Expectation-based theories of language comprehension, in particular Surprisal Theory,
go a long way in accounting for the behavioral correlates of word-by-word processing
difficulty, such as reading times. An open question, however, is in which component(s)
of the Event-Related brain Potential (ERP) signal Surprisal is reflected, and how these
electrophysiological correlates relate to behavioral processing indices. Here, we address
this question by instantiating an explicit neurocomputational model of incremental,
word-by-word language comprehension that produces estimates of the N400 and
the P600—the two most salient ERP components for language processing—as well
as estimates of “comprehension-centric” Surprisal for each word in a sentence. We
derive model predictions for a recent experimental design that directly investigates
“world-knowledge”-induced Surprisal. By relating these predictions to both empirical
electrophysiological and behavioral results, we establish a close link between Surprisal,
as indexed by reading times, and the P600 component of the ERP signal. The resultant
model thus offers an integrated neurobehavioral account of processing difficulty in
language comprehension