Syntactically-guided Information Maintenance in Sentence Comprehension
For psycholinguistics researchers, this work reconciles two competing hypotheses about maintenance cost in sentence comprehension, though it is an incremental contribution.
The paper proposes that language users selectively maintain information based on syntactic structure, with maintenance cost affected by both the number of predicted heads and incomplete dependencies. Using Japanese reading time data, they show these factors are not reducible to each other and that readers who slow down for maintenance benefit more from predictability.
Maintaining information in context is essential in successful real-time language comprehension, but maintenance is cognitively costly and can slow processing. We hypothesize that rational language users selectively maintain information that is crucial for future prediction, guided by syntactic structure. Under this view, two factors affect maintenance cost: the number of predicted heads and the number of incomplete dependencies. Although these factors have been treated as competing hypotheses in the literature, our account predicts that they are not reducible to one another. We show this is the case, using a naturalistic reading time dataset in Japanese, a language in which the two factors contrast particularly clearly. We further show that there is a tradeoff such that readers that slow down for maintenance tend to benefit more from predictability, providing additional support for the proposed account.