For the first time, scientists at Carnegie Mellon University have identified which emotion a person is experiencing based on brain activation.
The study, published in the June 19, 2013 issue of PLOS ONE, combines functional magnet resonance imaging (fMRI) and machine learning to measure brain signals to accurately read emotions in individuals. Led by researchers within CMU's Dietrich College of Humanities and Social Sciences, the findings illustrate how the brain categorizes emotions, giving researchers the first reliable process to analyze emotions. Until now, research on emotions has been long stymied by the lack of reliable methods to evaluate them, mostly because people are often reluctant to honestly report their feelings. Further complicating matters is that many emotional responses may not be consciously experienced.
Identifying emotions based on neural activation builds on previous discoveries by CMU's Marcel Just and Tom M. Mitchell that used similar techniques to create a computational model that identifies individuals' thoughts of concrete objects, often dubbed "mind reading."
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