  Core knowledge about physical objects -- e.g., their permanency, spatial
transformations, and interactions -- is one of the most fundamental building
blocks of biological intelligence across humans and non-human animals. While AI
techniques in certain domains (e.g. vision, NLP) have advanced dramatically in
recent years, no current AI systems can yet match human abilities in flexibly
applying core knowledge to solve novel tasks. We propose a new AI approach to
core knowledge that combines 1) visual representations of core knowledge
inspired by human mental imagery abilities, especially as observed in studies
of neurodivergent individuals; with 2) tree-search-based program synthesis for
flexibly combining core knowledge to form new reasoning strategies on the fly.
We demonstrate our system's performance on the very difficult Abstraction \&
Reasoning Corpus (ARC) challenge, and we share experimental results from
publicly available ARC items as well as from our 4th-place finish on the
private test set during the 2022 global ARCathon challenge.
