Computational Sentence-Level Metrics of Reading Speed and Its Ramifications for Sentence Comprehension.

ABSTRACT: The majority of research in computational psycholinguistics on sentence processing has focused on word-by-word incremental processing within sentences, rather than holistic sentence-level representations. This study introduces two novel computational approaches for quantifying sentence-level processing: sentence surprisal and sentence relevance. Using multilingual large language models (LLMs), we compute sentence surprisal through three methods, chain rule, next sentence prediction, and negative log-likelihood, and apply a "memory-aware" approach to calculate sentence-level semantic relevance based on convolution operations. The sentence-level metrics developed are tested and compared to validate whether they can predict the reading speed of sentences, and, further, we explore how sentence

SUBMITTER: Sun K 

PROVIDER: S-EPMC12281087 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature