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Bayesian learning of visual chunks by human observers

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Bayesian learning of visual chunks by human observers

Auteurs : Gerg Orbán [Hongrie, États-Unis] ; J Zsef Fiser [États-Unis] ; Richard N. Aslin [États-Unis] ; Máté Lengyel [Hongrie, Royaume-Uni]

Source :

Proceedings of the National Academy of Sciences of the United States of America [ 0027-8424 ] ; 2008.

RBID : PMC:2268207

Abstract

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 chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison 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 set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2268207

DOI: 10.1073/pnas.0708424105
PubMed: 18268353
PubMed Central: 2268207

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PMC:2268207

Le document en format XML

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<front><div type="abstract" xml:lang="en"><p>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 chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison 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 set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input.</p>
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Bayesian learning of visual chunks by human observers

Bayesian learning of visual chunks by human observers

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Abstract

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