Modified items

Are children and adults sensitive to gaps in their knowledge, and do they actively elicit information to resolve such knowledge gaps? In a cross-situational word learning task, we asked 5-year-olds, 6- to 9-year-olds and adults to estimate their knowledge of newly learned word–object associations. We then examined whether participants preferentially sampled objects they reported not knowing the label in order to hear their labels again. We also examined whether such uncertainty-driven sampling behaviour led to improved learning. We found that all age groups were sensitive to gaps in their knowledge of the word–object associations, i.e. were more likely to say they had correctly indicated the label of an object when they were correct, relative to when they were incorrect. Furthermore, 6- to 9-year-olds and adults—but not 5-year-olds—were more likely to sample objects whose labels they reported not knowing. In other words, older children and adults displayed sampling behaviour directed at reducing knowledge gaps and uncertainty, while younger children did not. However, participants who displayed more uncertainty-driven sampling behaviour were not more accurate at test. Our findings underscore the role of uncertainty in driving 6- to 9-year-olds’ and adults’ sampling behaviour and speak to the mechanisms underlying previously reported performance boosts in active learning.

Peter Zezula, IT-Team

The current study examines the extent to which children’s interests and caregivers’ sensitivity to their children’s interests are associated with the quality of caregiver-child interaction, and subsequent learning. 81 caregiver-child dyads (24-30-month-old children) completed an online shared book-reading task where caregivers and children read two e-books with pictures and descriptions of objects from different categories – one previously determined to be of low and one of high interest to the child (with one novel word-object mapping introduced in each book). We also obtained separate behavioural indices of children’s interests and children’s later recognition of newly-introduced word-object mappings. Our findings highlight that the quality of caregiver-child interaction is predicted by children’s interests and caregivers’ perception of children’s interests, although we find only limited overlap between our behavioural indices of children’s interests and caregiver perception of children’s interests. Neither of these factors predicted later novel word recognition. Thus, while the dynamics between higher quality of caregiver-child interaction, children’s interests and learning remain inconclusive, caregivers and children appear to be more attentive, enthusiastic and engaged reading about topics that (caregivers believe) interest the child. Furthermore, learning in itself seems to be successful, regardless of factors involved, through the mere task of shared book-reading.

Language is learned through social interactions, in which gaze has a special role because it can be used to guide the attention and reference objects easily. Children, starting from very early ages, are also very good at utilizing gaze to map labels to referenced objects. To achieve language teaching robots, we need to understand how these functions of gaze can be implemented most efficiently. To this aim, we allowed children to interact with a social robot to learn the labels of several objects in a naturalistic setting. In some trials the child guided the gaze and chose the object to be learned while the robot was following and in the others they changed the roles and robot guided the gaze and decided on the object to be learned. We measured how much children actually followed the robot’s gaze and how many words they learned in these two conditions, referred to as active and passive learning conditions, respectively. The results indicate that although children followed the robot’s gaze and learned words successfully, there were no meaningful differences inword learning between the two conditions. The rate of gaze following and time spent looking at the robot did not influence word learning, either. The implications of these results for use of robots in educational settings are further discussed.