Comparing a mentalist and an interactionist approach for trust analysis in Human-Robot Interaction

Trust is an important aspect of a human-robot interaction (HRI) as it mitigates the performance of many activities. Users' trust may be impacted when robots make mistakes. To be able to properly time trust-reparation actions, robots should detect trust variations during the interaction. There are very few computational models of trust for such a task. The existing ones relied on either Psychological or Sociological theories that gave place to different definitions and analysis tools. We can distinguish two main approaches in the trust literature: the mentalist and the interactionist one. In this paper, we compare both approaches for trust detection, and explore how the adoption of two different assessment tools on an HRI dataset may lead to different results. We identify criteria that set them apart, and provide guidelines on the possibilities that each approach offers depending on the target computational model of trust.