Neural Investigations of Face Perception and Attention Using Population Receptive Field Modeling

每一天，我们与他人的许多社交互动都依赖于我们从别人的脸识别他们的能力。然而，大约2%的普通人群在脸部感知方面存在缺陷。是什么让一些人擅长辨认人脸，而另一些人则不擅长呢？科学家们已经确定了与识别面孔有关的大脑区域。此外，行为研究表明，面部感知是“整体的”。也就是说，对整个面部的感知，一下子要好于对单个面部特征的感知。然而，目前还不清楚是什么神经计算支撑了这种对面孔的整体感知。由于整体人脸处理需要脸部各部分的空间整合，研究的目标是检验这种处理是否依赖于脸部选择脑中神经元的空间整合。此外，这项研究将确定神经元空间整合的个体差异是否会预测人脸识别能力的个体差异。这个项目预计将在几个方面对社会产生影响。了解人脸识别的神经机制可以改善测量，增强诊断，并最终改善人脸识别缺陷个体的幸福感。该项目还将通过公开提供测量和建模大脑反应的新方法和计算工具来开发研究基础设施。该项目旨在促进女性在STEM领域的参与和教育。该研究计划通过利用尖端神经成像和计算方法，包括群体接受野(PRF)建模和分层卷积神经网络，以及面部感知的行为测量，解决了知识中的重要差距。特别是，PRF方法提供了一个独特的机会，可以在计算框架内操作和测量空间加工和注意对脸部知觉的影响。因此，这项研究(I)将通过量化面孔加工和注意的空间方面来促进对面孔感知神经机制的理解，(Ii)将揭示不同面孔刺激在不同注意需求下的空间整合如何解释面孔感知能力的个体差异，以及(Iii)将产生最新的和神经准确的人类面孔感知的计算模型。

Every day, our many social interactions with others depend on our ability to recognize people from their faces. However, about 2% of the general population have deficits in face perception. What makes some people good at recognizing faces and others bad at it? Scientists have identified brain regions involved in perceiving faces. Additionally, behavioral research has shown that face perception is "holistic". That is, perception of the whole face, all at once, is better than that of individual facial features. However, it is unknown what neural computations underlie this holistic perception of faces. Because holistic face processing requires spatial integration across face parts, the goal of the research is to examine whether such processing relies on spatial integration by neurons in face-selective brain. Additionally, the research will determine if individual differences in neuronal spatial integration predict individual differences in face recognition ability. This project is expected to impact society in several ways. Understanding the neural mechanisms of face recognition could improve measurement, enhance diagnosis, and ultimately improve the wellbeing of individuals with deficits in face recognition. The project will also develop research infrastructure by making publicly available the new methods and computational tools for measurement and modeling of brain responses. The project intends to advance the participation and education of women in STEM fields.The research program addresses important gaps in knowledge by leveraging cutting-edge neuroimaging and computational approaches, including population receptive field (pRF) modeling and hierarchical convolution neural networks, together with behavioral measurements of face perception. In particular, the pRF approach offers a unique opportunity to operationalize and measure spatial processing and attentional effects on face perception in a computational framework. Thus, the research (i) will advance understanding of the neural mechanisms of face perception by quantifying spatial aspects of face processing and attention, (ii) will uncover how spatial integration of different face stimuli, under differing attentional demands explains individual differences in face perception abilities, and (iii) will generate an up-to-date and neurally accurate computational model of human face perception.