A neurocomputational investigation of human face processing

人脸处理的神经计算研究

• 批准号: RGPIN-2014-04640
• 负责人: Nestor, Adrian
• 金额: $ 2.26万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566649
• 关键词: neurocomputational; investigation; human; face; processing

Face recognition plays a critical role in the ability to navigate our every-day physical and social environment. As such, it is the focus of significant research efforts in cognitive neuroscience. Overall, this research has contributed a lot to our knowledge regarding functional localization (i.e., what brain areas host face representations). In contrast though, much less is known about the actual nature of face representations (i.e., what type of neural code supports them). The overarching goal of the current proposal is to help elucidate this code along with the computations responsible for its generation. Three outstanding issues guide this investigation. First, what is the statistical structure of neural face representations as revealed by activation patterns in the human ventral cortex? Second, what encoding mechanism is responsible for mapping low-level image properties of face stimuli onto high-level neural face representations? And last, how can one invert this mechanism into reconstructing face images from the neural patterns that they elicit? To address these issues, the investigation combines human psychophysics, functional magnetic resonance imaging (MRI), and computational modeling along with novel statistical and machine learning techniques for the analysis of empirical data. In summary, the research proposed here aims to provide an algorithmic-level account of the relationship between pixel-based stimulus codes and neural-based representational codes in the domain of face perception. Ultimately, this work aims to shed light on what it means to recognize a face and on how our brains are able to accomplish this amazing perceptual feat.

人脸识别在我们日常的物理和社会环境中起着至关重要的作用。因此，它是认知神经科学中重要研究努力的焦点。总体而言，这项研究对我们关于功能定位(即，哪些大脑区域拥有面部表征)的知识做出了很大贡献。然而，相比之下，关于面部表征的实际性质(即，哪种类型的神经代码支持它们)的了解要少得多。当前提案的首要目标是帮助阐明该代码以及负责生成该代码的计算。三个悬而未决的问题指导着这次调查。首先，人类腹侧皮质的激活模式揭示了神经面部表征的统计结构是什么？其次，什么编码机制负责将面部刺激的低级图像属性映射到高级神经面部表征？最后，人们如何才能将这种机制转化为从它们引发的神经模式中重建人脸图像？为了解决这些问题，这项研究结合了人类心理物理学、功能磁共振成像(MRI)和计算建模，以及用于分析经验数据的新的统计和机器学习技术。综上所述，本文提出的研究旨在为人脸感知领域中基于像素的刺激编码和基于神经的表征编码之间的关系提供一个算法级别的解释。最终，这项工作旨在阐明识别一张脸意味着什么，以及我们的大脑如何能够完成这一惊人的感知壮举。