using eye-tracking to understand interaction patterns in web information search

8/14/2019 Using Eye-Tracking to Understand Interaction Patterns in Web Information Search

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Using Eye-tracking to UnderstandInteraction Patterns in WebInformation Search

Jacek GwizdkaDepartment of Library and Information Science

CONTACT:

www.jsg.tel



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



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



How: Data Exploration and Analysis (1)

Poodle User Interaction Exploration and Modeling System



5

Reading model

Region transition

model

How: Data Exploration and Analysis (2)

Eye-movement on a web pageImages and control



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




• 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)




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




• Pupil size – Task evoked pupil response (TEPR) as ameasure of mental effort

Query reformulation

Query reformulationSwitching between

results list and

individual results



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





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



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)



• 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



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



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?

using eye-tracking to understand interaction patterns in web information search

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