While recent research suggests Large Language Models match human creative performance in divergent thinking tasks, visual creativity remains underexplored. This study compared image generation in human participants (Visual Artists and Non Artists) and using an image generation AI model (two prompting conditions with varying human input: high for Human Inspired, low for Self Guided). Human raters (N=255) and GPT4o evaluated the creativity of the resulting images. We found a clear creativity gradient, with Visual Artists being the most creative, followed by Non Artists, then Human Inspired generative AI, and finally Self Guided generative AI. Increased human guidance strongly improved GenAI's creative output, bringing its productions close to those of Non Artists. Notably, human and AI raters also showed vastly different creativity judgment patterns. These results suggest that, in contrast to language centered tasks, GenAI models may face unique challenges in visual domains, where creativity depends on perceptual nuance and contextual sensitivity, distinctly human capacities that may not be readily transferable from language models.

翻译：尽管近期研究表明，大语言模型在发散性思维任务中与人类创造性表现相当，但视觉创造力领域仍待探索。本研究对比了人类参与者（视觉艺术家与非艺术家）与图像生成AI模型（两种提示条件：高人类参与的"人类启发"与低人类参与的"自主引导"）生成的图像。人类评分员（N=255）与GPT4o对生成图像的创造性进行了评估。结果呈现清晰的创造力梯度：视觉艺术家创造力最高，其次是非艺术家、人类启发生成式AI，最后是自主引导生成式AI。增加人类引导显著提升了生成式AI的创造性输出，使其作品质量接近非艺术家水平。值得注意的是，人类与AI评分员在创造性评判模式上亦存在显著差异。这些结果表明，与语言中心型任务不同，生成式AI模型在视觉领域可能面临独特挑战——在此领域中，创造力依赖于感知微妙性与情境敏感性，这些人类特有的能力可能难以从语言模型中直接迁移。