Proceedings of the Annual Meeting of the Cognitive Science Society

How do people balance the reuse of learned routines with the need to invent new solutions under cognitive limitations? Recent computational frameworks have begun to develop resource-rational approaches to program induction, with a common theme being the benefits of building a "library" of past solutions for creative reuse. Here, we study these mechanisms in an online experiment where participants learned real-world musical melodies. Our results reveal systematic error patterns during reconstruction and improvisation tasks, with participants repeating local patterns and displaying a behavioral bias consistent with simpler programs. To explain these findings, we developed a non-parametric Bayesian model using a hierarchical Pitman–Yor process to learn both a global library encoding domain-general primitives, and a local library capturing melody-specific motifs—both helping to constrain the hypothesis space. Our model makes testable predictions about human error distributions and adaptive behaviors that balance the trade-off between efficiency and creativity when resources are scarce.