Barbey (2018) proposes a Network Neuroscience Theory of Human Intelligence, suggesting that general intelligence (g) emerges from the small-world topology of brain networks and the dynamic reorganization of their community structure, promoting system-wide flexibility and adaptation. . Crystallized intelligence involves easily accessible network states, utilizing prior knowledge and experience, while fluid intelligence recruits difficult to access network states, supporting cognitive flexibility and adaptive problem solving.
The ability to flexibly transition between network states is the basis for the g-factor, enabling rapid exchange of information between networks and capturing individual differences in information processing at a global level. Network neuroscience research highlights the importance of brain network dynamics in intelligence, rather than focusing on specific brain regions or isolated networks.
The article reviews neuroscience evidence to elucidate how g-factor emerges from individual differences in the network architecture of the human brain. He discusses how the small-world topology of brain networks, with short- and long-distance connections, allows for high local and global efficiency in information processing.
Network Neuroscience Theory offers a new perspective on intelligence, emphasizing network flexibility and dynamics rather than functional localization. This framework lays the foundation for new approaches to understanding individual differences in general intelligence by examining the topology and dynamics of the global brain network.
Reference:
BARBEY, Aron K. Network neuroscience theory of human intelligence. Trends in Cognitive Sciences , vol. 22, no. 1, p. 8-20, 2018.
