  Determining the correct form of a verb in context requires an understanding
of the syntactic structure of the sentence. Recurrent neural networks have been
shown to perform this task with an error rate comparable to humans, despite the
fact that they are not designed with explicit syntactic representations. To
examine the extent to which the syntactic representations of these networks are
similar to those used by humans when processing sentences, we compare the
detailed pattern of errors that RNNs and humans make on this task. Despite
significant similarities (attraction errors, asymmetry between singular and
plural subjects), the error patterns differed in important ways. In particular,
in complex sentences with relative clauses error rates increased in RNNs but
decreased in humans. Furthermore, RNNs showed a cumulative effect of attractors
but humans did not. We conclude that at least in some respects the syntactic
representations acquired by RNNs are fundamentally different from those used by
humans.
