Human visual object categorization can be described by models with low memory capacity.
Studies of high-level models of visual object categorization have left unresolved issues of neurobiological relevance, including how features are extracted from the image and the role played by memory capacity in categorization performance. We compared the ability of a comprehensive set of models to match the categorization performance of human observers while explicitly accounting for the models' numbers of free parameters. The most successful models did not require a large memory capacity, suggesting that a sparse, abstracted representation of category properties may underlie categorization performance. This type of representation--different from classical prototype abstraction--could also be extracted directly from two-dimensional images via a biologically plausible early-vision model, rather than relying on experimenter-imposed features.