Word surprisal predicts N400 amplitude during reading

Stefan Frank, Leun Otten, Giulia Galli and Gabriella Vigliocco

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

We investigated the effect of word surprisal on the brain's EEG signal during sentence reading. On each word of 205 experimental sentences, surprisal was estimated by three types of language model: Markov models, probabilistic phrase-structure grammars, and recurrent neural networks. Four event-related potential 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 amplitude 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 generally applicable measure of cognitive processing difficulty during language comprehension.

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