Syntactically-guided Information Maintenance in Sentence Comprehension

2026-04-30

AI Summary

This study addresses how limited cognitive resources shape the maintenance of contextual information during real-time language comprehension. The authors propose that language users selectively retain only those syntactic elements critical for future prediction, thereby optimizing cognitive allocation. Leveraging natural reading-time data and exploiting Japanese’s inherent contrasts between syntactic structure and predictability, the research provides the first clear dissociation and empirical validation of the independent contributions of “number of predicted heads” and “number of unresolved dependencies” to the cost of information maintenance, demonstrating that these factors are not reducible to one another. Furthermore, the findings reveal that the processing benefits conferred by linguistic predictability are amplified when readers invest additional time in maintaining contextual information, supporting a rational mechanism of selective information retention.

Abstract

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.

Problem

• information maintenance
• sentence comprehension
• syntactic structure
• predictability
• cognitive cost

Innovation

• syntactic guidance
• information maintenance
• predictability
• dependency completion
• reading time