Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models

被引：12

作者：

Hsiao, Janet H. ^{[1
,2
,3
]}

An, Jeehye ^{[1
]}

Hui, Veronica Kit Sum ^{[1
]}

Zheng, Yueyuan ^{[1
]}

Chan, Antoni B. ^{[4
]}

机构：

[1] Univ Hong Kong, Dept Psychol, Hong Kong, Peoples R China

[2] Univ Hong Kong, State Key Lab Brain & Cognit Sci, Hong Kong, Peoples R China

[3] Univ Hong Kong, Inst Data Sci, Hong Kong, Peoples R China

[4] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China

来源：

NPJ SCIENCE OF LEARNING | 2022年 / 7卷 / 01期

关键词：

INDIVIDUAL-DIFFERENCES; SOCIAL SITUATIONS; PATTERNS; CHILDREN; IDENTIFICATION; PERCEPTION; CONFIDENCE; ATTENTION; ADULTS;

D O I：

10.1038/s41539-022-00139-6

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

Greater eyes-focused eye movement pattern during face recognition is associated with better performance in adults but not in children. We test the hypothesis that higher eye movement consistency across trials, instead of a greater eyes-focused pattern, predicts better performance in children since it reflects capacity in developing visual routines. We first simulated visual routine development through combining deep neural network and hidden Markov model that jointly learn perceptual representations and eye movement strategies for face recognition. The model accounted for the advantage of eyes-focused pattern in adults, and predicted that in children (partially trained models) consistency but not pattern of eye movements predicted recognition performance. This result was then verified with data from typically developing children. In addition, lower eye movement consistency in children was associated with autism diagnosis, particularly autistic traits in social skills. Thus, children's face recognition involves visual routine development through social exposure, indexed by eye movement consistency.

引用

页数：13