Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
2026-06-08 • Computation and Language
Computation and Language
AI summaryⓘ
The authors studied creativity by looking at how people organize and connect knowledge in their minds using different language tasks. They created layered semantic networks from six tasks done by over 500 people from four countries, showing that each task reveals unique information about how knowledge is organized. They found that combining these layers better captures differences between high- and low-creativity people than using single tasks. Their machine learning model could predict creativity scores more accurately when using these combined layers. This approach offers a richer, culturally diverse way to understand and measure creativity.
CreativitySemantic memorySemantic networksMultiplex networksVerbal fluencySpreading activationRidge regressionMachine learningCognitive tasksAssociative knowledge
Authors
Edith Haim, Kurt Haim, Roger E. Beaty, Cynthia S. Q. Siew, Massimo Stella
Abstract
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.