Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and AI-simulated raters

Abstract Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human-, GPT3.5-, and Sonnet 3.7-generated stories. Using Explainable Artificial Intelligence (XAI) we test whether features relative to Mednick’s associative theory of creativity can explain creativity ratings assigned by humans or AI raters. Using XGBoost, we examine 5 scenarios: (i)