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Emotion regulation can be characterized by different activities that attempt to alter an emotional response, whether behavioral, physiological or neurological. The two most widely adopted strategies, cognitive reappraisal and expressive suppression are explored in this study, specifically in the context of disgust. Study participants (N = 21) experienced disgust via video exposure, and were instructed to either regulate their emotions or express them freely. If regulating, they were required to either cognitively reappraise or suppress their emotional experiences while viewing the videos. Video recordings of the participants' faces were taken during the experiment and electrocardiogram (ECG), electromyography (EMG), and galvanic skin response (GSR) readings were also collected for further analysis. We compared the participants behavioral (facial musculature movements) and physiological (GSR and heart rate) responses as they aimed to alter their emotional responses and computationally determined that when responding to disgust stimuli, the signals recorded during suppression and free expression were very similar, whereas those recorded during cognitive reappraisal were significantly different. Thus, in the context of this study, from a signal analysis perspective, we conclude that emotion regulation via cognitive reappraisal significantly alters participants' physiological responses to disgust, unlike regulation via suppression. 

The purpose of this work is to determine if the ability to interpret a convolutional neural network (CNN) architecture can enhance human performance, pertaining to face recognition. We are interested in distinguishing between the faces of two similar-looking actresses of Indian origin, who have only a few discriminating features. This recognition task proved challenging for humans who were not previously familiar with the actresses (novices) as they performed only just better than random. When asked to perform the same task, humans who were more familiar with the actresses (experts) performed significantly better. We attempted the same task with a Siamese CNN which performed as well as the experts. We therefore became interested in applying any new knowledge obtained from the CNN to aid in improving the distinguishing abilities of other novices. This was accomplished by generating activation maps from the CNN. The maps showed what parts of the input face images created the highest activations in the last convolutional layer of the network. Using "fooling'" techniques, we also investigated what spatial locations on the face were most responsible for confusing one actress for the other. Empirically, the cheekbones and foreheads were determined to be the strongest differentiating features between the actresses. By providing this information verbally to a new set of novices, we successfully raised the human recognition rates by 11%. For this work, we therefore successfully increased human understanding pertaining to facial recognition via post-hoc interpretability of a CNN.