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Humans exhibit garden path effects: When reading sentences that aretemporarily structurally ambiguous, they slow down when the structure isdisambiguated in favor of the less preferred alternative. Surprisal theory(Hale, 2001; Levy, 2008), a prominent explanation of this finding, proposesthat these slowdowns are due to the unpredictability of each of the words thatoccur in these sentences. Challenging this hypothesis, van Schijndel & Linzen(2021) find that estimates of the cost of word predictability derived fromlanguage models severely underestimate the magnitude of human garden patheffects. In this work, we consider whether this underestimation is due to thefact that humans weight syntactic factors in their predictions more highly thanlanguage models do. We propose a method for estimating syntactic predictabilityfrom a language model, allowing us to weigh the cost of lexical and syntacticpredictability independently. We find that treating syntactic predictabilityindependently from lexical predictability indeed results in larger estimates ofgarden path. At the same time, even when syntactic predictability isindependently weighted, surprisal still greatly underestimate the magnitude ofhuman garden path effects. Our results support the hypothesis thatpredictability is not the only factor responsible for the processing costassociated with garden path sentences.

Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities