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Conference Paper: Mind reading: discovering individual preferences from eye movements using switching hidden Markov models

TitleMind reading: discovering individual preferences from eye movements using switching hidden Markov models
Authors
KeywordsHidden Markov model
Gaze preference
Eye movement
Face recognition
Issue Date2016
PublisherCognitive Science Society. The Conference Proceedings' website is located at http://mindmodeling.org/cogsci2016/index.html
Citation
The 38th Annual Meeting of the Cognitive Science Society (CogSci 2016), Philadelphia, PA., 10-13 August 2016. In Conference Proceedings, 2016, p. 182-187 How to Cite?
AbstractHere we used a hidden Markov model (HMM) based approach to infer individual choices from eye movements in preference decision-making. We assumed that during a decision making process, participants may switch between exploration and decision-making periods, and this behavior can be better captured with a Switching HMM (SHMM). Through clustering individual eye movement patterns described in SHMMs, we automatically discovered two groups of participants with different decision making behavior. One group showed a strong and early bias to look more often at the to-be chosen stimulus (i.e., the gaze cascade effect; Shimojo et al., 2003) with a short final decision-making period. The other group showed a weaker cascade effect with a longer final decision- making period. The SHMMs also showed capable of inferring participants’ preference choice on each trial with high accuracy. Thus, our SHMM approach made it possible to reveal individual differences in decision making and discover individual preferences from eye movement data.
DescriptionConference Theme: Integrating Psychological, Philosophical, Linguistic, Computational and Neural Perspectives
Poster Session 3: no. 33
Persistent Identifierhttp://hdl.handle.net/10722/232763

 

DC FieldValueLanguage
dc.contributor.authorChuk, TY-
dc.contributor.authorChan, AB-
dc.contributor.authorShimojo, S-
dc.contributor.authorHsiao, JHW-
dc.date.accessioned2016-09-20T05:32:09Z-
dc.date.available2016-09-20T05:32:09Z-
dc.date.issued2016-
dc.identifier.citationThe 38th Annual Meeting of the Cognitive Science Society (CogSci 2016), Philadelphia, PA., 10-13 August 2016. In Conference Proceedings, 2016, p. 182-187-
dc.identifier.urihttp://hdl.handle.net/10722/232763-
dc.descriptionConference Theme: Integrating Psychological, Philosophical, Linguistic, Computational and Neural Perspectives-
dc.descriptionPoster Session 3: no. 33-
dc.description.abstractHere we used a hidden Markov model (HMM) based approach to infer individual choices from eye movements in preference decision-making. We assumed that during a decision making process, participants may switch between exploration and decision-making periods, and this behavior can be better captured with a Switching HMM (SHMM). Through clustering individual eye movement patterns described in SHMMs, we automatically discovered two groups of participants with different decision making behavior. One group showed a strong and early bias to look more often at the to-be chosen stimulus (i.e., the gaze cascade effect; Shimojo et al., 2003) with a short final decision-making period. The other group showed a weaker cascade effect with a longer final decision- making period. The SHMMs also showed capable of inferring participants’ preference choice on each trial with high accuracy. Thus, our SHMM approach made it possible to reveal individual differences in decision making and discover individual preferences from eye movement data.-
dc.languageeng-
dc.publisherCognitive Science Society. The Conference Proceedings' website is located at http://mindmodeling.org/cogsci2016/index.html-
dc.relation.ispartofProceedings of the 38th Annual Conference of the Cognitive Science Society, CogSci 2016-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectHidden Markov model-
dc.subjectGaze preference-
dc.subjectEye movement-
dc.subjectFace recognition-
dc.titleMind reading: discovering individual preferences from eye movements using switching hidden Markov models-
dc.typeConference_Paper-
dc.identifier.emailHsiao, JHW: jhsiao@hku.hk-
dc.identifier.authorityHsiao, JHW=rp00632-
dc.description.naturepostprint-
dc.identifier.hkuros263200-
dc.identifier.spage182-
dc.identifier.epage187-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161007-

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