Browsing by Subject "Humanoid robots"

Now showing 1 - 2 of 2

Open Access
Do robots distract us as much as humans? The effect of human-like appearance and perceptual load
(IEEE Computer Society, 2020) Ürgen, Burcu A.; Yılmaz, Selin; Güneysu, İlayda; Cerrahoğlu, Begüm; Dinçer, Ece
Attention is an important mechanism for solving certain tasks, but our environment can distract us via irrelevant information. As robots increasingly become part of our lives, one important question is whether they could distract us as much as humans do, and if so to what extent. To address this question, we conducted a study in which subjects were engaged in a central letter detection task. The task irrelevant distractors were pictures of three agents; a mechanical robot, a human-like robot, and a real human. We also manipulated the perceptual load to investigate whether the demands of the task influence how much these agents distract us. Our results show that robots distract people as much as humans, as demonstrated by significant increase in reaction times and decrease in task accuracy in the presence of agent distractors as compared to the situation when there was no distractor. However, we found that the task difficulty interacted with the humanlikeness of the distractor agent. When the task was less demanding, the agent that distracted most was the most humanlike agent, whereas when the task was more demanding, the least human-like agent distracted the most. These results not only provide insights about how to design humanoid robots but also sets as a great example of a fruitful collaboration between humanrobot interaction and cognitive sciences.
Open Access
Does the appearance of an agent affect how we perceive his/her voice? Audio-visual predictive processes in human-robot interaction
(IEEE Computer Society, 2020) Sarıgül, Büşra; Saltık, İmge; Hokelek, Batuhan; Ürgen, Burcu A.
Robots increasingly become part of our lives. How we perceive and predict their behavior has been an important issue in HRI. To address this issue, we adapted a well-established prediction paradigm from cognitive science for HRI. Participants listened a greeting phrase that sounds either human-like or robotic. They indicated whether the voice belongs to a human or a robot as fast as possible with a key press. Each voice was preceded with a human or robot image (a human-like robot or a mechanical robot) to cue the participant about the upcoming voice. The image was either congruent or incongruent with the sound stimulus. Our findings show that people reacted faster to robotic sounds in congruent trials than incongruent trials, suggesting the role of predictive processes in robot perception. In sum, our study provides insights about how robots should be designed, and suggests that designing robots that do not violate our expectations may result in a more efficient interaction between humans and robots.