No abstract

It is widely recognized that robot-based interventions for autism spectrum disorders (ASD) hold promise, but the question remains as to whether social humanoid robots could facilitate joint attention performance in children with ASD. In this study, responsive joint attention was measured under two conditions in which different agents, a human and a robot, initiated joint attention via video. The participants were 15 children with ASD (mean age: 4.96 ± 1.10 years) and 15 typically developing (TD) children (mean age: 4.53 ± 0.90 years). In addition to analyses of fixation time and gaze transitions, a longest common subsequence approach (LCS) was employed to compare participants’ eye movements to a predefined logical reference sequence. The fixation of TD toward agent’s face was earlier and longer than children with ASD. Moreover, TD showed a greater number of gaze transitions between agent’s face and target, and higher LCS scores than children with ASD. Both groups showed more interests in the robot’s face, but the robot induced a lower proportion of fixation time on the target. Meanwhile participants showed similar gaze transitions and LCS results in both conditions, suggesting that they could follow the logic of the joint attention task induced by the robot as well as human. We have discussed the implications for the effects and applications of social humanoid robots in joint attention interventions.

This study aims to probe how children with and without autism spectrum disorders (ASD) attribute false belief to a social robot and predict its action accordingly. Twenty 5- to 7-year-old children with ASD and 20 age- and IQ-matched typically developing (TD) children participated in two false belief tasks adapted for robot settings (change-of-location task and the unexpected-contents task). The results showed that most TD children are capable of attributing false belief to the social robot, that is, they could infer higher level mental states in robots, which extends our understanding in TD children’s perception and cognition on social robots. Conversely, children with ASD still show difficulty in interpreting robots’ mental states compared to their TD peers, which would greatly interfere with their interactions and communications with social robots and might impact on efficiency of robot-based intervention and education approaches. This group difference in attributing false belief to social robots could not be explained by the different perception and categorization of the robot. Our study implies that although children with ASD appear to be highly attracted by social robots, they still have difficulty in understanding mental states when socially interacting with robots, which should be taken into consideration when designing the robot-based intervention approach targeting to improve social behaviors of ASD.