Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples

2026-06-08

AI Summary

Traditional creativity research has been constrained by single-task paradigms, limiting its ability to comprehensively capture the semantic memory structures underlying creative cognition. This study integrates multilingual associative data from six cognitive tasks to construct, for the first time, multidimensional semantic networks that represent cross-culturally relevant knowledge organization associated with creativity. Through semantic network modeling, structural reducibility analysis, spreading activation simulations, affective scoring, and ridge regression prediction, we find that high- and low-creativity individuals exhibit significantly distinct network architectures—a difference absent in AI-generated networks. Moreover, different task layers provide complementary information, and incorporating structural similarity features boosts creativity prediction accuracy by 50%, with network topology metrics contributing most substantially to this improvement.

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.

Problem

• creativity
• semantic memory
• multiplex networks
• associative knowledge
• cross-cultural

Innovation

• multiplex semantic networks
• creativity modeling
• semantic memory
• spreading activation
• cross-cultural cognition