Partner Modeling is Mutual

Collaborative learning has been hypothesized to be related to the cognitive effort engaged by co-learners to build a shared understanding. The process of constructing this shared understanding requires each team member to build some kind of representation of the behavior, beliefs, knowledge or intentions of other group members. This contribution reports interesting findings regarding to the process of modeling each other. In two empirical studies, we measured the accuracy of the mutual model, i.e. the difference between what A believes B knows, has done or intends to do and what B actually knows, has done or intends to do. In both studies, we found a significant correlation between the accuracy of A's model of B and the accuracy of B's model of A. This leads us to think that the process of modeling one's partners does not simply reflect individual attitudes or skills but emerges as a property of group interactions. We describe on-going studies that explore these preliminary results

Mutual Modelling in Robotics: Inspirations for the Next Steps

Mutual modelling, the reciprocal ability to establish a mental model of the other, plays a fundamental role in human interactions. This complex cognitive skill is however difficult to fully apprehend as it encompasses multiple neuronal, psychological and social mechanisms that are generally not easily turned into computational models suitable for robots. This article presents several perspectives on mutual modelling from a range of disciplines, and reflects on how these perspectives can be beneficial to the advancement of social cognition in robotics. We gather here both basic tools (concepts, formalisms, models) and exemplary experimental settings and methods that are of relevance to robotics. This contribution is expected to consolidate the corpus of knowledge readily available to human-robot interaction research, and to foster interest for this fundamentally cross-disciplinary field