Emergent selectivity for scenes, object properties, and contour statistics in feedforward models of scene-preferring cortex
Listed in
- @MariusPeelen's saved articles (MariusPeelen)
Abstract
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 feedforward computational models of visual feature coding 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 feedforward models of the PPA exhibit emergent selectivity across multiple levels of complexity, giving rise to seemingly high-level preferences for scenes and for objects that are large, spatially fixed, and inanimate/manmade while simultaneously yielding low-level preferences for rectilinear shapes and cardinal orientations. These results reconcile disparate theories of PPA function in a unified model of feedforward feature coding, and they demonstrate how multifaceted selectivity profiles naturally emerge from the feedforward computations of visual cortex and the natural statistics of images.
SIGNIFICANCE STATEMENT
Visual neuroscientists characterize cortical selectivity by identifying stimuli that drive regional responses. A perplexing finding is that many higher-order visual regions exhibit selectivity profiles 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 feedforward, hierarchical coding of visual features. Our work reconciles seemingly divergent findings of selectivity in scene-preferring cortex and suggests that surprisingly simple feedforward feature representations may be central to the category-selective organization of the human visual system.
