The Role of the Caregiver’s Responsiveness in Affect-Grounded Language Learning by a Robot: Architecture and First Experiments

Abstract

Most computational models of language development adopt a passive-learner view on language learning, and disregard the important role that motivation and affect play in the development of communication. In this paper, we present a motivation-grounded, active learning robot model of language acquisition that relies on social interaction with a caregiver. The robot learns multiple associations—between words and internal states, and between the latter and perceived objects–allowing it to have a “meaning potential” of the acquired language, which is in line with the functionalist view of language theory. We evaluate the model experimentally in different environments and with different levels of caregiver’s responsiveness to study the impact of external factors on language acquisition.