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Conference Paper: Hidden Markov modeling of eye movements with image information leads to better discovery of regions of interest
Title | Hidden Markov modeling of eye movements with image information leads to better discovery of regions of interest |
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Authors | |
Keywords | Eye-tracking Face recognition Hidden Markov Model Machine learning |
Issue Date | 2016 |
Publisher | Cognitive 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. 1032-1037 How to Cite? |
Abstract | Hidden Markov models (HMM) can describe the spatial and temporal characteristics of eye-tracking recordings in cognitive tasks. Here, we introduce a new HMM approach. We developed HMMs based on fixation locations and we also used image information as an input feature. We demonstrate the benefits of the newly proposed model in a face recognition study wherein an HMM was developed for every subject. Discovery of regions of interest on facial stimuli is improved compared to earlier approaches. Moreover, clustering of the newly developed HMMs lead to very distinct groups. The newly developed approach also allows reconstructing image information at fixation. |
Description | Conference Theme: Integrating Psychological, Philosophical, Linguistic, Computational and Neural Perspectives Poster Session 2: no. 56 |
Persistent Identifier | http://hdl.handle.net/10722/232765 |
DC Field | Value | Language |
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dc.contributor.author | Brueggemann, S | - |
dc.contributor.author | Chan, AB | - |
dc.contributor.author | Hsiao, JHW | - |
dc.date.accessioned | 2016-09-20T05:32:10Z | - |
dc.date.available | 2016-09-20T05:32:10Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | The 38th Annual Meeting of the Cognitive Science Society (CogSci 2016), Philadelphia, PA., 10-13 August 2016. In Conference Proceedings, 2016, p. 1032-1037 | - |
dc.identifier.uri | http://hdl.handle.net/10722/232765 | - |
dc.description | Conference Theme: Integrating Psychological, Philosophical, Linguistic, Computational and Neural Perspectives | - |
dc.description | Poster Session 2: no. 56 | - |
dc.description.abstract | Hidden Markov models (HMM) can describe the spatial and temporal characteristics of eye-tracking recordings in cognitive tasks. Here, we introduce a new HMM approach. We developed HMMs based on fixation locations and we also used image information as an input feature. We demonstrate the benefits of the newly proposed model in a face recognition study wherein an HMM was developed for every subject. Discovery of regions of interest on facial stimuli is improved compared to earlier approaches. Moreover, clustering of the newly developed HMMs lead to very distinct groups. The newly developed approach also allows reconstructing image information at fixation. | - |
dc.language | eng | - |
dc.publisher | Cognitive Science Society. The Conference Proceedings' website is located at http://mindmodeling.org/cogsci2016/index.html | - |
dc.relation.ispartof | Proceedings of the 38th Annual Conference of the Cognitive Science Society, CogSci 2016 | - |
dc.subject | Eye-tracking | - |
dc.subject | Face recognition | - |
dc.subject | Hidden Markov Model | - |
dc.subject | Machine learning | - |
dc.title | Hidden Markov modeling of eye movements with image information leads to better discovery of regions of interest | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Hsiao, JHW: jhsiao@hku.hk | - |
dc.identifier.authority | Hsiao, JHW=rp00632 | - |
dc.description.nature | postprint | - |
dc.identifier.hkuros | 263211 | - |
dc.identifier.spage | 1032 | - |
dc.identifier.epage | 1037 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 161007 | - |