  Infants expect physical objects to be rigid and persist through space and
time and in spite of occlusion. Developmentists frequently attribute these
expectations to a "core system" for object recognition. However, it is unclear
if this move is necessary. If object representations emerge reliably from
general inductive learning mechanisms exposed to small amounts of environment
data, it could be that infants simply induce these assumptions very early.
Here, we demonstrate that a domain general learning system, previously used to
model concept learning and language learning, can also induce models of these
distinctive "core" properties of objects after exposure to a small number of
examples. Across eight micro-worlds inspired by experiments from the
developmental literature, our model generates concepts that capture core object
properties, including rigidity and object persistence. Our findings suggest
infant object perception may rely on a general cognitive process that creates
models to maximize the likelihood of observations
