Neurocomputational Foundations of Simple Choice Self-Control, and Altruism; Applications of Neuroeconomics
Antonio Rangel studies the computational and neurobiological basis of human decision making. He uses a variety of tools from neuroscience, economics, psychology, and computer science, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), transcranial magnetic stimulation (TMS), single-unit recordings in human patients, eye-tracking, and computational modeling.
Those approaches are allowing Rangel and his research collaborators to pursue several avenues of inquiry. They are trying to understand how the brain assigns value to various options when faced with a choice, identifying the regions of the brain that encode those decisions. Eye-tracking experiments are revealing how the brain compares those assigned values to arrive at a decision. The researchers are also studying how the process of valuation changes when people exert control over their decisions—for example, when people try to exercise self-control in choosing to eat broccoli instead of chocolate cake. They are probing how such value is computed in social decision-making situations, such as when choosing to donate to charitable organizations and making decisions concerning moral behavior. Another question concerns how neural computations differ when making decisions for oneself versus for others. Rangel's lab is also interested in how neuroeconomics can be applied to design solutions to real-world problems at both the individual and societal level.
Prior to his time at Caltech, Rangel was on the faculty at Stanford University from 1998 to 2006. He was also a Faculty Research Fellow at the National Bureau of Economic Research between 1999 and 2007. He has served as the president of the Society for Neuroeconomics (2009–2010) and was a National Fellow at the Hoover Institution (2000–2001). He has received a Mentor Recognition Award from the University of California, San Diego (2005), a Harvard University Certificate of Distinction in Teaching (1997), and the Allyn Young Prize for Excellence in Teaching from Harvard (1997).
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