The influence of internal and external cues on the joint attention of children with autism spectrum disorder

ABSTRACT

Issues with joint attention, especially avoiding eye contact with others, are considered one of the core defects found in the early stages of children with autism spectrum disorder (ASD). This study is the first to use pupil reflection technology, which can be used to present an image of the participants within the virtual character’s eyes on the screen, thereby arousing their interest and promoting eye contact with the character during the interaction. The study evaluated the efficacy of pupil reflection as an internal cue to improve a participant’s joint attention. A total of 63 children with ASD were asked to participate in the study. The stimuli materials were combined with internal cues and external cues. We found that, first, both the internal and external cues attracted the attention of the ASD participants and can play a prompting role in the intervention training. Second, the internal cues appeared to successfully simulate the real world in that the ASD participants paid more attention to the eyes. It can be used to improve eye contact with others. Third, there was no obvious interaction between the internal and external cues. This demonstrated that pupil reflection had a positive impact on the visual processing and joint attention of children with ASD.

KEYWORDS:

• Internal and external cues
• visual contact
• children with ASD
• pupil reflection
• joint attention

Acknowledgments

We are grateful to Qihui and Linjie special institutions in Wuhan and the research participants in the two institutions. We also thank our colleagues at the National Engineering Research Center for E-Learning of the Central China Normal University who participated in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation [grant number 61977027], the Science & Technology Major Project of Hubei Province (Next-Generation AI Technologies, No. 2021BEA159).

Notes on contributors

Chaoying He

Chaoying He is a Ph.D student in Department of Special Education at National Changhua University of Education. Her research focuses on the intervention of children with ASD.

Jingying Chen

Jingying Chen is a professor in National Engineering Research Center For E-Learning at Central China Normal University. Her interests focus on technology enhanced learning for special children, computer vision, human–computer interaction, artificial intelligence.

Kun Zhang

Kun Zhang is an associate professor in National Engineering Research Center For E-Learning at Central China Normal University. His research focuses special education informatization, intelligence assessment and intervention for children with autism.