Constant Curvature Segments as Building Blocks of 2D Shape Representation

How the visual system represents shape, and how shape representations might be computed by neural mechanisms, are fundamental and unanswered questions. Here, we investigated the hypothesis that 2-dimensional (2D) contour shapes are encoded structurally, as sets of connected constant curvature segments. We report 3 experiments investigating constant curvature segments as fundamental units of contour shape representations in human perception. Our results showed better performance in a path detection paradigm for constant curvature targets, as compared with locally matched targets that lacked this global regularity (Experiment 1), and that participants can learn to segment contours into constant curvature parts with different curvature values, but not into similarly different parts with linearly increasing curvatures (Experiment 2). We propose a neurally plausible model of contour shape representation based on constant curvature, built from oriented units known to exist in early cortical areas, and we confirmed the model's prediction that changes to the angular extent of a segment will be easier to detect than changes to relative curvature (Experiment 3). Together, these findings suggest the human visual system is specially adapted to detect and encode regions of constant curvature and support the notion that constant curvature segments are the building blocks from which abstract contour shape representations are composed.