研究人类与AI协作创作艺术时的集体创新规律。
Dynamics of collective creativity in AI art competitions
- 通过分析13个月内的368场艺术接力活动，追踪图像演化路径。
- 图像逐渐简化并趋同于特定主题（如蒸汽朋克、外星建筑）。
- 虽更复杂的原创图吸引更多点赞，但用户偏好更简单的再创作。
创造力是文化演进的核心，但群体如何产生新颖性仍难以从历史记录中推断。已有研究显示，迭代学习会使文化产物趋向学习者的归纳偏见，但多数实验基于人类线性链路，未能揭示网络化人机系统中的动态。本研究以Artbreeder平台的每日“混剪派对”为对象，该平台允许用户基于单一种子图持续共创，形成分支式演化图像谱系。我们分析了13个月间368场活动产生的130,882张图像，发现图像随迭代趋于简化，并收敛至若干共同主题“吸引子”（如蒸汽朋克场景、外星建筑）。尽管更新颖的父图能生成更复杂且更受欢迎的子图，但用户实际上更倾向再创作较不新颖、较简单的图像。此外，规模更大的混剪活动虽带来更高新颖性，却牺牲了图像复杂度。
原文摘要 · Abstract (English)
Creativity is a fundamental aspect of how culture evolves, yet the mechanisms by which groups produce novelty are notoriously difficult to infer from the historical record. Iterated learning experiments have shown that cultural transmission reliably distorts artifacts toward the inductive biases of learners, but most of this work uses linear chains between human participants, leaving open how these dynamics play out in the networked, human-AI systems that increasingly shape cultural production. In this study, we leverage one such system, Artbreeder, which hosts daily "remix parties" where users iteratively build on each other's work from a single seed image, producing branching lineages of human-AI co-created images. We analyze a dataset of 130,882 images from 368 remix parties over 13 months and find that images become simpler and converge toward common thematic "attractors" (e.g., steampunk scenes, alien architecture). We also find that while more novel "parent" images produce more novel and complex "children" that attract more likes, users paradoxically prefer to remix images that are less novel and complex. Finally, larger remix parties produce more novelty at the cost of lower complexity.
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