Nanyang Business School
Research Director, Culture Science Institute
Affiliated Scholar, Virginia Tech Carilion Research Institute
Office: 6790 5746
Assistant Professor G. Christopoulos holds a PhD from University of Cambridge and has extensive postdoctoral experience (Cambridge, Virginia Tech and Baylor College of Medicine). He is currently the Research Director of the Culture Science Institute at NBS.
I am mostly a Decision Neuroscientist, i.e. examine the neurobehavioral underpinnings of human decision making, especially in volatile environments. In a series of studies we identified the neural correlates underlying decision making under risk and risk attitudes (PNAS, 2009; Journal of Neuroscience, 2009, 2010). More recent studies have explored the basic neurocomputational parameters underlying learning in social environments.
In a related research, we also examine the decision making of successful professionals and entrepreneurs, aiming to translate and test basic decision making concepts in a more experienced population (as compared to students).
Another research stream in my Lab examines the impact of culture on human behavior, especially when people need to navigate different cultures.
Finally, another big sub-group in my Lab studies the interaction of the built environment with human behavior and especially cognition. This research is supported by a multi-million funding stream from MND.
My aim is to uncover, explain, predict and improve human decision making. Methodologically, we employ behavioural methods derived from psychology; computational methods stemming from game theory, learning theory and microeconomics; and biological measurements such as human neuroimaging using functional Magnetic Resonance Imaging (fMRI).
Research Interest in the Neuroscience of Learning and Education
I am interested in the following questions:
1. The impact of built environment and architecture on learning: how can we built an environment that facilitates learning using neuroscience principles? Examples could range from basic aspects such as space, light, color, sounds to more complex aspects such as chronobiology, social settings and competition.
2. Basic mechanisms of reinforcement learning and how they are translated to real-life skills.
3. Adult learning – especially professionals and entrepreneurs aged between 25-50 years old. This part of the population is the most financially active and yet we hardly know the exact changes and mechanisms that characterize this age group.
4. Cross-cultural differences; the mixed-culture classroom.