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Article: Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling

TitleIs having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling
Authors
KeywordsEye movement
Face learning
Face recognition
Hidden Markov model
Individual difference
Issue Date2017
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/visres
Citation
Vision Research, 2017, v. 141, p. 204-216 How to Cite?
AbstractThe hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants’ patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. © 2017 Elsevier Ltd
Persistent Identifierhttp://hdl.handle.net/10722/244723
ISSN
2017 Impact Factor: 2.069
2015 SCImago Journal Rankings: 0.957
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChuk, TY-
dc.contributor.authorChan, AB-
dc.contributor.authorHsiao, JHW-
dc.date.accessioned2017-09-18T01:57:51Z-
dc.date.available2017-09-18T01:57:51Z-
dc.date.issued2017-
dc.identifier.citationVision Research, 2017, v. 141, p. 204-216-
dc.identifier.issn0042-6989-
dc.identifier.urihttp://hdl.handle.net/10722/244723-
dc.description.abstractThe hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants’ patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. © 2017 Elsevier Ltd-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/visres-
dc.relation.ispartofVision Research-
dc.rightsPosting accepted manuscript (postprint): © <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectEye movement-
dc.subjectFace learning-
dc.subjectFace recognition-
dc.subjectHidden Markov model-
dc.subjectIndividual difference-
dc.titleIs having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling-
dc.typeArticle-
dc.identifier.emailHsiao, JHW: jhsiao@hku.hk-
dc.identifier.authorityHsiao, JHW=rp00632-
dc.identifier.doi10.1016/j.visres.2017.03.010-
dc.identifier.scopuseid_2-s2.0-85019006609-
dc.identifier.hkuros276071-
dc.identifier.volume141-
dc.identifier.spage204-
dc.identifier.epage216-
dc.identifier.isiWOS:000418783200020-
dc.publisher.placeUnited Kingdom-

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