Do VLMs Align Better with Humans than LLMs during Natural Reading?

Abstract

Large language models have become increasingly useful computational models of human language processing, but it remains open whether vision-language learning makes text representations more human-like during natural reading. We address this question by comparing matched LLM and vision-language model pairs under strictly text-only input and evaluating alignment with human brain activity (whole-cortex fMRI) and human behavior (synchronized regressive saccades). We identify a selective, rather than global, effect of vision-language training on human-model alignment. In the two within-lineage model pairs, VLMs more accurately predicted human regressive saccades, whereas VLMs and LLMs showed comparable whole-cortex fMRI alignment. However, sentence-level analyses revealed that the VLM advantage in fMRI alignment increased with the visual evocative strength of the sentences. Together, these findings provide a controlled in-silico comparison of multimodal training histories, showing that vision-language pretraining selectively improves model-human alignment via reading behavior and visually grounded content.