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The method of population receptive fields (pRFs) allows quantitative modeling of brain activity responses to external stimuli, and has been used extensively to investigate cortical organization in health and disease (Dumoulin & Wandell, 2008, Dumoulin & Knapen, 2018). Since its introduction, the method has been extended to capture suppressive surrounds and compressive spatial summation (Zuiderbaan et al, 2012; Kay et al, 2013). Interestingly, suppressive surrounds explain more signal variance in early, but not late visual cortex; whereas the converse is true for compressive spatial summation. Divisive normalization has been proposed to underlie many psychophysical and neurological phenomena, and is a prime candidate for a canonical neural computation (Carandini & Heeger, 2012). Here, we build a pRF model based on divisive normalization and ask whether it can 1) unify previous pRF models and 2 …