Figure 1. Computational goals of a visual categorization system.

a | The recognition system should generalize across a range of category exemplars — as well as across format and image transformations — while distinguishing between categories with similar features and configurations (for example, between faces of different species). b | To achieve efficient categorization, category information should be easy to read out. One way to achieve this efficiently is to have representations that are linearly separable. Assuming that an exemplar is represented by the distributed responses across a population of neurons, the computational constraint of separability entails that two exemplars of a category will evoke more similar distributed responses across the neural population than two exemplars of different categories (left graph). If this constraint is met, a simple linear classifier can be used to categorize stimuli (right graph). c | The recognition system should be able to extract several levels of information from a given input, as required by the task demands; in other words, it should enable flexible access to category information at several levels of abstraction. All photos in parts a and b courtesy of Getty/PhotoDisc. Barack Obama photo courtesy of Pictorial Press Ltd/Alamy. White House photo courtesy of Getty/PhotoDisc.