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

机译:Grafix:解析低质量和高质量的眼跟踪数据的半自动方法

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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.
机译:固定持续时间(FD)已被广泛使用作为信息处理和注意的测量。然而,数据质量等问题可以严重影响固定检测方法的准确性,从而影响我们的结果的有效性(Holmqvist,Nyström,&Mulvey,2012)。在研究婴儿等特殊人群时,这是至关重要的,其中具有测试的常见问题(例如,高度的运动,不可靠的眼睛检测,低空间精度)导致高度可变的数据质量,并且具有高度耗时的现有FD检测方法(手工)编码)或不精确(自动检测)。为了解决这个问题,我们呈现Grafix,一种新颖的半自动方法,其由两步过程组成,其中最初通过使用基于速度的算法来解析眼睛跟踪数据,其输入参数由用户调整,然后使用图形界面进行操纵,允许对算法的准确和快速调整。本算法(1)平滑原始数据,(2)内插缺失数据点,(3)应用许多标准以自动评估和去除艺术定影。输入参数(例如,速度阈值,插值延迟)可以容易地手动调整以适合每个参与者。此外,本申请包括可视化工具,其便于手动编码固定。我们通过在呈现低质量和高质量数据的两组婴儿中进行次要互联器可靠性分析来评估该方法,并将其与先前的方法进行比较。结果表明,我们的两步方法具有适应性的FD检测标准,在低质量和高质量的数据中引发了更可靠和稳定的措施。

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