Skip to main content
eScholarship
Open Access Publications from the University of California

Interindividual differences in predicting words versus sentence meaning: Explaining N400 amplitudes using large-scale neural network models

Abstract

Prediction error, both at the level of sentence meaning and at the level of the next presented word, has been shown to successfully account for N400 amplitudes. Here we investigate whether people differ in the representational level at which they implicitly predict upcoming language. We computed a measure of prediction error at the level of sentence meaning (termed semantic update) and a measure of prediction error at the level of the next word (surprisal). Both measures significantly accounted for N400 amplitudes even when the other measure was controlled for. Most important for current purposes, both effects were significantly negatively correlated. Moreover, random-effects model comparison showed that individuals differ in whether their N400 amplitudes are driven by semantic update only, by surprisal only, or by both, and that the most common model in the population was either semantic update or the combined model but clearly not the pure surprisal model.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View