Cleaning up systematic error in eye-tracking data by using required fixation locations
Cleaning up systematic error in eye-tracking data by using required fixation locations
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DOI：
10.3758/bf03195487
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发表时间：
2002-11-01
期刊：
影响因子：
--
通讯作者：
Halverson, T
中科院分区：
文献类型：
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作者：
Hornof, AJ;Halverson, T
在运行眼动跟踪实验的过程中，一个计算机系统或子系统通常向参与者呈现刺激并记录手动响应，另一个计算机系统或子系统收集眼动数据，在实验过程中两者之间几乎没有交互。本文演示了这两个系统如何相互作用，以促进更丰富的实验设计和应用，并产生更准确的眼动跟踪数据。在眼动追踪研究中，参与者被周期性地指示看特定的屏幕位置或明确的所需注视位置（RFL），以便针对参与者校准眼动追踪器。实验程序的设计通常也会产生许多隐含的RFL屏幕位置，参与者必须在某个时间窗口或某个时刻查看这些位置，以便成功和正确地完成任务，但没有明确的指示来固定这些位置。在这些时间窗口中或在这些时刻，可以检查由眼睛跟踪器记录的注视与对应于隐式RFL的屏幕位置之间的差异，并且比较的结果可以用于各种目的。本文展示了如何使用视差来监控眼动仪校准精度的恶化，并在必要时自动调用重新校准程序。本文还演示了差异如何在屏幕区域和参与者之间变化，以及如何使用每个参与者的独特错误签名来减少为该参与者收集的眼动数据中的系统误差。
In the course of running an eye-tracking experiment, one computer system or subsystem typically presents the stimuli to the participant and records manual responses, and another collects the eye movement data, with little interaction between the two during the course of the experiment. This article demonstrates how the two systems can interact with each other to facilitate a richer set of experimental designs and applications and to produce more accurate eye tracking data. In an eye-tracking study, a participant is periodically instructed to look at specific screen locations, or explicit required fixation locations (RFLs), in order to calibrate the eye tracker to the participant. The design of an experimental procedure will also often produce a number of implicit RFLs-screen locations that the participant must look at within a certain window of time or at a certain moment in order to successfully and correctly accomplish a task, but without explicit instructions to fixate those locations. In these windows of time or at these moments, the disparity between the fixations recorded by the eye tracker and the screen locations corresponding to implicit RFLs can be examined, and the results of the comparison can be used for a variety of purposes. This article shows how the disparity can be used to monitor the deterioration in the accuracy of the eye tracker calibration and to automatically invoke a recalibration procedure when necessary. This article also demonstrates how the disparity will vary across screen regions and participants and how each participant's unique error signature can be used to reduce the systematic error in the eye movement data collected for that participant.