Paper: Word surprisal predicts N400 amplitude during reading

ACL ID P13-2152
Title Word surprisal predicts N400 amplitude during reading
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

We investigated the effect of word sur- prisal on the EEG signal during sen- tence reading. On each word of 205 ex- perimental sentences, surprisal was esti- mated by three types of language model: Markov models, probabilistic phrase- structure grammars, and recurrent neu- ral networks. Four event-related poten- tial components were extracted from the EEG of 24 readers of the same sentences. Surprisal estimates under each model type formed a significant predictor of the am- plitude of the N400 component only, with more surprising words resulting in more negative N400s. This effect was mostly due to content words. These findings provide support for surprisal as a gener- ally applicable measure of processing dif- ficulty during language comprehension.