Functional Neuroimaging of Face and Object Representations in the Ventral Visual Pathway

腹侧视觉通路中面部和物体表征的功能神经成像

• 批准号: 0352775
• 负责人: James Haxby
• 金额: $ 12.5万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0352775&HistoricalAwards=false
• 关键词: Functional; Neuroimaging; Face; Object; Representations

Visual recognition of faces and other object categories evokes distinct, category-related patterns of response in the extrastriate cortices of the human ventral visual pathway. With funding from NSF, Dr. James V. Haxby is investigating the representations of within-category distinctions among faces in the ventral visual pathway of the human brain. Previously, he and his colleagues showed that the patterns of submaximal neural responses also carry category-related information, indicating that the representations of different object categories are overlapping, and that weak and strong neural responses are both integral to population responses that represent object appearance. However, previous studies only addressed the distinctions between representations for different object categories and only analyzed the consistency of patterns of response within but not across individual subjects. The objective of the funded research is to investigate the representation of distinctions between exemplars within a single category, namely human faces. Experiments are testing whether representations of within-category distinctions among faces have a more focal anatomical distribution in the ventral pathway, that is, whether within-category differences are represented in regions that respond maximally to faces or are more broadly distributed. Studies are (1) examining the distribution of neural responses to within-category stimulus changes, and (2) analyzing the patterns of response evoked by stimuli. Face appearance is manipulated along two computationally-defined dimensions, one that reflects continuous variation in gender-related appearance and a second that reflects continuous variation from lean to wide, to investigate the correlation between patterns of neural response and computationally-defined alterations of face appearance. With other investigators, in addition to using methods of topographic pattern analysis, more sophisticated methods (e.g. PCA, ICA) are being developed for dissecting patterns of response that can identify the parts or subspaces that carry different aspects of information. These studies represent a novel approach to elucidating the detailed structure of the representation of faces in the ventral visual pathway. The long-term goal is to discover the principles of organization that underlie the topography of face representations and, thereby, reveal the correspondence rules that relate patterns of neural response to computational descriptions of information about face appearance. These principles of organization for topographically-organized population responses also may be relevant for the study of other types of information, such as visual motion, audition, and language. Development of new methods for analyzing patterns of response in neuroimaging data will benefit the entire field of functional neuroimaging and will facilitate the development of a new perspective on the neural representation of abstract information. Using computational models to guide the research on neural representation of faces will advance understanding of how faces are recognized, how factors such as social stereotypes and race affect face recognition, and the role face perception plays in social communication.Broader impacts. This research involves the efforts of a postdoctoral fellow and graduate students. It is helping to build the research and education program in cognitive neuroscience at Princeton University. The research is being used in classes at Princeton and will serve as the basis for guiding student research projects and for helping researchers from other fields, such as social psychology, apply functional neuroimaging to related questions. Students from under-represented groups are encouraged to become involved with the research team and to complete research projects. Collaborations with computational modelers and applied mathematicians facilitate the learning of new computational methods by psychologists and serve to ignite interest in cognitive neuroscience in other disciplines.

面孔和其他物体类别的视觉识别在人类腹侧视觉通路的纹外皮层中引起不同的、与类别相关的反应模式。 在NSF的资助下，James V. Haxby博士正在研究人类大脑腹侧视觉通路中面孔之间类别内差异的表征。此前，他和他的同事们表明，次最大神经反应的模式也携带类别相关的信息，表明不同对象类别的表示是重叠的，弱和强神经反应都是代表对象外观的群体反应的组成部分。 然而，以前的研究只解决了不同的对象类别的表征之间的区别，只分析了反应模式的一致性，但没有跨个别科目。 资助研究的目的是调查在一个单一的类别，即人脸的样本之间的区别的代表性。实验正在测试是否表示类别内的区别面孔之间有一个更集中的解剖分布在腹侧通路，也就是说，是否类别内的差异表示在区域，最大限度地回应面孔或更广泛的分布。 研究内容包括：（1）检查类别内刺激变化的神经反应分布;（2）分析刺激诱发的反应模式。 面部外观被操纵沿着两个计算定义的维度，一个反映了性别相关外观的连续变化，第二个反映了从瘦到宽的连续变化，以研究神经响应模式和计算定义的面部外观变化之间的相关性。 与其他调查人员，除了使用地形模式分析的方法，更复杂的方法（例如PCA，伊卡）正在开发的解剖模式的反应，可以识别的部分或子空间，携带不同方面的信息。这些研究代表了一种新的方法来阐明的详细结构的代表面孔的腹侧视觉通路。 长期目标是发现面部表征地形的组织原则，从而揭示将神经反应模式与面部外观信息的计算描述联系起来的对应规则。 这些原则的组织地形组织人口的反应也可能是相关的其他类型的信息，如视觉运动，听觉和语言的研究。 神经影像学数据中反应模式分析的新方法的发展将有利于整个功能神经影像学领域，并将促进抽象信息的神经表征的新视角的发展。 使用计算模型来指导面部神经表征的研究将促进对面部识别的理解，社会刻板印象和种族等因素如何影响面部识别，以及面部感知在社会交往中的作用。 这项研究涉及一名博士后研究员和研究生的努力。 它正在帮助普林斯顿大学建立认知神经科学的研究和教育计划。 这项研究正在普林斯顿大学的课堂上使用，并将作为指导学生研究项目的基础，并帮助其他领域的研究人员，如社会心理学，将功能性神经成像应用于相关问题。 鼓励来自代表性不足群体的学生参与研究团队并完成研究项目。 与计算建模者和应用数学家的合作促进了心理学家对新计算方法的学习，并激发了其他学科对认知神经科学的兴趣。