Limited working memory resources are strategically allocated based on informativity.

Less predictable information is prioritized for working memory resources.

Less predictable linguistic units are encoded with more robust memory representation.

Less predictable target nouns exhibit weaker agreement attraction effect.

Language comprehension has been argued to be expectation-based, with more predictable linguistic units being easier to process. However, as a communicative tool, language is often used to deliver messages that are novel and informative, suggesting the necessity of some cognitive mechanisms handling less predictable but more informative content. This paper proposes strategic memory allocation as one such mechanism. Although less predictable linguistic units require greater processing effort for memory encoding, recognizing the inconsistency between top-down predictions and bottom-up perceptual input may signal the working memory system to prioritize these units, enhancing the robustness of their representation against interference. We examine this hypothesis through the lens of the agreement attraction effect in two self-paced reading experiments. In Experiment 1, we find that less predictable but more informative target nouns exhibit weaker agreement attraction in online reading times, especially with more fine-grained measures of predictability such as the surprisal from large language models. This weaker agreement attraction effect for less predictable target nouns confirms our hypothesis that informative linguistic units are prioritized and receive more robust memory representation. In Experiment 2, however, no modulation of agreement attraction emerges when we manipulate the predictability of distractor nouns, suggesting the need for a more nuanced characterization of how information is structured and operated in memory. Our findings highlight an interplay of memory, predictive processing, and implicit learning. We also discuss the implications of our result for memory efficiency and memory compression. More broadly, by demonstrating that the limited memory resources are dynamically optimized for the relevant processing task, the current study highlights a connection to the resource-rational analysis of human cognition in general.

Informativity

Prediction

Strategic memory allocation

Memory interference

Agreement attraction

Sentence processing

Resource-rational

© 2025 The Authors. Published by Elsevier Inc.