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

Mehdi Parviz, Mark Johnson, Blake Johnson, Jon Brock

Research output: Contribution to journalConference paperpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)38-46
Number of pages9
JournalProceedings of the Australasian Language Technology Association Workshop 2011
Publication statusPublished - 2011
EventAustralasian Language Technology Workshop (9th : 2011) - Canberra
Duration: 1 Dec 20112 Dec 2011

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