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

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.