AUTHOR=Kelley Megan S. , Noah J. Adam , Zhang Xian , Scassellati Brian , Hirsch Joy 

TITLE=Comparison of Human Social Brain Activity During Eye-Contact With Another Human and a Humanoid Robot

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 7 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2020.599581

DOI=10.3389/frobt.2020.599581

ISSN=2296-9144

ABSTRACT=Robot design to simulate interpersonal social interaction is an active area of research with applications in therapy and companionship. Neural responses to eye-to-eye contact in humans have recently been employed to determine the neural systems that are active during social interactions. Whether eye-contact with a social robot engages the same neural system remains to be seen. Here, we employ a similar approach to compare human-human and human-robot social interactions.  We assume that if human-human and human-robot eye-contact elicit similar neural activity in the human, then the perceptual and cognitive processing is also the same for human and robot. That is, the robot is processed similar to the human. However, if neural effects are different, then perceptual and cognitive processing is assumed to be different. In this study neural activity was compared for human-to-human and human-to-robot conditions using near infrared spectroscopy for neural imaging and a robot (Maki) with eyes that blink and move right and left. Eye-contact was confirmed by eye-tracking for both conditions.  Increased neural activity was observed in human social systems including the right temporal parietal junction and the dorsolateral prefrontal cortex during human-human eye contact but not human-robot eye-contact.   This finding demonstrates that this robot design does not comparably engage the social processing network despite the comparable eye-to-eye contact behavior. These findings establish a foundation for future research to determine how elements of robot design impact human social processing and may offer a method for capturing difficult to quantify components of human-robot interaction, such as social engagement.