Year: 2013
Type: conference-paper
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 generally applicable measure of processing difficulty during language comprehension.
Institutions: University College London, University of Siena
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W2166362343