  A hallmark of human intelligence is the ability to adapt to new situations,
by applying learned rules to new content (systematicity) and thereby enabling
an open-ended number of inferences and actions (generativity). Here, we propose
that the human brain accomplishes these feats through pathways in the parietal
cortex that encode the abstract structure of space, events, and tasks, and
pathways in the temporal cortex that encode information about specific people,
places, and things (content). Recent neural network models show how the
separation of structure and content might emerge through a combination of
architectural biases and learning, and these networks show dramatic
improvements in the ability to capture systematic, generative behavior. We
close by considering how the hippocampal formation may form integrative
memories that enable rapid learning of new structure and content
representations.
