| Title | Automatic classification of children with autism spectrum disorder by using a computerized visual-orienting task |
| Authors | He, Qiao Wang, Qiandong Wu, Yaxue Yi, Li Wei, Kunlin |
| Affiliation | Peking Univ, Acad Adv Interdisciplinary Studies, Peking Tsinghua Ctr Life Sci, Beijing, Peoples R China Beijing Normal Univ, Fac Psychol, Beijing Key Lab Appl Expt Psychol, Natl Demonstrat Ctr Expt Psychol Educ, Beijing, Peoples R China Peking Univ, Sch Psychol & Cognit Sci, Beijing 100871, Peoples R China Peking Univ, Beijing Key Lab Behav & Mental Hlth, Beijing 100871, Peoples R China |
| Issue Date | Apr-2021 |
| Publisher | PSYCH JOURNAL |
| Abstract | Early screening and diagnosis of autism spectrum disorder (ASD) primarily rely on behavioral observations by qualified clinicians whose decision process can benefit from the combination of machine learning algorithms and sensor data. We designed a computerized visual-orienting task with gaze-related or non-gaze-related directional cues, which triggered participants' gaze-following behavior. Based on their eye-movement data registered by an eye tracker, we applied the machine learning algorithms to classify high-functioning children with ASD (HFA), low-functioning children with ASD (LFA), and typically developing children (TD). We found that TD children had higher success rates in obtaining rewards than HFA children, and HFA children had higher rates than LFA children. Based on raw eye-tracking data, our machine learning algorithm could classify the three groups with an accuracy of 81.1% and relatively high sensitivity and specificity. Classification became worse if only data from the gaze or nongaze conditions were used, suggesting that "less-social" directional cues also carry useful information for distinguishing these groups. Our findings not only provide insights about visual-orienting deficits among children with ASD but also demonstrate the promise of combining classical behavioral paradigms with machine learning algorithms for aiding the screening for individuals with ASD. |
| URI | http://hdl.handle.net/20.500.11897/612429 |
| ISSN | 2046-0252 |
| DOI | 10.1002/pchj.447 |
| Indexed | SSCI |
| Appears in Collections: | 前沿交叉学科研究院 心理与认知科学学院 行为与心理健康北京市重点实验室 |
