This image shows the average positions of brain regions used to identify emotional states.
Credit: Carnegie Mellon University
For the first time, scientists at Carnegie Mellon University have identified which emotion a person is experiencing based on brain activity.
The study, which will be published in the June 19 issue of PLOS ONE, combines functional magnetic resonance imaging (fMRI) and machine learning to measure brain signals to accurately read emotions in individuals.
Led by researchers in CMU's Dietrich College of Humanities and Social Sciences, the findings illustrate how the brain categorizes feelings, 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 activity builds on previous discoveries by CMU's Marcel Just and Tom M. Mitchell, which used similar techniques to create a computational model that identifies individuals' thoughts of concrete objects, often dubbed "mind reading."
"This research introduces a new method with potential to identify emotions without relying on people's ability to self-report," said Karim Kassam, assistant professor of social and decision sciences and lead author of the study.
"It could be used to assess an individual's emotional response to almost any kind of stimulus, for example, a flag, a brand name or a political candidate."
One challenge for the research team was find a way to repeatedly and reliably evoke different emotional states from the participants.
Traditional approaches, such as showing subjects emotion-inducing film clips, would likely have been unsuccessful because the impact of film clips diminishes with repeated display.
The researchers solved the problem by recruiting actors from CMU's School of Drama.
Read the full article here