Abstract
Reading times on words in a sentence depend on the amount of information the words convey, which can be estimated by probabilistic language models. We investigate whether event-related potentials (ERPs), too, are predicted by information measures. Three types of language models estimated four different information measures on each word of a sample of English sentences. Six different ERP deflections were extracted from the EEG signal of participants reading the same sentences. A comparison between the information measures and ERPs revealed a reliable correlation between N400 amplitude and word surprisal. Language models that make no use of syntactic structure fitted the data better than did a phrase-structure grammar, which did not account for unique variance in N400 amplitude. These findings suggest that different information measures quantify cognitively different processes and that readers do not make use of a sentence’s hierarchical structure for generating expectations about the upcoming word.
Highlights
Frank s.frank@ucl.ac.uk Department of Experimental Psychology, University College London and Centre for Language Studies, Radboud University Nijmegen
There are 10,000 word types in the selected BNC training corpus, so a model with no knowledge of the language would have a linguistic accuracy of log(1/10, 000) = −9.21
Dotted lines indicate χ2 = ±3.84, the critical value at the α = .05-level, which must not be taken as an indication of statistical significance because of the exploratory nature of these results
Summary
Frank s.frank@ucl.ac.uk Department of Experimental Psychology, University College London and Centre for Language Studies, Radboud University Nijmegen Otten Institute of Cognitive Neuroscience, University College London
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