  Our understanding of the visual world goes beyond naming objects,
encompassing our ability to parse objects into meaningful parts, attributes,
and relations. In this work, we leverage natural language descriptions for a
diverse set of 2K procedurally generated objects to identify the parts people
use and the principles leading these parts to be favored over others. We
formalize our problem as search over a space of program libraries that contain
different part concepts, using tools from machine translation to evaluate how
well programs expressed in each library align to human language. By combining
naturalistic language at scale with structured program representations, we
discover a fundamental information-theoretic tradeoff governing the part
concepts people name: people favor a lexicon that allows concise descriptions
of each object, while also minimizing the size of the lexicon itself.
