Connectivity and creativity in semantic neural networks

Creativity and insight are distinctive attributes of human cognition, but their neural basis remains poorly understood due to the difficulty of experimental study. As such, computational modeling can play an important role in understanding these phenomena. Some researchers have proposed that creative individuals have a “deeper” organization of knowledge, allowing them to connect remote associates and form novel ideas. It is reasonable to assume that the depth and richness of semantic organization in individual minds is related to the connectivity of neural networks involved in semantic representation. In this paper, we use a simple and plausible neurodynamical model of semantic networks to study how the connectivity structure of these networks relates to the richness of the semantic constructs, or ideas, they can generate. This work is motivated, in part, by research showing that experimentally obtained semantic networks have a specific connectivity pattern that is both small-world and scale-free. We show that neural semantic networks reflecting this structure have richer semantic dynamics than those with other connectivity structures. Though simple, this model may provide insight into the important issue of how the physical structure of the brain determines one of the most profound features of the human mind - its capacity for creative thought.