用多任务构建多层语义网络，更全面捕捉跨文化创造力的关联知识。
Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
- 通过六种认知任务构建多层语义网络，避免单一任务局限性。
- 组合相似结构层使创造力预测准确率提升50%。
- 适合研究创造力、认知神经科学与跨文化心理学的研究者。
创造力是一种依赖语义记忆组织与检索的复杂认知能力，但多数研究仅用单一任务衡量，难以全面反映其复杂性。本研究探讨了从六种认知任务中构建的多层语义网络（multiplex networks）作为更全面的创造力关联知识建模方法。数据来自4个国家（奥地利、美国、新加坡、意大利）共518名个体，通过词汇流畅性、句子链、自由联想和叙事写作任务构建语义网络，并整合为多层结构。以基于AI人格的生成结果作对比基准。结构可简化性分析显示，不同任务层捕获了非冗余的信息，支持多任务而非单一任务。高创造力与低创造力群体的网络保持结构差异，而AI生成网络则无论创造力分组均呈现近乎一致结构。最后，利用12个特征（网络度量、情感评分、激活扩散模拟）在岭回归模型中预测个体创造力得分，发现经前期筛选出的结构相似层组合，使概念验证预测准确率提高50%。结构度量贡献最高，激活扩散动态提供额外预测力。结果表明，多层语义网络能更丰富地刻画跨文化的创造力关联知识。研究还公开数据集与代码，促进创造力领域的多样化计算研究。
原文摘要 · Abstract (English)
Creativity is a complex cognitive ability that relies on knowledge organisation and retrieval from semantic memory. Yet most research uses a single task to measure it, capturing only a fraction of this complexity. This study investigates multiplex networks - layered semantic networks obtained from six cognitive tasks - as a more comprehensive approach to modelling the associative knowledge underlying creativity. We collected data from N=518 individuals from four countries (Austria, USA, Singapore, Italy). From their responses to verbal fluency, sentence-chain, free association, and narrative writing tasks, we constructed semantic networks and assembled them in a multiplex structure. AI persona-based responses provided a comparison baseline. Structural reducibility analyses showed that different task layers captured distinct, non-redundant information about semantic organisation, supporting the use of multiple tasks over any single one. The networks from high- and low-creative groups remained structurally distinct, while AI-generated networks showed near-identical structures regardless of creativity group. Finally, we used 12 features (network measures, emotional scores, and spreading activation simulations) in a machine learning model using ridge regression to predict individual creativity scores. The combination of structurally similar layers, as identified in the previous stage, improved a proof-of-concept prediction accuracy by 50%. Structural measures showed the highest feature importance, with spreading activation dynamics providing additional predictive power. Together, these findings indicate that multiplex semantic networks capture a richer, cross-cultural picture of associative knowledge underlying creativity. We also release our diverse dataset and code to foster diverse computational approaches within the creativity community.
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