稳定扩散模型揭示了人类与AI在视觉创造力中的持续性差距
Stable diffusion models reveal a persisting human and AI gap in visual creativity
AI总结：
研究比较了人类参与者与AI图像生成模型在视觉创造力上的表现，发现视觉艺术家最具有创造力，随后是非艺术家，再是受人类启发的生成AI，最后是自我引导的生成AI。人类指导显著提升了生成AI的创造力，但人类与AI评估者在创造力判断上存在显著差异。
摘要：
尽管近期研究表明大型语言模型在发散思维任务中与人类创造力表现相当，但视觉创造力仍被忽视。本研究比较了人类参与者（视觉艺术家和非艺术家）与使用图像生成AI模型（两种提示条件：高人类启发和低自我引导）在图像生成中的表现。人类评估者（N=255）和GPT4o评估了生成图像的创造力。研究发现存在明显的创造力梯度，视觉艺术家最具有创造力，随后是非艺术家，然后是受人类启发的生成AI，最后是自我引导的生成AI。增加的人类指导显著提高了生成AI的创造力输出，使其作品接近非艺术家的作品。值得注意的是，人类和AI评估者在创造力判断模式上也存在显著差异。这些结果表明，与语言中心任务不同，生成AI模型在视觉领域可能面临独特的挑战，因为创造力依赖于感知细微差别和情境敏感性，这些是明显人类能力，可能无法轻易从语言模型中转移。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
