Dynamic Programming for Re-Mapping Noisy Fixations in Translation Tasks

Michael Carl

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    Resumé

    Eyetrackers which allow for free head movements are in many cases imprecise to the extent that reading patterns become heavily distorted. The poor usability and interpretability of
    these gaze patterns is corroborated by a "naïve" fixation-to-symbol mapping, which often wrongly maps the possibly drifted center of the observed fixation onto the symbol directly
    below it. In this paper I extend this naïve fixation-to-symbol mapping by introducing background knowledge about the translation task. In a first step, the sequence of fixation-to- symbol mappings is extended into a lattice of several possible fixated symbols, including those on the line above and below the naïve fixation mapping. In a second step a dynamic
    programming algorithm applies a number of heuristics to find the best path through the lattice, based on the probable distance in characters, in words and in pixels between
    successive fixations and the symbol locations, so as to smooth the gazing path according to the background gazing model. A qualitative and quantitative evaluation shows that the algorithm increases the accuracy of the re-mapped symbol sequence.
    OriginalsprogEngelsk
    TidsskriftJournal of Eye Movement Research
    Vol/bind6
    Udgave nummer2
    Sider (fra-til)1-11
    ISSN1995-8692
    StatusUdgivet - 2013

    Emneord

    • Fixation-to-symbol mapping
    • drift-correction algorithm
    • drift in gaze data

    Citer dette

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    Dynamic Programming for Re-Mapping Noisy Fixations in Translation Tasks. / Carl, Michael.

    I: Journal of Eye Movement Research, Bind 6, Nr. 2, 2013, s. 1-11.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - Dynamic Programming for Re-Mapping Noisy Fixations in Translation Tasks

    AU - Carl, Michael

    PY - 2013

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    N2 - Eyetrackers which allow for free head movements are in many cases imprecise to the extent that reading patterns become heavily distorted. The poor usability and interpretability ofthese gaze patterns is corroborated by a "naïve" fixation-to-symbol mapping, which often wrongly maps the possibly drifted center of the observed fixation onto the symbol directlybelow it. In this paper I extend this naïve fixation-to-symbol mapping by introducing background knowledge about the translation task. In a first step, the sequence of fixation-to- symbol mappings is extended into a lattice of several possible fixated symbols, including those on the line above and below the naïve fixation mapping. In a second step a dynamicprogramming algorithm applies a number of heuristics to find the best path through the lattice, based on the probable distance in characters, in words and in pixels betweensuccessive fixations and the symbol locations, so as to smooth the gazing path according to the background gazing model. A qualitative and quantitative evaluation shows that the algorithm increases the accuracy of the re-mapped symbol sequence.

    AB - Eyetrackers which allow for free head movements are in many cases imprecise to the extent that reading patterns become heavily distorted. The poor usability and interpretability ofthese gaze patterns is corroborated by a "naïve" fixation-to-symbol mapping, which often wrongly maps the possibly drifted center of the observed fixation onto the symbol directlybelow it. In this paper I extend this naïve fixation-to-symbol mapping by introducing background knowledge about the translation task. In a first step, the sequence of fixation-to- symbol mappings is extended into a lattice of several possible fixated symbols, including those on the line above and below the naïve fixation mapping. In a second step a dynamicprogramming algorithm applies a number of heuristics to find the best path through the lattice, based on the probable distance in characters, in words and in pixels betweensuccessive fixations and the symbol locations, so as to smooth the gazing path according to the background gazing model. A qualitative and quantitative evaluation shows that the algorithm increases the accuracy of the re-mapped symbol sequence.

    KW - Fixation-to-symbol mapping

    KW - drift-correction algorithm

    KW - drift in gaze data

    M3 - Journal article

    VL - 6

    SP - 1

    EP - 11

    JO - Journal of Eye Movement Research

    JF - Journal of Eye Movement Research

    SN - 1995-8692

    IS - 2

    ER -