Towards a Model of Information Seeking by Integrating Visual, Semantic and Memory Maps

This paper presents a threefold model of information seeking. A visual, a semantic and a memory map are dynamically computed in order to predict the location of the next fixation. This model is applied to a task in which the goal is to find among 40 words the one which best corresponds to a definition. Words have visual features and they are semantically organized. The model predicts scanpaths which are compared to human scanpaths on 3 high-level variables (number of fixations, average angle between saccades, rate of progression saccades). The best fit to human data is obtained when the memory map is given a strong weight and the semantic component a low weight.

[1]  Marilyn Hughes Blackmon,et al.  A Comprehension-based Model of Web Navigation and Its Application to Web Usability Analysis , 2000, BCS HCI.

[2]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[3]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[4]  S. Tipper,et al.  Inhibition of return to successively cued spatial locations: commentary on Pratt and Abrams (1995). , 1996, Journal of experimental psychology. Human perception and performance.

[5]  John R. Anderson,et al.  ACT-R: A Theory of Higher Level Cognition and Its Relation to Visual Attention , 1997, Hum. Comput. Interact..

[6]  Michael L. Mack,et al.  VISUAL SALIENCY DOES NOT ACCOUNT FOR EYE MOVEMENTS DURING VISUAL SEARCH IN REAL-WORLD SCENES , 2007 .

[7]  Robin L. Hill,et al.  Eye movements : a window on mind and brain , 2007 .

[8]  A. L. Yarbus.  Eye Movements During Perception of Complex Objects , 1967 .

[9]  Raymond Klein,et al.  Inhibitory tagging system facilitates visual search , 1988, Nature.

[10]  Wayne D. Gray,et al.  Suboptimal tradeoffs in information seeking , 2006, Cognitive Psychology.

[11]  Carol Collier Kuhlthau,et al.  Learning in Digital Libraries: An Information Search Process Approach , 1997, Libr. Trends.

[12]  Gary Feng,et al.  Eye movements as time-series random variables: A stochastic model of eye movement control in reading , 2006, Cognitive Systems Research.

[13]  J. Pratt,et al.  Inhibition of return to successively cued spatial locations. , 1995, Journal of experimental psychology. Human perception and performance.

[14]  John T. Guthrie,et al.  Locating Information in Documents: Examination of a Cognitive Model. , 1988 .

[15]  Danielle S. McNamara,et al.  Handbook of latent semantic analysis , 2007 .

[16]  Steven P. Tipper,et al.  Inhibition of return to successively cued spatial locations : Commentary on Pratt and Abrams (1995) , 1996 .

[17]  Wai-Tat Fu,et al.  SNIF-ACT: A Model of Information Foraging on the World Wide Web , 2003, User Modeling.

[18]  D. S. Wooding,et al.  The relationship between the locations of spatial features and those of fixations made during visual examination of briefly presented images. , 1996, Spatial vision.

[19]  Peter Brusilovsky,et al.  User Modeling 2003 , 2003, Lecture Notes in Computer Science.

[20]  Herbert A. Simon,et al.  The Structure of Ill Structured Problems , 1973, Artif. Intell..

[21]  Peter Pirolli,et al.  Information Foraging , 2009, Encyclopedia of Database Systems.

[22]  A. L. I︠A︡rbus.  Eye Movements and Vision , 1967 .

[23]  Patrick F. Reidy.  An Introduction to Latent Semantic Analysis , 2009 .

[24]  Gary Marchionini,et al.  Information Seeking in Electronic Environments , 1995 .