  More predictable words are easier to process - they are read faster and
elicit smaller neural signals associated with processing difficulty, most
notably, the N400 component of the event-related brain potential. Thus, it has
been argued that prediction of upcoming words is a key component of language
comprehension, and that studying the amplitude of the N400 is a valuable way to
investigate the predictions we make. In this study, we investigate whether the
linguistic predictions of computational language models or humans better
reflect the way in which natural language stimuli modulate the amplitude of the
N400. One important difference in the linguistic predictions of humans versus
computational language models is that while language models base their
predictions exclusively on the preceding linguistic context, humans may rely on
other factors. We find that the predictions of three top-of-the-line
contemporary language models - GPT-3, RoBERTa, and ALBERT - match the N400 more
closely than human predictions. This suggests that the predictive processes
underlying the N400 may be more sensitive to the surface-level statistics of
language than previously thought.
