Neural Mechanisms of Face Recognition
Neural Mechanisms of Face Recognition
批准号:
10018284
负责人:
Winrich Freiwald
金额:
$7.2万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-30 至 2020-09-29
关键词:
ArchitectureAreaBiological ModelsBlindnessBrainCellsCharacteristicsCodeCognitiveComplexComputer AnalysisDevelopmentDwarfismElectrophysiology (science)Experimental ModelsEyeFaceFace ProcessingFragile X SyndromeFunctional Magnetic Resonance ImagingFundingGoalsHumanImageImpairmentIntelligenceKnowledgeLaboratoriesLifeLightMacacaMental disordersMissionMotionNeurodevelopmental DisorderNeuronsOutcomeOutputPersonsPlant RootsPopulationPrevalenceProcessPropertyProsopagnosiaPublic HealthResearchRoleSeriesShapesSocial PerceptionSocial Well-BeingStandard ModelStimulusStructureSupport SystemSyndromeSystemTestingUnited States National Institutes of HealthVisionVisualWell in selfWilliams SyndromeWorkactive visionautism spectrum disorderdesigndisabilityinformation processinginnovationinsightneural circuitneuromechanismobject recognitionoperationorganizational structureperceptual organizationrelating to nervous systemresponseretinal imagingsocialtheoriesvisual informationvisual process
中文摘要
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
人脸识别机理及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。
英文摘要
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.
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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
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.
DOI:
10.1016/j.conb.2020.08.012
发表时间:
2020-12
期刊:
Current opinion in neurobiology
影响因子:
5.7
作者:
[Freiwald WA]
通讯作者:
Freiwald WA
DOI:
10.1016/j.cub.2016.10.015
发表时间:
2017-01-09
期刊:
Current biology : CB
影响因子:
--
作者:
[Leibo JZ, Liao Q, Anselmi F, Freiwald WA, Poggio T]
通讯作者:
Poggio T
DOI:
10.1371/journal.pbio.1002245
发表时间:
2015
期刊:
PLoS biology
影响因子:
9.8
作者:
[Schwiedrzik CM, Zarco W, Everling S, Freiwald WA]
通讯作者:
Freiwald WA
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