Eye tracking in developmental cognitive neuroscience - The good, the bad and the ugly
Eye tracking in developmental cognitive neuroscience - The good, the bad and the ugly
复制标题
DOI：
10.1016/j.dcn.2019.100710
复制
发表时间：
2019-12-01
影响因子：
4.7
通讯作者：
Hooge, Ignace T. C.
中科院分区：
文献类型：
--
作者：
Hessels, Roy S.;Hooge, Ignace T. C.
眼动追踪是发展认知神经科学中一种流行的研究工具，用于研究知觉和认知过程的发展。然而，在发展背景下的眼动跟踪也具有挑战性。在本文中，我们问如何眼动追踪数据质量的知识可以用来改善眼动追踪记录和分析的纵向研究，使有效的结论，儿童发展可能会得出。我们通过采用数据质量的观点来回答这个问题，并调查了眼动跟踪设置，培训方案和青年研究的数据分析（调查了6000名儿童的神经认知发展）。我们首先展示了我们的眼动追踪设置是如何优化的，以记录高质量的眼动追踪数据。其次，我们表明，即使经过全面的训练协议，眼动跟踪数据质量也可能依赖于操作员。最后，我们报告了四个年龄组（5个月，10个月，3岁和9岁）的眼动跟踪数据质量指标的分布，基于1531个记录。最后，我们为（前瞻性）发展性眼动追踪研究人员提供建议，并对其他方法进行概括。
Eye tracking is a popular research tool in developmental cognitive neuroscience for studying the development of perceptual and cognitive processes. However, eye tracking in the context of development is also challenging. In this paper, we ask how knowledge on eye-tracking data quality can be used to improve eye-tracking recordings and analyses in longitudinal research so that valid conclusions about child development may be drawn. We answer this question by adopting the data-quality perspective and surveying the eye-tracking setup, training protocols, and data analysis of the YOUth study (investigating neurocognitive development of 6000 children). We first show how our eye-tracking setup has been optimized for recording high-quality eye-tracking data. Second, we show that eye-tracking data quality can be operator-dependent even after a thorough training protocol. Finally, we report distributions of eye-tracking data quality measures for four age groups (5 months, 10 months, 3 years, and 9 years), based on 1531 recordings. We end with advice for (prospective) developmental eye-tracking researchers and generalizations to other methodologies.