James E. Cutting explores the perception of pictorial space, particularly the differential rotation effect observed by Goldstein (1987). This effect describes how objects in a picture appear to rotate as the viewer moves around the picture. Cutting proposes a model based on affine geometry, which transforms pictorial space through shears, compressions, and dilations according to the viewer's position relative to the composition point of the picture. The model explains Goldstein's findings well, showing that the perceived orientation of objects is influenced by the viewer's position and the properties of the picture. Cutting also discusses the dual nature of representational pictures, where both the objects portrayed and the picture itself are objects. He analyzes the impact of lens length on pictorial depth and demonstrates how affine distortions affect the perceived orientation of rods and eye glances in Goldstein's experiments. The model predicts the observed data with high correlation, suggesting that affine geometry can effectively explain the differential rotation effect in pictorial space.James E. Cutting explores the perception of pictorial space, particularly the differential rotation effect observed by Goldstein (1987). This effect describes how objects in a picture appear to rotate as the viewer moves around the picture. Cutting proposes a model based on affine geometry, which transforms pictorial space through shears, compressions, and dilations according to the viewer's position relative to the composition point of the picture. The model explains Goldstein's findings well, showing that the perceived orientation of objects is influenced by the viewer's position and the properties of the picture. Cutting also discusses the dual nature of representational pictures, where both the objects portrayed and the picture itself are objects. He analyzes the impact of lens length on pictorial depth and demonstrates how affine distortions affect the perceived orientation of rods and eye glances in Goldstein's experiments. The model predicts the observed data with high correlation, suggesting that affine geometry can effectively explain the differential rotation effect in pictorial space.