We recognize objects easily every day, but object recognition is in fact a very difficult problem. Even leading computer algorithms do not match human performance today. Object recognition is not easy for the brain either: a series of cortical areas, taking up ~40% of the brain, is dedicated to vision. But we know very little about the rules by which the brain transforms what we see into what we perceive. What is the nature of this representation? What are the underlying rules?
Our approach to this problem is best understood through an analogy to colour. We see millions of colors but it is well known that color perception is three-dimensional - any color we perceive can be represented using three numbers. Can we do likewise for the millions of shapes we see? Do shapes also reside in a low-dimensional space? To gain insight into these questions, we perform behavioral experiments in humans and record the electrical activity of neurons from the monkey visual cortex. In the human experiments, we probe the perceptual representation using behavioral tasks such as visual search or categorization. In the monkey experiments, we probe the representation at the level of single neurons in the inferotemporal cortex, an area critical for object recognition. We use both the behavioral and neuronal data to test and validate computational models of object recognition.