@article{PerugiaPaetzelPruesmannAlanenpaeaeetal.2021,
  author    = {Perugia, Giulia and Paetzel-Pr{\"u}smann, Maike and Alanenp{\"a}{\"a}, Madelene and Castellano, Ginevra},
  title     = {I can see it in your eyes},
  series = {Frontiers in robotics and AI},
  volume    = {8},
  journal   = {Frontiers in robotics and AI},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2296-9144},
  doi       = {10.3389/frobt.2021.645956},
  pages     = {18},
  year      = {2021},
  abstract  = {Over the past years, extensive research has been dedicated to developing robust platforms and data-driven dialog models to support long-term human-robot interactions. However, little is known about how people's perception of robots and engagement with them develop over time and how these can be accurately assessed through implicit and continuous measurement techniques. In this paper, we explore this by involving participants in three interaction sessions with multiple days of zero exposure in between. Each session consists of a joint task with a robot as well as two short social chats with it before and after the task. We measure participants' gaze patterns with a wearable eye-tracker and gauge their perception of the robot and engagement with it and the joint task using questionnaires. Results disclose that aversion of gaze in a social chat is an indicator of a robot's uncanniness and that the more people gaze at the robot in a joint task, the worse they perform. In contrast with most HRI literature, our results show that gaze toward an object of shared attention, rather than gaze toward a robotic partner, is the most meaningful predictor of engagement in a joint task. Furthermore, the analyses of gaze patterns in repeated interactions disclose that people's mutual gaze in a social chat develops congruently with their perceptions of the robot over time. These are key findings for the HRI community as they entail that gaze behavior can be used as an implicit measure of people's perception of robots in a social chat and of their engagement and task performance in a joint task.},
  language  = {en}
}
@article{PaetzelPruesmannPerugiaCastellano2021,
  author    = {Paetzel-Pr{\"u}smann, Maike and Perugia, Giulia and Castellano, Ginevra},
  title     = {The influence of robot personality on the development of uncanny feelings},
  series = {Computers in human behavior},
  volume    = {120},
  journal   = {Computers in human behavior},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0747-5632},
  doi       = {10.1016/j.chb.2021.106756},
  pages     = {17},
  year      = {2021},
  abstract  = {Empirical investigations on the uncanny valley have almost solely focused on the analysis of people?s noninteractive perception of a robot at first sight. Recent studies suggest, however, that these uncanny first impressions may be significantly altered over an interaction. What is yet to discover is whether certain interaction patterns can lead to a faster decline in uncanny feelings. In this paper, we present a study in which participants with limited expertise in Computer Science played a collaborative geography game with a Furhat robot. During the game, Furhat displayed one of two personalities, which corresponded to two different interaction strategies. The robot was either optimistic and encouraging, or impatient and provocative. We performed the study in a science museum and recruited participants among the visitors. Our findings suggest that a robot that is rated high on agreeableness, emotional stability, and conscientiousness can indeed weaken uncanny feelings. This study has important implications for human-robot interaction design as it further highlights that a first impression, merely based on a robot?s appearance, is not indicative of the affinity people might develop towards it throughout an interaction. We thus argue that future work should emphasize investigations on exact interaction patterns that can help to overcome uncanny feelings.},
  language  = {en}
}