Recognising novel deforming objects

Current theories of visual object recognition tend to focus on static properties, particularly shape. Nonetheless, visual perception is a dynamic experience–as a result of active observers or moving objects. Here, we investigate whether dynamic information can influence visual object-learning. Three learning experiments were conducted that required participants to learn and subsequently recognize different non-rigid objects that deformed over time. Consistent with previous studies of rigid depth-rotation, our results indicate that human observers do represent object-motion. Furthermore, our data suggest that dynamic information could compensate for when static cues are less reliable, for example, as a result of viewpoint variation