OWLET: An automated, open-source method for infant gaze tracking using smartphone and webcam recordings

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.