  Humans flexibly solve new problems that differ qualitatively from those they
were trained on. This ability to generalize is supported by learned concepts
that capture structure common across different problems. Here we develop a
naturalistic drawing task to study how humans rapidly acquire structured prior
knowledge. The task requires drawing visual objects that share underlying
structure, based on a set of composable geometric rules. We show that people
spontaneously learn abstract drawing procedures that support generalization,
and propose a model of how learners can discover these reusable drawing
programs. Trained in the same setting as humans, and constrained to produce
efficient motor actions, this model discovers new drawing routines that
transfer to test objects and resemble learned features of human sequences.
These results suggest that two principles guiding motor program induction in
the model - abstraction (general programs that ignore object-specific details)
and compositionality (recombining previously learned programs) - are key for
explaining how humans learn structured internal representations that guide
flexible reasoning and learning.
