Driving the brain towards creativity and intelligence: A network control theory analysis

Abstract

High-level cognitive constructs, such as creativity and intelligence, entail complex and multiple processes, including cognitive control processes. Recent neurocognitive research on these constructs highlight the importance of dynamic interaction across neural network systems and the role of cognitive control processes in guiding such a dynamic interaction. How can we quantitatively examine the extent and ways in which cognitive control contributes to creativity anf intelligence? To address this question, we apply a computational network control theory (NCT) approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how NCT relates to individual differences in distinct measures of creative ability and intelligence. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that intelligence is related to the ability to “drive” the brain system into easy to reach neural states by the right inferior parietal lobe and lower integration abilities in the left retrosplenial cortex. We also find that creativity is related to the ability to “drive” the brain system into difficult to reach states by the right dorsolateral prefrontal cortex (inferior frontal junction) and higher integration abilities in sensorimotor areas. Furthermore, we found that different facets of creativity—fluency, flexibility, and originality—relate to generally similar but not identical network controllability processes. We relate our findings to general theories on intelligence and creativity.

Art Education for the Development of Complex Thinking Metacompetence: A Theoretical Approach

“…The above coincides with the proposal of Goleman (2013), who points out that emotion has a direct influence on creative processes and thinking in general and, above all, in decision making, an important factor for the development of critical thinking. Kenett et al (2018) also agree that the integration of sensory-motor areas intervenes in the creative capacity, noting that this is consistent with the perspective of embodied cognition. This would take place through the sensory-motor simulation, the perceptual recreation and the acquisition of the introspective state during experiences with the world, the body and the mind.…”

“…Kenett et al () also agree that the integration of sensory‐motor areas intervenes in the creative capacity, noting that this is consistent with the perspective of embodied cognition. This would take place through the sensory‐motor simulation, the perceptual recreation and the acquisition of the introspective state during experiences with the world, the body and the mind.…”

Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics

“…Two such examples were briefly discussed in the review: the application of multiplex network analysis to examine how different cognitive domains interact (e.g., [65,66,207]) and the application of percolation theory to study cognitive phenomena such as memory decline or flexibility of thought (e.g., [119,201]). Cognitive network science could also benefit from adopting and contributing to state-of-theart network methodologies used to study neural systems and brain dynamics, such as network dynamic analysis [208] and network control theory [209][210][211]. Network dynamic analysis examines time varying community assignments over brain functional connectivity networks and has attributed state flexibility-variation of assignment of a brain region to a specific community across time-to capacities such as motorskill learning and language comprehension [208].…”

“…Network control theory quantifies the extent that different nodes in a network drive the dynamics over the network. Recent studies have applied network control theory to the analysis of white-matter connectivity networks to examine the roles of different brain regions in driving neural dynamics [209][210][211]. Importing such state-of-the-art methods to the cognitive domain could greatly advance the study of dynamics in cognitive networks.…”

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

“…Brain changes its state according to the environment similar to an artificially intelligent machine which adapts to learn from the input attributes without being explicitly programmed. So, a BCI illiterate at one point of time may adapt to learn with continuous trials due to dynamic brain states [103].…”