Network science provides a set of quantitative methods to investigate complex systems,
including human cognition. Although cognitive theories in different domains are strongly …

Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a
goal that has greatly benefitted from the application of network science approaches. This …

Modern AI technologies make use of statistical learners that lead to self-empiricist logic,
which, unlike human minds, use learned non-symbolic representations. Nevertheless, it …

A fundamental goal of education is to inspire and instill deep, meaningful, and long-lasting
conceptual change within the knowledge landscapes of students. This commentary posits …

In order to investigate how high school students and researchers perceive science-related
(STEM) subjects, we introduce forma mentis networks. This framework models how people …

Fostering creativity is vital for tackling 21st-century challenges, and education plays a key
role in nurturing this skill. According to the associative theory, creativity involves connecting …

Cognitive scientists have a long-standing interest in quantifying the structure of semantic
memory. Here, we investigate whether a commonly used paradigm to study the structure of …

Math anxiety is a clinical pathology impairing cognitive processing in math-related contexts.
Originally thought to affect only inexperienced, low-achieving students, recent investigations …

Discovering the most suitable network structure of the learning domain represents one of the
main challenges of knowledge delivery and acquisition. We propose a multidimensional …

For over five decades, researchers have used network analysis to understand educational
contexts, spanning diverse disciplines and thematic areas. The wealth of traditions and …