The Role of Eye Movement Consistency in Learning to Recognise Faces : Computational and Experimental Examinations
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings for the 42nd Annual Meeting of the Cognitive Science Society |
Subtitle of host publication | Developing a Mind: Learning in Humans, Animals, and Machines |
Publisher | The Cognitive Science Society |
Pages | 1072-1078 |
Publication status | Published - Jul 2020 |
Publication series
Name | Proceedings for the Annual Meeting of the Cognitive Science Society, CogSci |
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Conference
Title | 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020) |
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Location | Virtual |
Period | 29 July - 1 August 2020 |
Link(s)
Abstract
In face recognition, the frequency of looking at the eyes, the
most diagnostic feature, predicts better performance in
adults but not in children, suggesting that different factors
may underlie children’s face recognition performance. Here
we test the hypothesis that eye movement consistency plays
an important role during early learning stages. Through
computational modelling that combines a deep neural
network and a hidden Markov model that learns eye
movement strategies by interacting with the network, we
showed that consistency instead of eye movement pattern
better predicted face recognition performance during early
learning stages. Similarly, in human studies, children’s
consistency but not pattern of eye movements predicted
face recognition performance, and their eye movement
consistency was associated with executive function abilities.
Thus, learning to recognise faces initially involves
developing a consistent visual routine, which depends on
executive function abilities. This finding has important
implications for learning in both healthy and clinical
populations.
Research Area(s)
- Eye movement, face recognition, deep neural network, hidden Markov model, entropy
Citation Format(s)
The Role of Eye Movement Consistency in Learning to Recognise Faces: Computational and Experimental Examinations . / Hsiao, Janet H.; An, Jeehye; Chan, Antoni B.
Proceedings for the 42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines. The Cognitive Science Society, 2020. p. 1072-1078 (Proceedings for the Annual Meeting of the Cognitive Science Society, CogSci).
Proceedings for the 42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines. The Cognitive Science Society, 2020. p. 1072-1078 (Proceedings for the Annual Meeting of the Cognitive Science Society, CogSci).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review