Abstract
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.
Highlights
IntroductionWhile it is physically impossible to see the world through a child's eyes, it illustrates the intuitive appeal of investigating one's looking behavior
What wonder it must be to see the world through a child's eyes as they grow up
If the analysis tools used are susceptible to differences in data quality, invalid conclusions may be drawn about child development, at the individual level
Summary
While it is physically impossible to see the world through a child's eyes, it illustrates the intuitive appeal of investigating one's looking behavior. Eye tracking has been one of the main research methods in the last decades for gaining insights into early (neuro)cognitive development Eye tracking is one of the main methods used in the YOUth study 1 investigating individual developmental trajectories. If the analysis tools used are susceptible to differences in data quality, invalid conclusions may be drawn about child development, at the individual level (the bad). We address the ugly problems in eye-tracking research that may not always be the primary interest when an experimental study is conceived, yet which need be solved to ensure that valid conclusions about development can be drawn
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