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.
A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data