Local cues enable classification of image patches as surfaces, object boundaries, or illumination changes.

To correctly parse the visual scene, one must detect edges and determine their underlying cause. Previous work has demonstrated that neural networks trained to differentiate shadow and occlusion edges exhibit sensitivity to boundary sharpness and texture differences. Here, we investigate whether human observers are also sensitive to these cues using synthetic edge stimuli formed by quilting together two natural textures, allowing us to parametrically manipulate boundary sharpness, texture modulation, and luminance modulation. Observers classified five sets of synthetic boundary images as shadows, occlusions, or textures generated by varying these three cues in all possible combinations. These three cues exhibited strong interactions to determine categorization. For sharp edges, increasing luminance modulation made it less likely the patch would be classified as a texture and more likely it would be classified as an occlusion, whereas for blurred edges, increasing luminance modulation made it more likely the patch would be classified as a shadow. Boundary sharpness had a profound effect, so that in the presence of luminance modulation, increasing sharpness decreased the likelihood of classification as a shadow and increased the likelihood of classification as an occlusion. Texture modulation had little effect, except for a sharp boundary with zero luminance modulation. Results were consistent across all five stimulus sets, and human performance was well explained by a multinomial logistic regression model. Our results demonstrate that human observers make use of the same cues as previous machine learning models when detecting and determining the cause of an edge.