  Many complex problems are naturally understood in terms of symbolic concepts.
For example, our concept of "cat" is related to our concepts of "ears" and
"whiskers" in a non-arbitrary way. Fodor (1998) proposes one theory of
concepts, which emphasizes symbolic representations related via constituency
structures. Whether neural networks are consistent with such a theory is open
for debate. We propose unit tests for evaluating whether a system's behavior is
consistent with several key aspects of Fodor's criteria. Using a simple visual
concept learning task, we evaluate several modern neural architectures against
this specification. We find that models succeed on tests of groundedness,
modularlity, and reusability of concepts, but that important questions about
causality remain open. Resolving these will require new methods for analyzing
models' internal states.
