Joint Attention Behavior for Children with Autism Spectrum Disorder (ASD) Interacted with Social Robots
By utilizing a commercial humanoid robot NAO, this study compared the difference of joint attention behaviors induced by both the human and robot stimuli. Based on the analysis of fixation time, gaze transitions and LCS algorithm, the study could reveal both the static and dynamic process underlying the joint attention ability and compare the difference between the ASD and typically developing (TD) children.
15 ASD (mean age: 4.96±1.10) and 15 age-matched TD children (4.53±0.90) participated in this eye- tracking study. Video stimuli were used to induce joint attention behaviors of participants. Each video consisted of an agent (human or robot) sitting behind the table together with three objects on the left, middle and right side of the table. The agent would turn the head to one of the three truck toys, and the eye movement was recorded by the Tobii X3-120 eye-tracker system. Besides the traditional fixation time and gaze transition analysis, we introduced an LCS algorithm to measure the complexity of transition distributions between area of interests (AOIs).
Data analysis showed that the TD group fixated longer on the agent face (p=0.002) than the ASD group, and the ASD group gazed on the frame (p=0.045) and non-target (p=0.036) longer than the TD group. All participants spent more time on face area in the “Robot” condition (p=0.038), and more on target (p=0.004) and non-target in the “Human” condition (p=0.045). However, we did not found any significant difference in gaze transition analysis between the ASD and TD group. The LCS analysis revealed a significant effect of group (F(1, 28)=11.18, p=0.002, partial η2=0.898), indicating that the TD participants have significantly higher LCS scores than the ASD. It implied the TD could better follow the logic of the video.
This research showed a complex joint attention profile of the participants with ASD. Compared to the human agent, the robot attracted more interests of all participants, but was not able to facilitate the gaze transitions towards the targets or following the stimuli’s logic. It implies a negative impact on joint attention behavior when children were interacted with social robots.