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Conference Paper: The role of eye movement consistency in learning to recognise faces: Computational and experimental examinations
Title | The role of eye movement consistency in learning to recognise faces: Computational and experimental examinations |
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Authors | |
Keywords | Eye movement face recognition deep neural network hidden Markov model entropy |
Issue Date | 2020 |
Publisher | Cognitive Science Society. The Proceeding's web site is located at https://cognitivesciencesociety.org/cogsci20/index.html |
Citation | Proceedings of the 42nd Annual Conference of the Cognitive Science Society (CogSci 2020), Virtual Conference, Toronto, Canada, 29 July - 1 August 2020, p. 1072-1078 How to Cite? |
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 modeling 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 recognize 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. |
Persistent Identifier | http://hdl.handle.net/10722/285366 |
DC Field | Value | Language |
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dc.contributor.author | Hsiao, JHW | - |
dc.contributor.author | An, JH | - |
dc.contributor.author | Chan, AB | - |
dc.date.accessioned | 2020-08-18T03:52:48Z | - |
dc.date.available | 2020-08-18T03:52:48Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings of the 42nd Annual Conference of the Cognitive Science Society (CogSci 2020), Virtual Conference, Toronto, Canada, 29 July - 1 August 2020, p. 1072-1078 | - |
dc.identifier.uri | http://hdl.handle.net/10722/285366 | - |
dc.description.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 modeling 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 recognize 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. | - |
dc.language | eng | - |
dc.publisher | Cognitive Science Society. The Proceeding's web site is located at https://cognitivesciencesociety.org/cogsci20/index.html | - |
dc.relation.ispartof | Proceedings of the 42nd Annual Conference of the Cognitive Science Society (CogSci 2020) | - |
dc.subject | Eye movement | - |
dc.subject | face recognition | - |
dc.subject | deep neural network | - |
dc.subject | hidden Markov model | - |
dc.subject | entropy | - |
dc.title | The role of eye movement consistency in learning to recognise faces: Computational and experimental examinations | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Hsiao, JHW: jhsiao@hku.hk | - |
dc.identifier.authority | Hsiao, JHW=rp00632 | - |
dc.identifier.hkuros | 312839 | - |
dc.identifier.spage | 1072 | - |
dc.identifier.epage | 1078 | - |