  It has been argued that humans rapidly adapt their lexical and syntactic
expectations to match the statistics of the current linguistic context. We
provide further support to this claim by showing that the addition of a simple
adaptation mechanism to a neural language model improves our predictions of
human reading times compared to a non-adaptive model. We analyze the
performance of the model on controlled materials from psycholinguistic
experiments and show that it adapts not only to lexical items but also to
abstract syntactic structures.
