Abstract

Abstract: 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. brief bio: Linda B. Smith, Distinguished Professor at Indiana University Bloomington, is an internationally recognized leader in cognitive science and cognitive development. Taking a complex systems perspective, she seeks to understand the interdependencies among perceptual, motor and cognitive developments during the first three years of post-natal life. Using wearable sensors, including head-mounted cameras, she studies how the young learner’s own behavior creates learning experiences. The work has led to novel insights currently being extended through collaborations to robotics and artificial intelligence. She received her PhD from the University of Pennsylvania in 1977 and immediately joined the faculty at Indiana University. She won the David E. Rumelhart Prize for theoretical contributions to cognitive science and is an elected member of both the National Academy of Sciences and the American Academy of Arts and Science.