acl acl2013 acl2013-390 acl2013-390-reference knowledge-graph by maker-knowledge-mining
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Author: Stefan L. Frank ; Leun J. Otten ; Giulia Galli ; Gabriella Vigliocco
Abstract: We investigated the effect of word surprisal on the EEG signal during sentence reading. On each word of 205 experimental sentences, surprisal was estimated by three types of language model: Markov models, probabilistic phrasestructure 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 gener- ally applicable measure of processing difficulty during language comprehension.