toward interaction models derived from eye-tracking data presented at polish ia summit 2012

7/31/2019 Toward Interaction Models Derived From Eye-tracking Data presented at Polish IA Summit 2012

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Interaction Models DerivedFrom Eye-tracking Data .

Jacek Gwizdka & Michael ColeRutgers University, USA

[email protected]

http://jsg.tel

April 19, 2012

Towards
http://eye.html/http://eye.html/


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Eye-tracking?

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Eye-trackers Tobii


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Eye-movement in UX Research

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http://images/KPE1/KPE1_fromS004/05-1/Composer_Answ_Lookup_KPE1_fromS004_05-1Segment%201.avi


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There is a Lot More Eye-tracking Datacan offer UX / HCI / IA

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Eye-tracking Data

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State2State1

State3

Patterns


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Eye-movement Patterns

New methodology to analyze eye-movement patterns

Model readingand Measure cognitive effort Correlate with higher-level constructs

user task characteristics,

user knowledge, etc.

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Eye-tracking Fundamentals

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Reading Model Origins

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Based on E-Z Reader modelRayner , Pollatsek, Reichle

Serial reading

Words can be identified in parafovialregion

Early lexical access (word familiarity) + Complete lexical processing (word identification)

2o (70px) foveal region parafoveal region

a bit MORE


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Two-State Reading Model

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Filter fixations < 150ms (min time required for lexical processing)

Model states characterized by:

probability of transitions; number of lexical fixations; duration

length of eye-movement trajectory, amount of text covered

ScanRead

1-q

p

1-p

q

a bit MORE

isolatedfixationsfixation

sequences


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Example Reading Sequence

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Fixation sequence: (F F F) F (F F F) F F F F (F F F F F F) FReading modelstates: R S R S S S S R S

Reading state R | Scanning state S


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Cognitive Effort Measures of Reading

Reading Speed

Fixation Regression

Perceptual Span

Fixation Duration(lexical processing excess)

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foveal region

a b c d

Perceptual span = Mean(a,b,c,d)

regression

excess


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User Study 1: Cognitive Effort and Tasks

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OBI: advanced obituary

INT: interview preparation

CPE: copy editing

BIC: background information

N=32

MORE

JournalistsInformation Search


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Eye-data and Cognitive Effort Measures

Cognitive effort measuresreading speed

mean fixation durationperceptual spantotal fixation regressions

Task complexity

by designCopy Editing (CPE)Advance Obituary (OBI)

Search efforttask timepages visitedqueries entered

Subjective TaskDifficulty

CPE INT BIC OBI

As expected:

Copy Editing CPE easiestAdvance Obituary OBI most difficultSig: Kruskal-Wallis 2 =46.1, p


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Eye-data and Task Characteristics

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Measure Related Task CharacteristicsFrequency

of reading

state

transitions

SRbias to readAdvanced obituary andInterview preparationtasks: search

for document; task goal not specific

RS bias to scanCopy Editingtask: search for segment and task goal specific

ScanRead

1-q

p

1-p

q

MORE

Copy EditingInterview preparation


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User Study 2: Assessing Users Knowledge

Search in Genomics Domain

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N=40

MORE

Rate own domainknowledge


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Results: Modeling Domain Knowledge

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Reading Model features &cognitive effort measures

Eye-tracking Data

Domain knowledgeMeSH-based self-ratings

predicted

self-rated


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Results: Modeling Domain Knowledge

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predicted

self-rated

Reading Model features &

cognitive effort measuresreading seq length and

total durationperceptual spanfixation durationregressions

Reading Model

Eye-tracking Data Random

Forest Model

Domain knowledgeMeSH-based self-ratings

m

tk

=PDK

m

iii

*5

)*(1

Foreachuser predict

build model

agglomerativehierarchical

clustering(Wards)

PDK: Participants

domain knowledge

MORE


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From Real-time Interactions to Applications

CognitiveLoadDomainKnowledge InformationRelevance

Adaptpresentation& content

EnableInteraction(e.g., disabilities)

TaskAspects

Eye-TrackingData

Standardinput devices(mouse, keyboard)

other psycho-physiological devices(EEG, SCR, HRV)

Betterunderstandinteraction

Micro-level

Macro-level

Applications

CognitiveLoad Model

ReadingModel

TaskModel

Models

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Thank You! Dziekuje!

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Funding: Google, HP, IMLS (now funded by IMLS CAREER)

Collaborators: Drs. Nicholas Belkin, Art Chaovalitwongse (U Wash), Xiangmin Zhang,

Ralf Bierig (Post Doc); PhD students: Michael Cole (co-author), Chang Liu, Jingjing Liu, Irene Lopatovska

+ many Master and undergraduate students

Acknowledgements:


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Pytania?

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More info & contact http://jsg.tel

toward interaction models derived from eye-tracking data presented at polish ia summit 2012

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