HNDS-R: Brain network mechanisms of task-general cognition

Rutgers University Newark, Newark NJ

Investigators

Abstract

Abstract for "Brain network mechanisms of task-general cognition" What are the special properties of the human brain that make us intelligent? What is the brain circuitry underlying the intelligence of geniuses such as Leonardo da Vinci, Marie Curie, Albert Einstein and the neural basis for normal human intelligence, which surpasses machine and artificial intelligence in a broad spectrum of cognitive and social tasks? Understanding the neural basis of different varieties of human intelligence is one of the great challenges in neuroscience. In addition to the scientific quest to understand the nature of human cognition and intelligence, knowing what makes the human brain intelligent would have many useful applications. For example, this knowledge could potentially be used to guide brain stimulation to help those with learning disabilities. This knowledge could also help develop brain-computer interfaces and enhance artificial intelligence, with many applications in science, engineering, and business. This project will use brain imaging and brain stimulation to learn about some of the key brain network processes that make human intelligence possible. Converging evidence indicates that general human intelligence is primarily implemented by activity and connectivity in subnetworks of the brain termed cognitive control networks (CCNs). However, there is a critical need to determine how CCN activity and connectivity together generate intelligent goal-directed behavior. The overall objective of this project is to determine how CCNs implement intelligent behavior across a wide variety of different tasks. Researchers will use brain activity flow models – a novel method for determining how activity and connectivity together generate brain function –to investigate how CCNs implement task-general cognition underlying intelligent behavior. The project will utilize brain imaging and brain stimulation to build and test activity flow models of intelligent human behavior. This combined experimental and computational approach will allow testing of the hypothesis that CCNs dynamically re-route activity flows between sensory inputs and motor outputs, implementing intelligent behavior via conjunctive activations as well as dynamic connectivity changes. In addition to scientific research, this project will enhance Outreach and increase participation of underrepresented groups in STEM via supporting continuation and expansion of participation in the NSF-funded nationwide LSAMP (Louis Stokes Alliance for Minority Participation) program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.