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Study Finds Multimodal Training Provides Selective, Not Global, Benefits for Human-Like Language Processing

[Submitted on 27 May 2026]

18d ago· 2 min readenInsight

Summary

This research paper investigates whether vision-language models (VLMs) produce text representations that are more human-like than large language models (LLMs) during natural reading. By comparing tightly matched LLM and VLM pairs in a text-only setting, the study isolates the effect of multimodal training history. Using a human natural-reading dataset with fMRI responses and eye-tracking data, the authors found that multimodal pretraining does not provide a uniform global advantage in human alignment. However, VLMs showed selective advantages when sentences contained stronger visual semantic content, with converging evidence from both brain imaging and eye movement data.

Source

bskyStudy Finds Multimodal Training Provides Selective, Not Global, Benefits for Human-Like Language Processingarxiv.org

Key quotes

· 4 pulled

Our findings demonstrate that multimodal pretraining may not confer a uniform, global advantage in human alignment during natural reading

Language-internal representations remain the key factor for modeling human text processing

The VLM advantage could emerge more selectively when sentences contain stronger visual semantic content

Multimodal pretraining contributes selectively rather than globally to human-like language representations during natural reading

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Large language models (LLMs) have become increasingly useful computational models of human language processing, but it remains unclear whether vision-language learning makes text representations more human-like during natural reading. Here, we address thi

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