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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660804
• 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.

人脸识别对于我们驾驭日常物理和社会环境的能力起着至关重要的作用。因此，它是认知神经科学重大研究工作的重点。总的来说，这项研究为我们关于功能定位（即哪些大脑区域承载面部表征）的知识做出了很大贡献。但相比之下，人们对面部表征的实际性质知之甚少（即什么类型的神经代码支持它们）。当前提案的总体目标是帮助阐明此代码以及负责其生成的计算。 * 三个悬而未决的问题指导了本次调查。首先，人类腹侧皮层激活模式揭示的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示上？最后，如何将这一机制转化为根据其引发的神经模式重建面部图像？为了解决这些问题，该研究结合了人类心理物理学、功能磁共振成像 (MRI) 和计算模型以及用于分析经验数据的新颖统计和机器学习技术。*总而言之，此处提出的研究旨在为面部感知领域中基于像素的刺激代码和基于神经的表征代码之间的关系提供算法级别的解释。最终，这项工作旨在阐明识别面孔的含义以及我们的大脑如何完成这一惊人的感知壮举。