title: Statistical learning of new visual feature combinations by infants.
abstract: The ability of humans to recognize a nearly unlimited number of unique visual objects must be based on a robust and efficient learning mechanism that extracts complex visual features from the environment. To determine whether statistically optimal representations of scenes are formed during early development, we used a habituation paradigm with 9-month-old infants and found that, by mere observation of multielement scenes, they become sensitive to the underlying statistical structure of those scenes. After exposure to a large number of scenes, infants paid more attention not only to element pairs that cooccurred more often as embedded elements in the scenes than other pairs, but also to pairs that had higher predictability (conditional probability) between the elements of the pair. These findings suggest that, similar to lower-level visual representations, infants learn higher-order visual features based on the statistical coherence of elements within the scenes, thereby allowing them to develop an efficient representation for further associative learning.
authors.affiliation: Center for Visual Science, Department of Brain and Cognitive Sciences, University of Rochester, NY 14627, USA. fiser@bcs.rochester.edu
authors.forename: József
authors.initials: J
authors.lastname: Fiser
authors.forename: Richard N
authors.initials: RN
authors.lastname: Aslin
mesh_terms: D001245:Association Learning; D006185:Habituation, Psychophysiologic; D006801:Humans; D007223:Infant; D010364:Pattern Recognition, Visual; D011338:Probability Theory; D002666:Psychology, Child