title: Co-development of manner and path concepts in language, action, and eye-gaze behavior.
abstract: In order for artificial intelligent systems to interact naturally with human users, they need to be able to learn from human instructions when actions should be imitated. Human tutoring will typically consist of action demonstrations accompanied by speech. In the following, the characteristics of human tutoring during action demonstration will be examined. A special focus will be put on the distinction between two kinds of motion events: path-oriented actions and manner-oriented actions. Such a distinction is inspired by the literature pertaining to cognitive linguistics, which indicates that the human conceptual system can distinguish these two distinct types of motion. These two kinds of actions are described in language by more path-oriented or more manner-oriented utterances. In path-oriented utterances, the source, trajectory, or goal is emphasized, whereas in manner-oriented utterances the medium, velocity, or means of motion are highlighted. We examined a video corpus of adult-child interactions comprised of three age groups of children-pre-lexical, early lexical, and lexical-and two different tasks, one emphasizing manner more strongly and one emphasizing path more strongly. We analyzed the language and motion of the caregiver and the gazing behavior of the child to highlight the differences between the tutoring and the acquisition of the manner and path concepts. The results suggest that age is an important factor in the development of these action categories. The analysis of this corpus has also been exploited to develop an intelligent robotic behavior-the tutoring spotter system-able to emulate children's behaviors in a tutoring situation, with the aim of evoking in human subjects a natural and effective behavior in teaching to a robot. The findings related to the development of manner and path concepts have been used to implement new effective feedback strategies in the tutoring spotter system, which should provide improvements in human-robot interaction.
authors.affiliation: School of Mathematical & Computer Sciences, Heriot-Watt University & Applied Informatics Group, Bielefeld University.
authors.forename: Katrin S
authors.initials: KS
authors.lastname: Lohan
authors.forename: Sascha S
authors.initials: SS
authors.lastname: Griffiths
authors.forename: Alessandra
authors.initials: A
authors.lastname: Sciutti
authors.forename: Tim C
authors.initials: TC
authors.lastname: Partmann
authors.forename: Katharina J
authors.initials: KJ
authors.lastname: Rohlfing
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D002648:Child; D005133:Eye Movements / Q000502:physiology*; D005260:Female; D006801:Humans; D007100:Imitative Behavior / Q000502:physiology; D007802:Language; D007858:Learning / Q000502:physiology*; D008297:Male; D011597:Psychomotor Performance / Q000502:physiology*; D012371:Robotics
keywords: Adult-child interaction; Human-robot interaction; Imitation; Semantics; Teachable robots; Tutoring