NDLI: GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data

Content Provider	Springer Nature Link
Author	Saez de Urabain, Irati R. Johnson, Mark H. Smith, Tim J.
Copyright Year	2014
Abstract	Fixation durations (FD) have been used widely as a measurement of information processing and attention. However, issues like data quality can seriously influence the accuracy of the fixation detection methods and, thus, affect the validity of our results (Holmqvist, Nyström, & Mulvey, 2012). This is crucial when studying special populations such as infants, where common issues with testing (e.g., high degree of movement, unreliable eye detection, low spatial precision) result in highly variable data quality and render existing FD detection approaches highly time consuming (hand-coding) or imprecise (automatic detection). To address this problem, we present GraFIX, a novel semiautomatic method consisting of a two-step process in which eye-tracking data is initially parsed by using velocity-based algorithms whose input parameters are adapted by the user and then manipulated using the graphical interface, allowing accurate and rapid adjustments of the algorithms’ outcome. The present algorithms (1) smooth the raw data, (2) interpolate missing data points, and (3) apply a number of criteria to automatically evaluate and remove artifactual fixations. The input parameters (e.g., velocity threshold, interpolation latency) can be easily manually adapted to fit each participant. Furthermore, the present application includes visualization tools that facilitate the manual coding of fixations. We assessed this method by performing an intercoder reliability analysis in two groups of infants presenting low- and high-quality data and compared it with previous methods. Results revealed that our two-step approach with adaptable FD detection criteria gives rise to more reliable and stable measures in low- and high-quality data.
Starting Page	53
Ending Page	72
Page Count	20
File Format	PDF
ISSN	07433808
Journal	Behavior Research Methods
Volume Number	47
Issue Number	1
e-ISSN	15543528
Language	English
Publisher	Springer US
Publisher Date	2014-03-27
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fixation duration Eyetracker methodology Data quality Infant Naturalistic Attention Cognitive Psychology
Content Type	Text
Resource Type	Article
Subject	Experimental and Cognitive Psychology 3200/3201 Psychology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data

Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults

Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults

GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data

Gazepath: An eye-tracking analysis tool that accounts for individual differences and data quality

Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults

Noise-robust fixation detection in eye movement data: Identification by two-means clustering (I2MC)

Eye-tracking data quality as affected by ethnicity and experimental design

Eyes wide shut: implied social presence, eye tracking and attention

GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data

Similar Documents

GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data

Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults

Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults

GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data

Gazepath: An eye-tracking analysis tool that accounts for individual differences and data quality

Parsing eye-tracking data of variable quality to provide accurate fixation duration estimates in infants and adults

Noise-robust fixation detection in eye movement data: Identification by two-means clustering (I2MC)

Eye-tracking data quality as affected by ethnicity and experimental design

Eyes wide shut: implied social presence, eye tracking and attention

GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data