Syntactically-guided Information Maintenance in Sentence Comprehension

TL;DR

This paper proposes a syntactically-guided model for selective information maintenance during sentence comprehension, demonstrating how syntactic factors influence processing costs and predictability benefits in Japanese reading data.

Contribution

It introduces a novel account distinguishing two syntactic factors affecting maintenance cost, supported by naturalistic reading data and analysis of their interaction.

Findings

Maintenance cost is influenced by predicted heads and incomplete dependencies.

Readers slow down for maintenance benefit more from predictability.

The two factors are distinct and not reducible to each other.

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.