# Syntactically-guided Information Maintenance in Sentence Comprehension

arXiv: https://arxiv.org/abs/2604.27468 · HTML: https://mlsanity.com/paper/2604.27468/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?](https://mlsanity.com/paper/2212.12131/) (2022)
- [Testing the Predictions of Surprisal Theory in 11 Languages](https://mlsanity.com/paper/2307.03667/) (2023)
- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [The Natural Stories Corpus](https://mlsanity.com/paper/1708.05763/) (2017)
- [Release of Pre-Trained Models for the Japanese Language](https://mlsanity.com/paper/2404.01657/) (2024)
- [Strategic resource allocation in memory encoding: An efficiency principle shaping language processing](https://mlsanity.com/paper/2503.14728/) (2025)
- [Information-Theoretic Storage Cost in Sentence Comprehension](https://mlsanity.com/paper/2602.18217/) (2026)

## Built on by (most cited first, 1 of 1)

- [Using LMs to Model the Effects of Context and Coreference during Sentence Comprehension](https://mlsanity.com/paper/2609.32119/) (2026)

## Abstract (arXiv)

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 in 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. These patterns are not evident in English, however, and we highlight some issues to be resolved to understand the contribution of syntax in memory-efficient processing of various languages.