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Using Eye-tracking to UnderstandInteraction Patterns in WebInformation Search
Jacek GwizdkaDepartment of Library and Information Science
CONTACT:
www.jsg.tel
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Why eye-tracking?
• Information seeking is cognitive in nature
• Eye-mind link hypothesis– Eye movement is cognitively controlled and hence reflects
cognitive processes
– Changes in eye-movement patterns reflect changes in higher-
level cognitive processes and therefore (some) correspond todecision points
– examples: reading patterns, attention
• Eye movement is important to a completedescription of the phenomena addressed byinformation science
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Two approaches: bottom-up & top-down
• Bottom-up: using eye-tracking data to build modelsof cognitive processes– example: reading model based on EZ-Reader model – a
sequence of lexical fixations>113ms (Reichle, Pollatsek & Rayner,
2006)
• Top-down: characterizing cognitive aspects of information seeking episodes based on eye-trackingdata– example: information search session is segmented into
units which are then characterized based on eye-trackingdata. Example units of analysis: search tasks, tasksegments that correspond to cognitive decisions
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How: Data Exploration and Analysis (1)
Poodle User Interaction Exploration and Modeling System
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5
Reading model
Region transition
model
How: Data Exploration and Analysis (2)
Eye-movement on a web pageImages and control
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What we learned? - 1
• Search tasks: advance obituary (AO), interview preparation(IP), copy editing (CE), and back- ground information (BI)
• AO, IP bias towards reading and CE appears to biastowards scanning
• User’s higher level information seeking strategyaffects lower level visual cognition strategy
Scan to read transition
probabilities by user and task
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What we learned? - 2
• We examine regions of screen “considered” by aperson before making cognitive decisions (reformulatequery, visit web page)(convex hull area enclosed by the boundary fixations, in square pixels)
– Use of overview cloud is informed by tags in this cloud and
by search results list (15000 & 14000 sq. px)– Visits to search results are primarily informed by search
results, and to lesser extent by overview tag cloud (20000 &9000 sq. px)
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What we learned? - 3
Cost associated with switching cognitive activity
• between reading and scanning - reflects a change in cognitive
engagement
• between regions of screen: e.g., results list and overview(“cloud”)
– example: results list to self: higher transition probability > overview to self
overview to list higher transition probability > list tooverview
asymmetry in the cost of cognitive switching between searchand overview
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What we learned? - 4
• Pupil size – Task evoked pupil response (TEPR) as ameasure of mental effort
Query reformulation
Query reformulationSwitching between
results list and
individual results
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Questions?
Acknowledgements
PooDLE Project: Personalization of the Digital LibraryExperience
http://bit.ly/poodle_project
Poodle Team: Nick Belkin (PI), Jacek Gwizdka (PI), Xiangmin Zhang(PI)
PostDoc: Ralf Bierig
PhD students: Chang Liu, Jingjing Liu, and, in particular, Michael
Cole Master student: Jun Zhang
Funded by IMLS grant # LG-06-07-0105-07
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Cognitive Load and Web Search Tasks Jacek Gwizdka
Understand mental demands of search tasks andinterfaces
user interface
differences: L
higher peak
cognitive load: C
higher average
cognitive load: Q & B
CONTACT:
www.jsg.tel
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Using Eye-tracking to SegmentInteraction Patterns in WebInformation Search• Dr. Jacek Gwizdka has been using eye-tracking to investigate how
people use search results lists and keyword groups in searching forinformation. In his panel contribution, he will talk about relationshipsamong the eye fixation patterns, the complexity of informationdisplay and the cognitive differences between people. In particular,he will talk about using eye fixations to segment other interaction
data into meaningful units of cognitive actions, about using eyefixations to determine words that were processed by users, andabout using processing times and fixation patterns to distinguishbetween different levels of processing (perceptual vs. semanticprocessing) and different types of user engagement with text (i.e.reading vs. skimming). Dr. Gwizdka's current research is a part of alarger effort on personalization of digital library experience and isfunded by the Institute for Museum and Library Studies (IMLS)
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• Based on the EZ-Reader model: text reading in line(s)• scanning - isolated lexical fixations
• reading - sequence of lexical fixations
• isolated: in terms of location (spatial coords)
• Ref: Reichle, Pollatsek, Rayner (2006)
• lexical fixations >= 113 ms
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Eye movement
• Parameters of eye movement of interest:– length of saccades
– fixation location and length of fixations
– significance of first fixation, time to first fixation
– pupil size
– foveal coverage
– dynamics
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Questions for Panelists:
• What made you interested in using eye-trackers in yourresearch?
• What did you want to attain by using eye-trackers?
• Did you capture what you expected ?
• Are there anything you could obtain from using eye-trackers which you did not anticipate?
• Are there any gaps between what you want to obtain andwhat you could obtain?
• What do you want to attain in the near future by usingeye-tracking?
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