Georgetown Laboratory for Relational Cognition
Our work is directed along the three primary lines described below. This research is funded by grants from The National Science Foundation, The John Templeton Foundation, The American Legacy Foundation, Pymetrics, and Partners in Research at Georgetown University.
Understanding something new by relating it to something familiar, e.g., seeing that a statistical prediction interval is abstractly similar to a fishing net, is fundamental to how humans think, from expert scientific reasoning to classroom learning. We are investigating the cognitive and neural bases of abstract relational reasoning, with a particular focus on how people understand abstract similarities between things that seem different on the surface. The most valuable similarities are the ones that reveal hidden connections between things that seem different (e.g., the connection between the structure of the atom and the structure of the solar system). These abstract, hidden similarities have been recognized by effective thinkers, from Kepler to Einstein to Steve Jobs, as the fundamental basis for understanding and teaching complex, novel concepts. Our research has initiated the development of a new area of “semantic distance” research in relational reasoning. Semantic distance research addresses the ways in which cognitive and neural processes of relational reasoning change as surface-level differences increase (i.e. as the connections become more and more abstract).
No ability is more valued in the modern innovation-fueled economy than thinking creatively on demand, and the ability to consciously engage a heightened state of creative thinking (i.e., to try and succeed at thinking more creatively) is important for education and a rich mental life. While brain-based creativity research has focused on static individual differences in trait creativity, little is known about how changes in brain function support a state of heightened creative thinking when creativity is required. In addition, it is largely unknown whether a person can consciously engage and disengage a heightened creative state dynamically across short durations of time. This is particularly surprising because mechanisms that make creativity dynamic within an individual are likely to be critical for enabling current efforts in science, education, and industry to improve creative thinking and augment creative output.
As a component of investigating the biological bases of reasoning our research seeks to identify pathways of effect through which genetic variations influence reasoning-related cognitive functions (Fossella et al., 2006; Green, Munafo, et al., 2008; Green & Dunbar, 2012; Green, Kraemer, et al., 2013; Green et al., 2014). While “cognitive neurogenetic” research of this kind shows strong potential, this emerging field has not yet outgrown serious theoretical and interpretive hazards. One focus of our research is to identify sources of these hazards. In critical analyses of recent research, we have outlined methodological considerations for the “intermediate phenotype approach,” and emphasized statistical and paradigmatic strategies to ensure that results can be meaningfully interpreted (Green, Munafo et al., 2008; Green & Dunbar, 2012). The long-term goal of this work, in combination with our primary research lines described above, is to develop a stronger vertical integration of data on human reasoning at cognitive, neural, and genetic levels. Toward this goal, we have developed and tested gene-brain-cognition effect pathways that integrate data at the genetic, neural, and behavioral levels within a single directional model. We have used this approach to show that activity in four frontal brain regions mediates effects of a dopamine system-related gene on executive attention and IQ (Green, Kraemer et al., 2013). In collaboration with Dr. G. William Rebeck in the Department of Neuroscience at Georgetown, we have conducted a series of studies investigating the effects of genetic Alzheimer’s risk factors on brain structures and functions that support reasoning and executive function. This work has further demonstrated the efficacy of gene-brain-cognition modeling, and has generally supported an antagonistic pleiotropy account of Alzheimer’s genetic risk whereby genetic variants that confer risk late in life actually confer neurocognitive advantages in young people (Green et al., 2014; DiBattista et al., 2014; Stevens et al., 2014).