Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Traditional white-box interpretability methods are inapplicable to closed-source architectures and passive metrics fail to isolate causal features. We introduce Counterfactual Semantic Saliency (CSS). This black-box, model-agnostic framework quantifies the importance of objects by measuring the semantic shift induced by their causal ablation from a scene. To evaluate AI-human semantic alignment, we tested prominent VLMs against a human psychophysics baseline comprising 16,603 valid responses across 307 complex natural scenes and 1,306 high-fidelity counterfactual variants. Our analysis reveals a pervasive scene comprehension gap: models exhibit an over-reliance (relative to humans) on object size, object centrality, and low-level saliency of objects. In contrast, models are less sensitive to the presence of people in the scenes than our human participants when describing the images. The over-reliance on object size is a primary driver explaining variations in model-human semantic divergence. Code and data are available at https://github.com/starsky77/Counterfactual-Semantic-Saliency-Data .

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