Human-Robot Trust: is motion fluency an effective behavioral style for regulating robot trustworthiness

dc.contributor.advisorBrule, R. van den
dc.contributor.advisorHaselager, W.F.G.
dc.contributor.authorLigthart, M.E.U.
dc.description.abstractFinding good features to regulate robot trustworthiness will optimize the usage of robots. In previous research done by van den Brule et al. (submitted) motion fluency (smooth robot motions versus trembling robot motions) is studied. After a round of movie experiments a main effect is found, while after a round of Immersive Virtual Environment Technology (IVET) experiments no effect is observed. In this research, I explore the question whether the length of the task contributes to the presence of a fluency effect on the trustworthiness of a robot. More specifically it is investigated whether an effect of motion fluency is present in a short version of a task and disappears when the task is longer. The task in the virtual reality experiment consisted out of two actors: a (human) participant and a virtual robot. Both perform the Van Halen task: an actor has to pick brown balls from a conveyor belt and let other colored ball pass. The goal of the participant is to maximize the sum of their own score and the robot’s score. By correcting the robot when it makes a mistake a penalty is prevented. Results showed there is no significant difference between the measured trustworthiness in any of the four conditions. This might be because there is no effect of motion fluency. The meta-study of Hancock et al. (2011) indicates that no effect is very plausible. Also a null effect can be caused by the cognitive load of the task. A different explanation is the influence of interfering factors. An effect of motion fluency might be masked or countered by the recency effect (Desai et al., 2013), the assimilation effect (Herr et al., 1983) or the use of virtual reality (Bainbridge et al., 2008). All possibilities require further research.en_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.titleHuman-Robot Trust: is motion fluency an effective behavioral style for regulating robot trustworthinessen_US
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
544.37 KB
Adobe Portable Document Format