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Spatial processing by receptive fields is a core property of the visual system. However, it is unknown how spatial processing in high-level regions contributes to recognition behavior. As face inversion is thought to disrupt typical holistic processing of information in faces, we mapped population receptive fields (pRFs) with upright and inverted faces in the human visual system. Here we show that in face-selective regions, but not primary visual cortex, pRFs and overall visual field coverage are smaller and shifted downward in response to face inversion. From these measurements, we successfully predict the relative behavioral detriment of face inversion at different positions in the visual field. This correspondence between neural measurements and behavior demonstrates how spatial processing in face-selective regions may enable holistic perception. These results not only show that spatial processing in high-level visual regions is dynamically used towards recognition, but also suggest a powerful approach for bridging neural computations by receptive fields to behavior. 

While spatial information and biases have been consistently reported in high-level face regions, the functional contribution of this information toward face recognition behavior is unclear. Here, we propose that spatial integration of information plays a critical role in a hallmark phenomenon of face perception: holistic processing, or the tendency to process all features of a face concurrently rather than independently. We sought to gain insight into the neural basis of face recognition behavior by using a voxelwise encoding model of spatial selectivity to characterize the human face network using both typical face stimuli, and stimuli thought to disrupt normal face perception. We mapped population receptive fields (pRFs) using 3T fMRI in 6 participants using upright as well as inverted faces, which are thought to disrupt holistic processing. Compared to upright faces, inverted faces yielded substantial differences in measured pRF size, position, and amplitude. Further, these differences increased in magnitude along the face network hierarchy, from IOG- to pFus- and mFus-faces. These data suggest that pRFs in high-level regions reflect complex stimulus- dependent neural computations that underlie variations in recognition performance.