The Developing Infant Creates a Curriculum for Statistical Learning

New efforts are using head cameras and eye-trackers worn by infants to capture everyday visual environments from the point of view of the infant learner. From this vantage point, the training sets for statistical learning develop as the sensorimotor abilities of the infant develop, yielding a series of ordered datasets for visual learning that differ in content and structure between timepoints but are highly selective at each timepoint. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains. Future advances in computational models will be necessary to connect the developmentally changing content and statistics of infant experience to the internal machinery that does the learning. The nature of the environment that supports learning is fundamental to understanding human cognition. Advances in wearable sensors are enabling research to study the everyday environments of infants at scale and with precision. Egocentric vision is an emerging field that uses head cameras and head-mounted eye trackers to study visual environments from the viewpoint of acting and moving perceivers. Studies of infant visual environments from the first-person view show that these environments change systematically with development, effectively creating a curriculum for learning. The structure of infant visual environments not only challenges current assumptions of statistical learning but can also inform computational models of statistical learning.