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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=631152
• 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工作的焦点。总的来说，这项研究对我们关于功能定位的知识做出了很大贡献（即，哪些大脑区域拥有面部表征）。相比之下，人们对人脸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）。当前提案的首要目标是帮助阐明这段代码，沿着负责生成它的计算。三个突出的问题指导这项调查。首先，人类腹侧皮层的激活模式所揭示的神经面孔表征的统计结构是什么？第二，是什么样的编码机制负责映射低层次的图像属性的面部刺激到高层次的神经面部表征？最后，如何将这一机制转化为从它们引出的神经模式中重建面部图像？为了解决这些问题，调查结合了人类心理物理学，功能性磁共振成像（MRI）和计算建模沿着与新的统计和机器学习技术的经验数据的分析。总之，这里提出的研究的目的是提供一个算法级帐户之间的关系，基于像素的刺激代码和基于神经的表征代码在域的人脸感知。最终，这项工作旨在阐明识别人脸意味着什么，以及我们的大脑如何能够完成这一惊人的感知壮举。