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PSYCHOLOGICAL SCIENCE Research Article
 

Summary: PSYCHOLOGICAL SCIENCE
Research Article
VOL. 12, NO. 6, NOVEMBER 2001 Copyright © 2001 American Psychological Society 499
UNSUPERVISED STATISTICAL LEARNING OF HIGHER-ORDER
SPATIAL STRUCTURES FROM VISUAL SCENES
József Fiser and Richard N. Aslin
Department of Brain and Cognitive Sciences and Center for Visual Science, University of Rochester
Abstract--Three experiments investigated the ability of human ob-
servers to extract the joint and conditional probabilities of shape co-
occurrences 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 inde-
pendent of position, and conditional probabilities of shape co-occur-
rences. 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

  

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

 

Collections: Biology and Medicine