Investigating the complexity of creative thinking
The human mind can be extremely flexible as we solve problems and create new ideas. How could we possibly study the complex multiple cognitive capacities that support such flexibility? More generally, how can we study the complex cognitive and neural processes and dynamics that give rise to higher-level cognition such as creative thinking? A multidiscplinary field that has developed in the past two decades to computationally investigate complex systems is network science. Network science is based on mathematical graph theory, providing quantitative methods to investigate complex systems, and the processes that operate in these systems, as networks. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. The application of network science in cognitive science provides a powerful quantitative approach to represent cognitive systems (such as memory and language); enables a deeper understanding of cognition by capturing how the structure and processes operating on a network structure interact to produce behavioral phenomena; and provides a quantitative framework to model the dynamics of cognitive systems. To demonstrate the potential of applying network science in cognitive science, I will present a series of studies that investigate how differences in semantic memory relate to different facets of creativity in low and high creative individuals. They include computionally representing and investigating their structure of semantic memory (both at the group and individual level), simulating uncontrolled search processes over their semantic memory, examining the relations of semantic memory structure to creative achievement and fluid intelligence, and relating flexibility of thought to the robustness of their semantic networks to attack. Finally, I will demonstrate how the quantitative language of networks can be used to bridge across different levels of analysis (computational, behavioral, neural).