Can Peanuts Fall in Love with Distributional Semantics?
This challenges cognitive theories by showing that distributional semantics alone can explain some language processing effects, potentially reducing the need for complex mental representations.
The study tackled whether explicit situation models are necessary for contextual effects in language comprehension by modeling N400 amplitude data from a prior experiment using computational language models and word vectors without situation models. They found that a subset of these models could fully replicate the effect, suggesting situation models might not be required for such processing.
Context changes expectations about upcoming words - following a story involving an anthropomorphic peanut, comprehenders expect the sentence the peanut was in love more than the peanut was salted, as indexed by N400 amplitude (Nieuwland & van Berkum, 2006). This updating of expectations has been explained using Situation Models - mental representations of a described event. However, recent work showing that N400 amplitude is predictable from distributional information alone raises the question whether situation models are necessary for these contextual effects. We model the results of Nieuwland and van Berkum (2006) using six computational language models and three sets of word vectors, none of which have explicit situation models or semantic grounding. We find that a subset of these can fully model the effect found by Nieuwland and van Berkum (2006). Thus, at least some processing effects normally explained through situation models may not in fact require explicit situation models.