Using language models and latent semantic analysis to characterise the N400m neural response

The N400 is a human neuroelectric response to semantic incongruity in on-line sentence processing, and implausibility in context has been identified as one of the factors that influence the size of the N400. In this paper we investigate whether predictors derived from Latent Semantic Analysis, language models, and Roark’s parser are significant in modeling of the N400m (the neuromagnetic version of the N400). We also investigate significance of a novel pairwise-priming language model based on the IBM Model 1 translation model. Our experiments show that all the predictors are significant. Moreover, we show that predictors based on the 4-gram language model and the pairwise-priming language model are highly correlated with the manual annotation of contextual plausibility, suggesting that these predictors are capable of playing the same role as the manual annotations in prediction of the N400m response. We also show that the proposed predictors can be grouped into two clusters of significant predictors, suggesting that each cluster is capturing a different characteristic of the N400m response.