Predicting the N400 Component in Manipulated and Unchanged Texts with a Semantic Probability Model

Within the field of computational linguistics, recent research has made successful advances in integrating word space models with n-gram models. This is of particular interest when a model that encapsulates both semantic and syntactic information is desirable. A potential application for this can be found in the field of psycholinguistics, where the neural response N400 has been found to occur in contexts with semantic incongruities. Previous research has found correlations between cloze probabilities and N400, while more recent research has found correlations between cloze probabilities and language models. This essay attempts to uncover whether or not a more direct connection between integrated models and N400 can be found, hypothesizing that low probabilities elicit strong N400 responses and vice versa. In an EEG experiment, participants read a text manipulated using a language model, and a text left unchanged. Analysis of the results shows that the manipulations to some extent yielded results supporting the hypothesis. Further results are found when analysing responses to the unchanged text. However, no significant correlations between N400 and the computational model are found. Future research should improve the experimental paradigm, so that a larger scale EEG recording can be used to construct a large EEG corpus.