Pushing the Limits of Learning from Limited Data

Abstract

What is the mechanism behind people's remarkable ability to learn from very little data, and what are its limits? Preliminary evidence suggests people can infer categories from extremely sparse data, even when they have fewer labeled examples than categories. However, the mechanisms behind this learning process are unclear. In our experiment, people learned 8 categories defined over a 2D manifold from just 4 labeled examples. Our results suggest that people are forming rich representations of the underlying categories despite this limited information. These results push the limits of how little information people need to build strong and systematic category representations.