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Tuning in scene-preferring cortex for mid-level visual features gives rise to selectivity across multiple levels of stimulus complexity

The scene-preferring portion of the human ventral visual stream, known as the parahippocampal place area (PPA), responds to scenes and landmark objects, which tend to be large in real-world size, fixed in location, and inanimate. However, the PPA also exhibits preferences for low-level contour statistics, including rectilinearity and cardinal orientations, that are not directly predicted by theories of scene- and landmark-selectivity. It is unknown whether these divergent findings of both low- and high-level selectivity in the PPA can be explained by a unified computational theory. To address this issue, we fit hierarchical computational models of mid-level tuning to the image-evoked fMRI responses of the PPA, and we performed a series of high-throughput experiments on these models. Our findings show that hierarchical encoding models of the PPA exhibit emergent selectivity across multiple levels of complexity, giving rise to high-level preferences along dimensions of real-world size, fixedness, and naturalness/animacy as well as low-level preferences for rectilinear shapes and cardinal orientations. These results reconcile disparate theories of PPA function in a unified model of mid-level visual representation, and they demonstrate how multifaceted selectivity profiles naturally emerge from the hierarchical computations of visual cortex and the natural statistics of images.Visual neuroscientists characterize cortical selectivity by identifying stimuli that drive regional responses. A perplexing finding is that many higher-order visual regions exhibit selectivity spanning multiple levels of complexity: they respond to highly complex categories, such as scenes and landmarks, but also to surprisingly simplistic features, such as specific contour orientations. Using large-scale computational analyses and human brain imaging, we show how multifaceted selectivity in scene-preferring cortex can emerge from the coding of mid-level visual features, whose complexity is neither as simple as local contours nor as complex as scenes or objects. Our work reconciles seemingly divergent findings of selectivity in scene-preferring cortex and suggests that mid-level features may be central to understanding the category-selective organization of the human visual system.