Department of Computer and Information Science, University of Oregon, 1202 University of Oregon, Eugene, Oregon 97403-1202, USA.
Behav Res Methods. 2011 Sep;43(3):834-42. doi: 10.3758/s13428-011-0073-0.
In eye-tracking research, there is almost always a disparity between a person's actual gaze location and the location recorded by the eye tracker. Disparities that are constant over time are systematic error. In this article, we propose an error correction method that can reliably reduce systematic error and restore fixations to their true locations. We show that the method is reliable when the visual objects in the experiment are arranged in an irregular manner-for example, when they are not on a grid in which all fixations can be shifted to adjacent locations using the same directional adjustment. The method first calculates the disparities between fixations and their nearest objects. It then uses the annealed mean shift algorithm to find the mode of the disparities. The mode is demonstrated to correctly capture the magnitude and direction of the systematic error so that it can be removed. This article presents the method, an extended demonstration, and a validation of the method's efficacy.
在眼动追踪研究中，被试者的实际注视位置与眼动追踪仪记录的位置之间几乎总是存在差异。随着时间的推移而保持不变的差异是系统误差。在本文中，我们提出了一种错误纠正方法，可以可靠地减少系统误差并将注视点恢复到真实位置。我们表明，当实验中的视觉对象以不规则的方式排列时，该方法是可靠的，例如，当它们不在网格中时，所有注视点都可以使用相同的方向调整移至相邻位置。该方法首先计算注视点与其最近物体之间的差异。然后，它使用退火均值漂移算法找到差异的模式。该模式可以正确捕获系统误差的大小和方向，从而可以消除该误差。本文介绍了该方法、扩展演示以及该方法有效性的验证。
