Anytime Trust Rating Dynamics in a Human-Robot Interaction Task
<|reference_start|>Anytime Trust Rating Dynamics in a Human-Robot Interaction Task: Objective We model factors contributing to rating timing for a single-dimensional, any-time trust in robotics measure.   Background Many studies view trust as a slow-changing value after subjects complete a trial or at regular intervals. Trust is a multifaceted concept that can be measured simultaneously with a human-robot interaction.   Method 65 subjects commanded a remote robot arm in a simulated space station. The robot picked and placed stowage commanded by the subject, but the robot's performance varied from trial to trial. Subjects rated their trust on a non-obtrusive trust slider at any time throughout the experiment.   Results A Cox Proportional Hazards Model described the time it took subjects to rate their trust in the robot. A retrospective survey indicated that subjects based their trust on the robot's performance or outcome of the task. Strong covariates representing the task's state reflected this in the model.   Conclusion Trust and robot task performance contributed little to the timing of the trust rating. The subjects' exit survey responses aligned with the assumption that the robot's task progress was the main reason for the timing of their trust rating.   Application Measuring trust in a human-robot interaction task should take as little attention away from the task as possible. This trust rating technique lays the groundwork for single-dimensional trust queries that probe estimated human action.<|reference_end|>
@article{dekarske2024anytime,
    title={Anytime Trust Rating Dynamics in a Human-Robot Interaction Task},
    author={Jason Dekarske, Gregory Bales, Zhaodan Kong, Sanjay Joshi},
    journal={arXiv preprint arXiv:2408.00238},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00238},
    primaryClass={cs.HC}
}