Evaluating Saccade-Bounded Eye Movement Features for the User Modeling

This paper presents a foundation for an extensive framework expanding the use of eye movements as a source for user modeling. This work constructs a model of human oculomotor plant features during user's interactions with the goal of better interpreting user gaze data related to resource content. This work also explores the anatomical reasoning behind incorporating additional gaze features, the integration of the additional features into an existing interest modeling architecture, and a plan for assessing the impact of the addition of the features. The paper concludes with few observations regarding the promises of using OPF in a user modeling framework in studying search behavior.

[1]  Oleg V. Komogortsev,et al.  2D Oculomotor Plant Mathematical Model for eye movement simulation , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[2]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.

[3]  Brian P. Bailey,et al.  Using Eye Gaze Patterns to Identify User Tasks , 2004 .

[4]  Ali Borji,et al.  What do eyes reveal about the mind?: Algorithmic inference of search targets from fixations , 2015, Neurocomputing.

[5]  James J. Clark,et al.  A computational model for task inference in visual search. , 2013, Journal of vision.

[6]  Umberto Straccia,et al.  A personalized collaborative Digital Library environment: a model and an application , 2005, Inf. Process. Manag..

[7]  A T Bahill,et al.  Development, validation, and sensitivity analyses of human eye movement models. , 1980, Critical reviews in bioengineering.

[8]  Peter Brusilovsky,et al.  Inferring word relevance from eye-movements of readers , 2011, IUI '11.

[9]  Frank M. Shipman,et al.  Unified Relevance Feedback for Multi-Application User Interest Modeling , 2015, JCDL.

[10]  A. Bovik,et al.  Visual search in noise: revealing the influence of structural cues by gaze-contingent classification image analysis. , 2006, Journal of vision.

[11]  Marilyn Tremaine CHI '01 Extended Abstracts on Human Factors in Computing Systems , 2001, CHI Extended Abstracts.

[12]  Oleg V. Komogortsev,et al.  Two-Dimensional Linear Homeomorphic Oculomotor Plant Mathematical Model , 2012 .

[13]  L. Itti,et al.  Defending Yarbus: eye movements reveal observers' task. , 2014, Journal of vision.

[14]  L. Optican,et al.  Dynamic eye plant models and the control of eye movements , 2003, Strabismus.

[15]  Jacek Gwizdka News stories relevance effects on eye-movements , 2014, ETRA.

[16]  Mark Claypool,et al.  Implicit interest indicators , 2001, IUI '01.

[17]  N. Shimizu [Neurology of eye movements]. , 2000, Rinsho shinkeigaku = Clinical neurology.

[18]  Andreas Dengel,et al.  Eye movements as implicit relevance feedback , 2008, CHI Extended Abstracts.

[19]  Frank M. Shipman,et al.  Rationale and Architecture for Incorporating Human Oculomotor Plant Features in User Interest Modeling , 2016, CHIIR.

[20]  G. Zelinsky,et al.  Eye can read your mind: decoding gaze fixations to reveal categorical search targets. , 2013, Journal of vision.

[21]  Colin Ware,et al.  Zooming versus multiple window interfaces: Cognitive costs of visual comparisons , 2006, TCHI.

[22]  Jude W. Shavlik,et al.  Learning users' interests by unobtrusively observing their normal behavior , 2000, IUI '00.

[23]  Douglas W. Oard,et al.  Implicit Feedback for Recommender Systems , 1998 .

[24]  Frank M. Shipman,et al.  Mining user interest from search tasks and annotations , 2013, CIKM.

[25]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[26]  Cecilia R. Aragon,et al.  TWO DIMENTIONAL OCULOMOTOR PLANT MECHANICAL MODEL ( 2 DOPMM , 2010 .

[27]  Ali Borji,et al.  Probabilistic learning of task-specific visual attention , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Joseph H. Goldberg,et al.  Eye tracking for visualization evaluation: Reading values on linear versus radial graphs , 2011, Inf. Vis..

[29]  Nicholas J. Belkin,et al.  Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback , 2001, Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.