6 papers
Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
Edith Haim, Kurt Haim, Roger E. Beaty +2
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 on…
Cognitive networks reconstruct mindsets about STEM subjects and educational contexts in almost 1000 high-schoolers, University students and LLM-based digital twins
Francesco Gariboldi, Emma Franchino, Edith Haim +3
Attitudes toward STEM develop from the interaction of conceptual knowledge, educational experiences, and affect. Here we use cognitive network science to reconstruct group mindsets…
How to predict creativity ratings from written narratives: A comparison of co-occurrence and textual forma mentis networks
Roberto Passaro, Edith Haim, Massimo Stella
This tutorial paper provides a step-by-step workflow for building and analysing semantic networks from short creative texts. We introduce and compare two widely used text-to-networ…
SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks
Salvatore Citraro, Edith Haim, Alessandra Carini +3
We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool is designed to perform numerical simulation…
Cognitive networks highlight differences and similarities in the STEM mindsets of human and LLM-simulated trainees, experts and academics
Edith Haim, Lars van den Bergh, Cynthia S. Q. Siew +3
Understanding attitudes towards STEM means quantifying the cognitive and emotional ways in which individuals, and potentially large language models too, conceptualise such subjects…
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters
Edith Haim, Natalie Fischer, Salvatore Citraro +2
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 a…