  Recurrent neural network grammars (RNNGs) are generative models of
(tree,string) pairs that rely on neural networks to evaluate derivational
choices. Parsing with them using beam search yields a variety of incremental
complexity metrics such as word surprisal and parser action count. When used as
regressors against human electrophysiological responses to naturalistic text,
they derive two amplitude effects: an early peak and a P600-like later peak. By
contrast, a non-syntactic neural language model yields no reliable effects.
Model comparisons attribute the early peak to syntactic composition within the
RNNG. This pattern of results recommends the RNNG+beam search combination as a
mechanistic model of the syntactic processing that occurs during normal human
language comprehension.
