Do Vision-Language Models Understand Visual Persuasiveness?
This work addresses the problem of evaluating VLMs' ability to grasp visual persuasion for researchers in AI and human-computer interaction, but it is incremental as it builds on existing VLM capabilities.
The study investigated whether vision-language models (VLMs) understand visual persuasiveness by constructing a dataset and analyzing model performance, finding that VLMs have a recall-oriented bias and weak discriminative power for low/mid-level features, with high-level semantic alignment being the strongest predictor of human judgment.
Recent advances in vision-language models (VLMs) have enabled impressive multi-modal reasoning and understanding. Yet, whether these models truly grasp visual persuasion-how visual cues shape human attitudes and decisions-remains unclear. To probe this question, we construct a high-consensus dataset for binary persuasiveness judgment and introduce the taxonomy of Visual Persuasive Factors (VPFs), encompassing low-level perceptual, mid-level compositional, and high-level semantic cues. We also explore cognitive steering and knowledge injection strategies for persuasion-relevant reasoning. Empirical analysis across VLMs reveals a recall-oriented bias-models over-predict high persuasiveness-and weak discriminative power for low/mid-level features. In contrast, high-level semantic alignment between message and object presence emerges as the strongest predictor of human judgment. Among intervention strategies, simple instruction or unguided reasoning scaffolds yield marginal or negative effects, whereas concise, object-grounded rationales significantly improve precision and F1 scores. These results indicate that VLMs core limitation lies not in recognizing persuasive objects but in linking them to communicative intent.