Dissociating the Neural Representation of Scene Boundary and Extended Surfaces in Human Visual Scene Processing

Humans often rely on environmental boundaries for place recognition and navigation. However, what defines an effective boundary for human cortical scene processing remains unclear. Despite the prominent use of extended surfaces (e.g., walls) as environmental boundaries in the literature, some evidence suggests that effective boundaries may instead be qualified by their ability to mark the 3D shape of a local environment. In this study, we directly test this possibility using tightly controlled, artificial images of boundaries that define the same 3D shape of a local environment, systematically manipulating the number of poles to vary the internal structure of these boundaries. We hypothesize that if the human cortical scene-processing system encodes boundaries based on the geometric shape marked by boundary elements rather than surface continuity, it will represent geometrically equivalent boundaries similarly, regardless of whether a boundary is made up of wall surfaces or a varying number of poles. Using fMRI, we found that even a few isolated poles marking the vertices of a local 3D space were sufficient to elicit a wall-like representation in the parahippocampal place area, revealing its sensitivity to environmental shape composed of non-wall boundaries. The occipital place area was sensitive to graded variations in boundary structure, tracking continuous surface-like properties. Together, these results reveal neural sensitivity to non-wall boundaries in the human scene-selective cortical system and shed light on the distinct boundary features that support the encoding of environmental geometry across different cortical regions.