Mirror Neurons cannot be Fooled by Artificial Voices – a study with Implications for Education using Magnetic Resonance Imaging (MRI) and Convolutional Neural Network (CNN)

Mirror neurons have a crucial role in detecting and reproducing the actions of others as if the observer himself were performing the specific action. In this paper, four different audio voice files are used to determine, in two methods, the idea that the mirror neurons can be activated only by a real human original voice with a strong emotional load. The first method is the magnetic resonance imaging (MRI), which gives information about the brain activity regarding the specific areas where the mirror neurons are located when the four different audio files are listened to by a group of ten volunteers. The second method implies a deep learning approach, using two convolutional neural network (CNNs) architectures, one used to recognize the timbre of the audio speaker and the second one to determine the level of remnant (residual) emotion in the audio files listened to by them. The four audio files used are an audio text recorded by a Romanian actress with a specific emotion, two different actresses’ voice recordings with the same text and emotion, and with very similar voice features to the main actress, and the last one is an artificially generated voice using AI algorithm. The results show a promising response from both perspectives- the hypothesis that mirror neurons can’t be fooled by an artificial voice is confirmed, and that the intensity of emotion is higher in the original voice than the two imitating voices.