External and Internal Attribution in Human-Agent Interaction: Insights from Neuroscience and Virtual Reality

Abstract

Agents are designed in the image of humans, both internally and externally. The internal systems of agents imitate the human brain, both at the levels of hardware (i.e., neuromorphic computing) and software (i.e., neural networks). Furthermore, the external appearance and behaviors of agents are designed by people and based on human data. Sometimes, these humanlike qualities of agents are purposely selected to increase their social influence over human users, and sometimes the human factors that influence perceptions of agents are hidden. Inspired by Blascovich’s “threshold of social influence’, a model designed to explain the effects of different methods of anthropomorphizing embodied agents in virtual environments, we propose a novel framework for understanding how humans’ attributions of human qualities to agents affects their social influence in human-agent interaction. The External and Internal Attributions model of social influence (EIA) builds on previous work on agent-avatars in immersive virtual reality and provides a framework to link previous social science theories to neuroscience. EIA connects external and internal attributions of agents to two brain networks related to social influence. the external perception system, and the mentalizing system. Focusing human-agent interaction research along each of the attributional dimensions of the EIA model, or at the functional integration of the two, may lead to a better understanding of the thresholds of social influence necessary for optimal human-agent interaction.