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.

翻译：我们调查了超常词在多大程度上可用于预测人类语言处理困难的神经量度-N400。 为此,我们利用经常性神经网络来计算先前出版的N400神经语言学研究的刺激性超常值。我们发现超常词可以预测在很多情况下N400振幅,而如果它无法预测,则能对反应背后的神经认知过程提供宝贵的洞察力。