Indiana University Bloomington
"Learning from the infant’s point of view"
Learning depends on both the learning mechanism and the training material. This talk considers the natural statistics of infant visual experience. These natural training sets for human visual object recognition challenge usual assumptions about how we think about learning. These visual experiences are created in real time by infants’ own behaviors. They change systematically as infants’ bodies and behavior changes. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order – with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. The skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines.