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Context changes expectations about upcoming words - following a storyinvolving an anthropomorphic peanut, comprehenders expect the sentence thepeanut was in love more than the peanut was salted, as indexed by N400amplitude (Nieuwland & van Berkum, 2006). This updating of expectations hasbeen explained using Situation Models - mental representations of a describedevent. However, recent work showing that N400 amplitude is predictable fromdistributional information alone raises the question whether situation modelsare necessary for these contextual effects. We model the results of Nieuwlandand van Berkum (2006) using six computational language models and three sets ofword vectors, none of which have explicit situation models or semanticgrounding. We find that a subset of these can fully model the effect found byNieuwland and van Berkum (2006). Thus, at least some processing effectsnormally explained through situation models may not in fact require explicitsituation models.

Can Peanuts Fall in Love with Distributional Semantics?