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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2010-07-08
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en
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Abstract
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.
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Faculteit der Sociale Wetenschappen