Social Transmission of Information through Virtual Robotic Agents

Social learning includes simple or complex social mechanisms that allow us to understand cooperation and communication in animals, giving them better chances to survive for longer and thrive as a society. Specifically, certain types of social learning such as observational conditioning and stimulus enhancement have been investigated in the context of social information spread between primates. However, not many studies have utilized such social learning mechanisms to study social learning between humans and artificial agents. In the work described here, we seek to understand if and how simple social learning mechanisms can influence human participants using an online game platform with an immersive first person experience built through Unity. Specifically, we designed a study inspired by experiments in behavioural sciences to investigate whether and to what extent, a robotic agent can influence human's actions. The study compared two conditions in which the robot showed body-language based emotions in a positive or negative manner that could enhance certain stimuli for the human participants and influence their decision making. From this, we wanted to understand whether these effects are socially learned by humans. Objective (position of player in-game) and Subjective (questionnaires) measures were recorded, and markers using the objective data suggest successful social transmission of information. We believe this approach can make a novel contribution to the field of Human Interaction with Artificial Agents.