When the food arrives before the menu: Modeling event-driven surprisal in language comprehension

We present a neurocomputational—recurrent artificial neural network—model of language processing that integrates linguistic knowledge and world/event knowledge, and that produces word surprisal estimates that take into account both. Our model constructs a cognitively motivated situation model of the state-of-the-affairs as described by a sentence. Critically, these situation model representations inherently encode world/event knowledge. We show that the surprisal estimates that our model produces reflect both linguistic surprisal as well as surprisal that is driven by knowledge about structured events. We outline how we can employ the model to explore the interaction between these types of knowledge in online language processing.