Large-scale Vision-Language Models (LVLMs) integrate linguistic and visual information, demonstrating advanced task-solving capabilities. These models are originally derived from Large Language Models, leading to strong capabilities for language tasks. However, the impact of additional visual information on model responses remains insufficiently understood. In this study, we focus on the priming effect, a psychological phenomenon, to investigate how visual information influences language task processing. We present additional intentionally designed images alongside two types of language tasks with different characteristics and analyze changes in the model’s responses. Our experimental results show that model responses shift in the direction intended by the image, suggesting that LVLMs do not simply ignore visual information but actively incorporate it into language processing. Furthermore, the similarity between this behavior and priming effects observed in human cognition suggests that LVLMs may share certain aspects of human cognitive mechanisms.