A Pilot Study on Facial Expression Recognition by Autistic Children of a Social Robot

This study investigates the accuracy of recognition of four basic facial expressions (Angry, Happy, Sad, and Surprised) on a social assistive robot's face by children with and without autism spectrum disorder (ASD). Human-robot interaction experiments (HRI) were conducted with Milo, an expressive social robot. A total of 60 children aged 8–12 took part in the study: 41 non-autistic children and 19 children who met DSM-5 diagnostic criteria for ASD. Milo displayed four basic facial expressions in a randomized order across two consecutive attempts. Participants identified the expressions, allowing for a quantitative analysis of potential differences between groups.The proportion of correct answers was used for validity and reliability ratings. A logistic mixed-effect regression model was employed to analyze the relationship between accuracy, demographic variables, emotion type, and ASD status. Our results suggest that although overall differences in facial expression recognition between autistic and non-autistic participants may be subtle, more detailed emotion-specific analyses are necessary to identify and characterize these differences. Results indicated that overall group differences in labeling accuracy were modest, whereas emotion-specific analyses identified a significant group difference for Milo's Angry facial expression. The number of repeated attempts showed a marginally significant effect in overall accuracy in both groups, with the recognition accuracy decreasing on the second attempt, particularly among autistic children. However, this decline was sensitive to the treatment of “None of the above” responses. Therefore, the attempt effect should be interpreted cautiously. These findings highlight the importance of considering specific emotions, task structure, and response options when evaluating children's interpretations of robot-generated facial expressions.