We have used a combination of approaches from psychology, neuroscience, computer graphics and machine learning to investigate how we perceive and interpret 2D and 3D shape.
We have used computer simulations and neural networks to create databases consisting of hundreds of thousands of shapes being created and altered by interactions with other objects. For example, we have simulated liquids flowing, and soft objects being squashed, as well as ‘grown’ novel animal-like outlines, using a neural network that was trained on examples of thousands of natural animal shapes. This allows us to characterize shape --- and the way shape changes --- in the natural world.
We have then used these stimuli to perform visual perception experiments on volunteers, who judge the shape, or the objects properties (e.g. how soft the object is) based on viewing the shape or watching how the shape changes over time. By carefully selecting stimuli from our giant datasets, we can tease apart the predictions of rival theories about how the brain processes and interprets shape. We then build computational models of visual perception processes.
We have used this approach to study the perception of material properties, causal history, categorization and perceived shape similarity. This approach has allowed us to show, for example, that when we view liquids, we use a combination of particular shape and motion cues to work out how viscous the liquid. These cues work across a wide range of viewing conditions.