Human tutors intuitively reduce complexity in socially guided embodied grammar learning

The current investigation addresses whether the socially guided machine learning paradigm can be extended to a new domain, embodied grammar learning. Experimental results show that naive users indeed reduce the complexity of linguistic utterances in tutoring sessions for a simulated robot, even though their own knowledge of the subject area is only tacit. These findings have implications for the usability of robots as `teachable agents´, as well as for automatic language learning from interaction.