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

点击翻译按钮获取中文摘要

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.