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

    Saez de Urabain, I.R. and Johnson, Mark H. and Smith, Tim J. (2014) GraFIX: a semiautomatic approach for parsing low- and high-quality eye-tracking data. Behavior Research Methods 47 (1), pp. 53-72. ISSN 1554-3528.

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    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.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Fixation duration, Eyetracker methodology, Data quality, Infant, Naturalistic, Attention
    School: Birkbeck Schools and Departments > School of Science > Psychological Sciences
    Research Centre: Moving Image, Birkbeck Institute for the (BIMI), Brain and Cognitive Development, Centre for (CBCD)
    Depositing User: Administrator
    Date Deposited: 31 Mar 2014 09:19
    Last Modified: 14 Dec 2016 09:43
    URI: http://eprints.bbk.ac.uk/id/eprint/9476

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