Contingency scaffolds language learning

Abstract

In human robot interaction the question how to communicate is an important one. The answer to this question can be approached through several perspectives. One approach to study the best way how a robot should behave in an interaction with a human is by providing a consistent robotic behavior. From this we can gain insights into what parameters are triggering what responsive behavior in an user. This method allows us as roboticists to investigate how we can elicit a specific behavior in users in order to facilitate robot's learning. In previous studies, we have shown how responsive eye gaze and feedback on a looming detection is modifying the human tutoring behavior [1]. In this paper, we present a study was carried out within the ITALK project. The study is targeting, how we can tune robotic feedback strategies of the iCub robot to evoke a tutoring behavior in a human tutor that is supporting a language acquisition system. We used a longitudinal approach for the study to also verify the verbal feedback given by the robot.