Abstract
Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian
reasoners, and modeling the comprehension process with Bayesian decision trees. In this paper we extend the Narayanan and Jurafsky model
to make further predictions about reading time given the probability of
difference parses or interpretations, and test the model against reading
time data from a psycholinguistic experiment.
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