Department of Population Health, New York University School of Medicine, 227 E 30th St, 7th Fl, New York, NY, 10016, USA.
Department of Child & Adolescent Psychiatry, New York University School of Medicine, New York, NY, 10016, USA.
Behav Res Methods. 2023 Sep;55(6):3149-3163. doi: 10.3758/s13428-022-01962-w. Epub 2022 Sep 7.
Groundbreaking insights into the origins of the human mind have been garnered through the study of eye movements in preverbal subjects who are unable to explain their thought processes. Developmental research has largely relied on in-lab testing with trained experimenters. This constraint provides a narrow window into infant cognition and impedes large-scale data collection in families from diverse socioeconomic, geographic, and cultural backgrounds. Here we introduce a new open-source methodology for automatically analyzing infant eye-tracking data collected on personal devices in the home. Using algorithms from computer vision, machine learning, and ecological psychology, we develop an online webcam-linked eye tracker (OWLET) that provides robust estimation of infants' point of gaze from smartphone and webcam recordings of infant assessments in the home. We validate OWLET in a large sample of 7-month-old infants (N = 127) tested remotely, using an established visual attention task. We show that this new method reliably estimates infants' point-of-gaze across a variety of contexts, including testing on both computers and mobile devices, and exhibits excellent external validity with parental-report measures of attention. Our platform fills a significant gap in current tools available for rapid online data collection and large-scale assessments of cognitive processes in infants. Remote assessment addresses the need for greater diversity and accessibility in human studies and may support the ecological validity of behavioral experiments. This constitutes a critical and timely advance in a core domain of developmental research and in psychological science more broadly.
通过研究无法解释自己思维过程的言语前受试者的眼球运动，人们对人类思维的起源有了突破性的认识。发展研究在很大程度上依赖于经过训练的实验员在实验室中的测试。这种限制为婴儿认知提供了一个狭隘的视角，并阻碍了来自不同社会经济、地理和文化背景的家庭进行大规模数据收集。在这里，我们引入了一种新的开源方法，用于自动分析在家用个人设备上收集的婴儿眼动追踪数据。我们使用计算机视觉、机器学习和生态心理学中的算法，开发了一种在线网络摄像头链接眼动追踪器（OWLET），该追踪器可以从智能手机和网络摄像头记录的家庭婴儿评估中，对婴儿的注视点进行稳健估计。我们在一个远程测试的 7 个月大婴儿的大样本中（N=127）验证了 OWLET，使用了一个已建立的视觉注意力任务。我们表明，这种新方法可以在各种环境中可靠地估计婴儿的注视点，包括在计算机和移动设备上进行测试，并且与注意力的父母报告测量具有极好的外部有效性。我们的平台填补了当前用于快速在线数据收集和大规模评估婴儿认知过程的工具的重大空白。远程评估满足了人类研究中更大多样性和可及性的需求，并可能支持行为实验的生态有效性。这是发展研究和更广泛的心理科学核心领域的一个关键且及时的进展。
