Neurobehavioral Correlates of Surprisal in Language Comprehension: A Neurocomputational Model

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.

Automated summary

Research has found that expectation-based theories of language comprehension, particularly Surprisal Theory, are effective in explaining the behavioral correlates of word-by-word processing difficulty. However, there is still uncertainty about which component(s) of the Event-Related brain Potential (ERP) signal reflects Surprisal and how these electrophysiological correlates are related to behavioral processing indices. By establishing a neurocomputational model and experimental design, a close link between Surprisal and the P600 component has been identified, providing an integrated explanation for processing difficulty in language comprehension.