Study shows how your brain reacts through different emotions
A recent study has successfully predicted human emotional responses to various scenes using brain imaging and computer modeling. Conducted by neuroscientists from the University of California, Berkeley, Trinity College Dublin, and Google, the research determines not only whether a reaction was positive, negative, or neutral but also its intensity. Sonia Bishop, the study's senior author and an adjunct associate professor of neuroscience at UC Berkeley stated that this research aids in understanding how the brain represents complex emotional natural stimuli.
Study's potential impact on Autism Spectrum Disorder research
Bishop, who is also the newly appointed chair of psychology at Trinity College Dublin, noted that the simple tasks used in the study could facilitate research into autism spectrum disorder. This would be achieved by examining how individuals process everyday emotional stimuli. She emphasized the importance of recognizing and responding appropriately to emotionally charged situations for all species. Understanding how the brain enables nuanced responses to these situations has long been a topic of interest, Bishop explained.
Unraveling the brain's emotional response mechanism
The study, led by former UC Berkeley doctoral student Samy Abdel-Ghaffar, now at Google, involved showing human volunteers various natural images designed to evoke an emotional response. These images ranged from a baby's face to a snarling dog or a person vomiting. The participants' 3D brain activity was measured using functional magnetic resonance imaging (fMRI), and they were asked to rate each image as positive, negative or neutral and report their degree of emotional arousal.
Brain activity analysis reveals emotional response patterns
Analysis of the study revealed that regions of the occipital temporal cortex in the back of the brain are tuned to represent both the type of stimulus and its emotional characteristics. For instance, positive high-arousal faces were represented in slightly different regions than negative high-arousal faces or neutral low-arousal faces. Abdel-Ghaffar used machine learning to predict responses of a second group of volunteers based on these stable tuning patterns in the occipital temporal cortex.
Brain activity better predictor of emotional responses
Abdel-Ghaffar found that analyzing brain activity was a better predictor of participants' reactions than a machine learning assessment of the emotional aspects. Bishop concluded that this suggests the brain chooses which information is important to represent and holds stable representations of sub-categories of stimuli that integrate affective information. She also noted that the simple paradigm used in this study could be beneficial for future research into how individuals with various neurological and psychiatric conditions process emotional stimuli.
