  Both scientists and children make important structural discoveries, yet their
computational underpinnings are not well understood. Structure discovery has
previously been formalized as probabilistic inference about the right
structural form --- where form could be a tree, ring, chain, grid, etc. [Kemp &
Tenenbaum (2008). The discovery of structural form. PNAS, 105(3), 10687-10692].
While this approach can learn intuitive organizations, including a tree for
animals and a ring for the color circle, it assumes a strong inductive bias
that considers only these particular forms, and each form is explicitly
provided as initial knowledge. Here we introduce a new computational model of
how organizing structure can be discovered, utilizing a broad hypothesis space
with a preference for sparse connectivity. Given that the inductive bias is
more general, the model's initial knowledge shows little qualitative
resemblance to some of the discoveries it supports. As a consequence, the model
can also learn complex structures for domains that lack intuitive description,
as well as predict human property induction judgments without explicit
structural forms. By allowing form to emerge from sparsity, our approach
clarifies how both the richness and flexibility of human conceptual
organization can coexist.
