Contour junctions underlie neural representations of scene categories in high-level human visual cortex

Humans efficiently grasp complex visual environments, making highly consistent judgments of entry-level category despite their high variability in visual appearance. How does the human brain arrive at the invariant neural representations underlying categorization of real-world environments? We here show that the neural representation of visual environments in scenes-selective human visual cortex relies on statistics of contour junctions, which provide cues for the three-dimensional arrangement of surfaces in a scene. We manipulated line drawings of real-world environments such that statistics of contour orientations or junctions were disrupted. Manipulated and intact line drawings were presented to participants in an fMRI experiment. Scene categories were decoded from neural activity patterns in the parahippocampal place area (PPA), the occipital place area (OPA) and other visual brain regions. Disruption of junctions but not orientations led to a drastic decrease in decoding accuracy in the PPA and OPA, indicating the reliance of these areas on intact junction statistics. Accuracy of decoding from early visual cortex, on the other hand, was unaffected by either image manipulation. We further show that the correlation of error patterns between decoding from the scene-selective brain areas and behavioral experiments is contingent on intact contour junctions. Finally, a searchlight analysis exposes the reliance of visually active brain regions on different sets of contour properties. Statistics of contour length and curvature dominate neural representations of scene categories in early visual areas and contour junctions in high-level scene-selective brain regions.