Visual complexity of shapes: a hierarchical perceptual learning model

Understanding how people perceive the visual complexity of shapes has important theoretical as well as practical implications. One school of thought, driven by information theory, focuses on studying the local features that contribute to the perception of visual complexity. Another school, in contrast, emphasizes the impact of global characteristics of shapes on perceived complexity. Inspired by recent discoveries in neuroscience, our model considers both local features of shapes: edge lengths and vertex angles, and global features: concaveness, and is in 92% agreement with human subjective ratings of shape complexity. The model is also consistent with the hierarchical perceptual learning theory, which explains how different layers of neurons in the visual system act together to yield a perception of visual shape complexity.