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Bayesian learning of visual chunks by human observers
 

Summary: Bayesian learning of visual chunks
by human observers
Gergo Orba´n*
, Jo´ zsef Fiser
, Richard N. Aslin
, and Ma´te´ Lengyel*§¶
*Collegium Budapest Institute for Advanced Study, 2 Szentha´romsa´g utca, Budapest H-1014, Hungary; Department of Psychology and Volen Center for
Complex Systems, Brandeis University, 415 South Street, Waltham, MA 02454; Department of Brain and Cognitive Sciences, Center for Visual Science,
Meliora 406, University of Rochester, Rochester, NY 14627; §Gatsby Computational Neuroscience Unit, University College London, Alexandra House,
17 Queen Square, London WC1N 3AR, United Kingdom; and ¶Computational and Biological Learning Laboratory, Department of Engineering,
University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
Edited by James L. McClelland, Stanford University, Stanford, CA, and approved December 28, 2007 (received for review September 5, 2007)
Efficient and versatile processing of any hierarchically structured
information requires a learning mechanism that combines lower-
level features into higher-level chunks. We investigated this chunk-
ing mechanism in humans with a visual pattern-learning paradigm.
We developed an ideal learner based on Bayesian model compar-
ison that extracts and stores only those chunks of information that
are minimally sufficient to encode a set of visual scenes. Our ideal
Bayesian chunk learner not only reproduced the results of a large

  

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

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences