Network Neurocognition of Intelligence and Personality Differences
Intelligence describes our ability to reason, to understand complex ideas and to learn from experiences. It is associated with important life outcomes like educational or occupational success and seem to play a role even for health and longevity. Although it is one of the oldest psychological constructs, it is still of high relevance and constitutes a reliable indicator of general cognitive ability. Understanding the biological bases of human intelligence is an important scientific aim and former neuroscientific research has identified differences in brain structure and brain function covarying with individual variations in intelligence.
Network Neurocognition is a scientific discipline transferring methods from physics and mathematics to the investigation of human neuroimaging data (MRI, fMRI). Recently, it has been shown to be especially fruitful in the context of individual differences.
Our lab focusses on Personality Network Neurocognition as a new field of investigation applying graph-theoretical network approaches to established psychological theories about intelligence and human personality. By using its rich methodology and by adopting a system-level perspective on the brain, we aim to advance biologically-plausible theories of intelligence and personality, e.g., by unraveling the complex interaction between general intelligence and controlled attention.
Finally, our interdisciplinary team uses methods from Machine Learning to further develop connectome-based predictive modelling approaches. By using this methodology, we aim to go beyond correlative associations and to achieve robust out-of-sample predictions, i.e., predicting individual intelligence test scores on the basis of dynamic brain connectivity. Most of our research endeavors are based on MRI and fMRI data from large data bases such as the Human Connectome Project (www.humanconnectomeproject.org) and in general, our team fosters principle of Open and Reproducible Science.