People tend to unconsciously attribute personality traits to all kinds of technology including robots. But what personality do they want robots to have? Previous research has found support for two contradicting theories: similarity attraction and complementary attraction. The similarity attraction theory implies that people prefer a robot with a similar personality to their own (e.g., an extroverted person prefers an extroverted robot). According to the complementary attraction theory, people prefer a robot's personality opposite to their own (e.g., extroverted people prefer an introverted robot). In contrast to both theories, we argue that what is considered an appropriate personality for a robot depends on the task context. In a 2x2 between-groups experiment (N=45), we found trends that indicated similarity attraction for extrovert participants when the robot was a tour guide and complementary attraction for introverted participants when the robot was a cleaner. These trends show that preferences for robot personalities may indeed depend on the context of the robot's role and the stereotype perceptions people hold for certain jobs. Robot behaviors likely need to be adapted not in complimentary or similarity to the users' personality but to the users' expectations about what kind of personality and behaviors are consistent with such a task or role.
In this paper we present a lab study on robot abuse by children. 61 Japanese children of ages 7-9 interacted individually with Robovie, a social robot, in a context that promoted children's free disruptive behaviors towards the robot. We compared the robot's use of an adaptation of a parental discipline strategy, the so-called love-withdrawal technique, to a similar set of robot behaviors that lacked any specific strategy (neutral condition). The main insight we gained was that perhaps we should better not focus on general robot behaviors to try to fit all children, but rather, we should adapt the robot behaviors to children's individual differences. For instance, we found that the lovewithdrawal-based strategy was significantly more effective in children of age 8-9 than on children of 7.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.