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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683734
• 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.

面部识别在我们驾驭日常物理和社会环境的能力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工作的焦点。总的来说，这项研究对我们关于功能定位（即，哪些大脑区域承载面部表征）的知识做出了很大贡献。相比之下，人们对面部表征的实际性质知之甚少（即，支持它们的神经代码类型）。当前提案的首要目标是帮助阐明这些代码以及负责其生成的计算。*本次调查有三个突出问题。首先，通过人类腹侧皮层的激活模式揭示的神经面部表征的统计结构是什么？第二，什么编码机制负责将低级的面部刺激图像属性映射到高级的面部神经表征上？最后，我们如何将这种机制倒置，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，该研究结合了人类心理物理学、功能性磁共振成像（MRI）、计算建模以及用于分析经验数据的新型统计和机器学习技术。*总之，本文提出的研究旨在提供基于像素的刺激码和基于神经的表征码在面部感知领域之间关系的算法层面的解释。最终，这项工作旨在阐明识别人脸的意义，以及我们的大脑如何能够完成这一惊人的感知壮举。