A Shaky Foundation for Trust: Effects of task performance and movement style on trust and behavior in social Human-Robot interaction

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Consumer robots are slowly beginning to emerge as household appliances. As these robots become more sophisticated and are treated more as an addition to the family, it is important to equip them with the social skills needed to be accepted and trusted in a household environment. The Trust Me project aims to develop a social robot which can calibrate its trustworthiness based on the behavior of the person it is interacting with. Trust is an important factor in social interaction, as it is the attitude towards an agent in which it will help achieve an individual's goal. Task performance of the agent is seen as the most objective way to estimate its trustworthiness, but is difficult to observe before interaction takes place. Therefore, the agent's appearance and behavioral style (e.g. movements, display envelopes) is commonly used as a way to assess its trustworthiness. However, the agent's task performance and appearance do not have to correlate. The relationships between trust, behavior, task performance and appearance and behavioral style are not well understood. An experiment was performed in an Immersive Virtual Environment (IVE) in which participants had to perform a social decision task with a robot avatar. The task performance and movement style of the robot avatar were manipulated. The robot could have either a bad or good task performance and a shaky or smooth movement style. Results show that robots with a better task performance are generally trusted more than robots with bad task performance. At the same time, robots which have their level of movement style aligned with their level of task performance are trusted more than robots which have inconsistent levels of movement style and task performance. These results suggest that while it is important for a social robot to perform well on a task in order to be trusted, it is also important to show uncertainty by altering its behavioral style when the robot cannot perform a task satisfactorily. The fit effect of trust is also found in participant reaction times, distances kept from the robot, and movement speeds. The trust variables mediate these behavioral metrics. In future research on-line measurement of these metrics can be used by the robot to estimate its own trustworthiness. Potentially, the robot can utilize this information to alter its behavioral style to evoke the right amount of trust from its user.
Faculteit der Sociale Wetenschappen