How well does surprisal explain N400 amplitude under different experimental conditions?
This addresses the problem of understanding human language processing mechanisms for neurolinguistics researchers, but it is incremental as it builds on existing surprisal and N400 studies.
The study tackled the problem of predicting N400 amplitude, a neural measure of language processing difficulty, using word surprisal from recurrent neural networks, finding that surprisal predicts amplitude in many cases and failures offer insights into neurocognitive processes.
We investigate the extent to which word surprisal can be used to predict a neural measure of human language processing difficulty - the N400. To do this, we use recurrent neural networks to calculate the surprisal of stimuli from previously published neurolinguistic studies of the N400. We find that surprisal can predict N400 amplitude in a wide range of cases, and the cases where it cannot do so provide valuable insight into the neurocognitive processes underlying the response.