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