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

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 i...

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Bibliographic Details
Published in:NPJ science of learning 2022-10, Vol.7 (1), p.28-28, Article 28
Main Authors: Hsiao, Janet H., An, Jeehye, Hui, Veronica Kit Sum, Zheng, Yueyuan, Chan, Antoni B.
Format: Article
Language:English
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Summary: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.
ISSN:2056-7936
2056-7936
DOI:10.1038/s41539-022-00139-6