  Humans are highly efficient learners, with the ability to grasp the meaning
of a new concept from just a few examples. Unlike popular computer vision
systems, humans can flexibly leverage the compositional structure of the visual
world, understanding new concepts as combinations of existing concepts. In the
current paper, we study how people learn different types of visual
compositions, using abstract visual forms with rich relational structure. We
find that people can make meaningful compositional generalizations from just a
few examples in a variety of scenarios, and we develop a Bayesian program
induction model that provides a close fit to the behavioral data. Unlike past
work examining special cases of compositionality, our work shows how a single
computational approach can account for many distinct types of compositional
generalization.
