response retinal imaging social theories visual information visual process

项目摘要

PROJECT SUMMARY There is a fundamental gap in our understanding of the neural mechanisms supporting the recognition of complexly structured information like faces. The existence of this conceptual gap constitutes an important problem because, until it is filled, we will not be able to explain face recognition and the reasons for its impairment in neurodevelopmental disorders like autism or face blindness. The long-term goal is to understand the neural mechanisms of face recognition and build an artificial face-recognition system implementing neural computations and thus explain face recognition mechanistically. The overall objective of this proposal is the identification of the neural mechanisms and computations by which face-processing systems support active face recognition. The experimental model system consists of interconnected brain areas, each with a unique functional specialization, currently thought to support face identification in a feed-forward manner. The central hypothesis is that the face-processing system is instead designed to make information at multiple levels of perceptual organization explicit from facial feature, to face, to person, to the layout and meaning of an entire scene. The hypothesis is rooted in a series of experimental observations in the principal applicant’s laboratory. The rationale for this proposal is that completion of the proposed research will provide an understanding, in a model system, of how neural populations generate compositional, structured representations, imposing critical constraints on theories of cortex, intelligence, and active vision. The hypothesis will be tested by pursuing three specific aims: 1) Determine Rules of Part-Whole Interaction for Face Recognition, 2) Determine Mechanisms of Context- Modulation in Face Areas, and 3) Determine Population Codes for Complex Visual Scene Processing. Functional magnetic resonance imaging to localize face and body areas will be combined with electrophysiological recordings targeted to these regions and analyses of computational model systems to determine how facial feature integration into the holistic structure of the face shapes face identification, to determine the mechanisms by which object and body context impact face representations, and to elucidate the extent and form of social information codes in these areas supporting active high-level scene processing. The approach is innovative, because it fundamentally challenges the standard model of the circuits of face-processing, aiming to reveal the mechanisms supporting information processing at multiple levels of perceptual organization with a focus on extracting general principles of cortical circuit operations for high-level vision. The proposed research is significant, because it will provide a mechanistic understanding of the operations of complex neural circuits and how they implement computations that extract and package socially highly important information. Because the outcome is an advance in understanding circuit mechanisms of social perception, it will identify vulnerabilities of the face-processing system directly relevant to the understanding of face blindness, prosopagnosia, and of altered social perception in syndromes spanning autism spectrum disorders, fragile X, and Williams syndrome.

项目总结在我们对支持认知的神经机制的理解上存在着根本的差距像人脸这样结构复杂的信息。这一概念差距的存在构成了一个重要的这是一个问题，因为，在它被填满之前，我们将无法解释人脸识别和它受损的原因神经发育障碍，如自闭症或面部失明。长期的目标是理解神经人脸识别的机理和实现神经计算的人工人脸识别系统从而从机械上解释人脸识别。这项提案的总体目标是确定人脸处理系统支持主动人脸识别的神经机制和计算。实验模型系统由相互连接的大脑区域组成，每个区域都有独特的功能专业化，目前被认为支持前馈方式的人脸识别。中心假说取而代之的是，面部处理系统被设计成在多个知觉水平上产生信息组织清晰，从面部特征，到人脸，到人物，到整个场景的布局和意义。这个这一假设植根于主申请人实验室中的一系列实验观察。其基本原理是因为这项提议的目的是，完成拟议的研究将在一个模型系统中提供一种理解，神经种群如何生成组成的、结构化的表示，对大脑皮层、智力和主动视觉理论。这一假设将通过追求三个具体目标来检验： 1)确定用于人脸识别的部分-整体交互规则；2)确定上下文机制-- 人脸区域的调制，以及3)确定用于复杂视觉场景处理的总体编码。功能性定位面部和身体区域的磁共振成像将与电生理相结合针对这些区域的录音和计算模型系统的分析，以确定面部将特征融合到人脸整体结构中，对人脸形状进行识别，确定识别机制通过物体和身体情境对面孔表征的影响，并阐明社交的程度和形式这些区域中的信息代码支持主动高级场景处理。这种方法是创新的，因为它从根本上挑战了人脸处理电路的标准模型，旨在揭示支持多层次知觉组织的信息处理的机制，重点是提取高级视觉大脑皮层回路操作的一般原理。拟议的研究是意义重大，因为它将提供对复杂神经电路和他们如何实现提取和打包高度社会重要信息的计算。因为结果是在理解社会感知的回路机制方面取得了进展，它将识别人脸处理系统直接关系到对人脸失明、面容失认症和面孔失认症的理解改变了自闭症谱系障碍、脆性X综合征和威廉姆斯综合征等症状的社会认知。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Efficient inverse graphics in biological face processing.

生物人脸处理中的高效逆向图形。

DOI：
10.1126/sciadv.aax5979
发表时间：
2020
期刊：
Science advances
影响因子：
13.6
作者：
Yildirim,Ilker;Belledonne,Mario;Freiwald,Winrich;Tenenbaum,Josh
通讯作者：
Tenenbaum,Josh

Encoding of dynamic facial information in the middle dorsal face area.

DOI：
10.1073/pnas.2212735120
发表时间：
2023-02-21
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子：
11.1
作者：
Yang, Zetian;Freiwald, Winrich A.
通讯作者：
Freiwald, Winrich A.

Social interaction networks in the primate brain.