  As modern deep networks become more complex, and get closer to human-like
capabilities in certain domains, the question arises of how the representations
and decision rules they learn compare to the ones in humans. In this work, we
study representations of sentences in one such artificial system for natural
language processing. We first present a diagnostic test dataset to examine the
degree of abstract composable structure represented. Analyzing performance on
these diagnostic tests indicates a lack of systematicity in the representations
and decision rules, and reveals a set of heuristic strategies. We then
investigate the effect of the training distribution on learning these heuristic
strategies, and study changes in these representations with various
augmentations to the training set. Our results reveal parallels to the
analogous representations in people. We find that these systems can learn
abstract rules and generalize them to new contexts under certain circumstances
-- similar to human zero-shot reasoning. However, we also note some
shortcomings in this generalization behavior -- similar to human judgment
errors like belief bias. Studying these parallels suggests new ways to
understand psychological phenomena in humans as well as informs best strategies
for building artificial intelligence with human-like language understanding.
