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Statistical learning of new visual feature combinations by infants
 

Summary: Statistical learning of new visual feature
combinations by infants
JoŽ zsef Fiser* and Richard N. Aslin
Center for Visual Science, Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627
Edited by James L. McClelland, Carnegie Mellon University, Pittsburgh, PA, and approved September 23, 2002 (received for review August 6, 2002)
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 represen-
tations 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 ele-
ments in the scenes than other pairs, but also to pairs that had
higher predictability (conditional probability) between the ele-
ments of the pair. These findings suggest that, similar to lower-
level visual representations, infants learn higher-order visual fea-
tures based on the statistical coherence of elements within the

  

Source: Aslin, Richard N. - Department of Brain and Cognitive Sciences, University of Rochester

 

Collections: Biology and Medicine