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Cited by 805

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Unsupervised Statistical Learning of Higher-Order Spatial Structures from Visual Scenes

Fiser, József, Aslin, Richard N.·2001·Psychological Science

Abstract

Three experiments investigated the ability of human observers to extract the joint and conditional probabilities of shape cooccurrences during passive viewing of complex visual scenes. Results indicated that statistical learning of shape conjunctions was both rapid and automatic, as subjects were not instructed to attend to any particular features of the displays. Moreover, in addition to single-shape frequency, subjects acquired in parallel several different higher-order aspects of the statistical structure of the displays, including absolute shape-position relations in an array, shape-pair arrangements independent of position, and conditional probabilities of shape co-occurrences. Unsupervised learning of these higher-order statistics provides support for Barlow's theory of visual recognition, which posits that detecting “suspicious coincidences” of elements during recognition is a necessary prerequisite for efficient learning of new visual features.

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Metadata

Authors
Fiser, J., & Aslin, R. N.
Venue
Psychological Science
Published
2001-11
Volume
12
Issue
6
Pages
499-504
ISSN
  • 0956-7976 (ISSN-L)
  • 1467-9280
OpenAlex
W2051215923
Cited by
805
References
4
FWCI
4.51
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Unsupervised Statistical Learning of Higher-Order Spatial Structures from Visual Scenes. https://app.cochat.ai/papers/10.1111/1467-9280.00392
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Fiser, J., & Aslin, R. N. (2001). Unsupervised Statistical Learning of Higher-Order Spatial Structures from Visual Scenes. Psychological Science. https://doi.org/10.1111/1467-9280.00392

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