Research
Our work is directed along multiple lines described below. This research is funded by grants from The National Science Foundation, The John Templeton Foundation, The American Legacy Foundation, Pymetrics, Monad, and Partners in Research at Georgetown University. You can read more about the lab here.
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.
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Leveraging neuroimaging-based insights into the mechanisms of augmented state creativity (i.e., neural changes that lead to more creative thinking), we are conducting a suite of projects aimed at enhancing these endogenous mechanisms via exogenous modulation of brain function (Weinberger et al., 2017; 2018). This research has found that anodal transcranial direct current stimulation (tDCS) targeting the same frontopolar brain region we have previously implicated in augmented state creativity (see Green 2016 for review) yielded more creative relational thinking, including formulation of more semantically distant analogical connections (Green et al. 2017).
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Even among people who are very technically skilled at what they do, it is common to find individuals who recoil when they are asked to generate a new idea of their own. This apparent anxiety at the prospect of having to be creative may be associated with the colloquial refrain, “I’m not a creative person,” frequently used to turn aside requests for creative input or justify avoidance of creative pursuits. While this phrase is sometimes spoken lightheartedly, avoidance of creative endeavors has become an increasing impediment to advancement in the modern innovation economy. Creative abilities are highly prized across a wide range of fields and the ability to maximize one’s creative potential is only likely to become a more essential determinant of success in the innovation economy as creativity increasingly emerges as the human ability least achievable by artificial intelligence. Relatedly, fostering creative thinkers is a primary goal of educators from kindergarten through graduate school, and the ability to think creatively consistently predicts academic achievement. Thus, characteristics that keep people from realizing their creative potential are likely to have substantial impacts on achievement and opportunity.
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Fostering true conceptual understanding and effective reasoning strategies in the minds of students, not just the right answers on the test, is a goal of teachers in every science, technology, engineering, and mathematics (STEM) classroom. Recent advances in tools used to analyze images of the human brain allow detection of complexly-patterned changes in the brains of students that signify learning of STEM concepts and STEM-relevant relational reasoning strategies that. This advance may open a window onto biomarkers of precisely the type of learning that is the goal of educators. Critically, this new approach has not yet been applied to longitudinal learning in a real-world classroom, i.e., how the brain changes over time during schooling and how those brain changes relate to changes in knowledge and thinking skills. If it is possible to observe brain changes that correspond to classroom-based strengthening of concepts and thinking strategies, then – in conjunction with traditional assessment methods – this method has transformative potential to help identify the most effective real-world educational practices, and could profoundly influence the future use of brain imaging in education and education research.
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Creative thinking is critical to scientific advancement. Generating novel hypotheses, flexibly connecting diverse information, and envisioning solutions to ill-defined problems all depend on scientific creativity. In the STEM fields, creativity is the difference between innovation and standard competency, and that difference has become a central determinant of opportunity for STEM students. Fostering creative thinkers is a primary goal of educators from kindergarten through graduate school, and creative thinking ability consistently predicts academic achievement. However, the strong historical association of STEM exclusively with technical, rather than creative, thinking skills has left STEM creativity severely under- researched. Thus, despite the timeliness of the question, very little is known about how to foster creative thinking in STEM education. There is a critical need for rigorously developed tools for measuring STEM creativity that educators and researchers can apply to test how different teaching/training approaches impact scientific creativity. For many students, we don’t know what works, and we don’t yet have the tools to find out. Psychology and neuroscience have made considerable progress in characterizing domain-general creativity (e.g., divergent thinking), including brain-based predictions of an individual's creative ability. This project aims to leverage these discoveries to characterize, and potentially enhance, scientific creative thinking, starting with the development (in collaboration with teachers) of a classroom-useable scale to measure the development of scientific creativity in grade-school students.
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How are the beliefs we hold about the world, including both religious and nonreligious beliefs, determined by neurocognitive processes ranging from bottom-up learning of relatedness in patterns to relational schema representations of people and even of gods? We are conducting studies of these underlying mechanisms of belief through both behavioral and neuroimaging methods (Green & Moghaddam, 2018). This work engages commonalities and differences among diverse sets of believers, e.g., samples in the U.S. and Afghanistan, and samples who vary in their levels of belief and disbelief.
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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).
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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.
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