Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
Researchers have developed multiplex semantic networks, a layered approach to modeling the associative knowledge underlying creativity. By analyzing data from six cognitive tasks across 518 individuals from four countries, they found that different task layers capture distinct, non-redundant information about semantic organization. This method improved prediction accuracy for individual creativity scores by 50% when combined with machine learning, highlighting the importance of diverse data and structural network measures. AI
IMPACT This research offers a novel method for understanding and predicting creativity, potentially impacting AI systems designed for creative tasks.