Proceedings of the Annual Meeting of the Cognitive Science Society

Hidden Markov models (HMM) can describe the spatial andtemporal characteristics of eye-tracking recordings incognitive tasks. Here, we introduce a new HMM approach.We developed HMMs based on fixation locations and we alsoused image information as an input feature. We demonstratethe benefits of the newly proposed model in a facerecognition study wherein an HMM was developed for everysubject. Discovery of regions of interest on facial stimuli isimproved as compared with earlier approaches. Moreover,clustering of the newly developed HMMs lead to very distinctgroups. The newly developed approach also allowsreconstructing image information at each fixation.