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

@article{citeulike:2159716, abstract = {Three experiments investigated the ability of human observers 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 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.}, author = {Fiser, Jozsef and Aslin, Richard N. }, citeulike-article-id = {2159716}, doi = {10.1111/1467-9280.00392}, journal = {Psychological Science}, keywords = {cross-situational, statistical-learning}, number = {6}, pages = {499--504}, posted-at = {2007-12-22 15:54:49}, priority = {0}, title = {Unsupervised Statistical Learning of Higher-Order Spatial Structures From Visual Scenes}, url = {http://www.blackwell-synergy.com/doi/abs/10.1111/1467-9280.00392}, volume = {12}, year = {2001} }

TY - JOUR ID - citeulike:2159716 L3 - citeulike-article-id:2159716 TI - Unsupervised Statistical Learning of Higher-Order Spatial Structures From Visual Scenes JF - Psychological Science VL - 12 IS - 6 SP - 499 EP - 504 N2 - Three experiments investigated the ability of human observers 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 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. KW - cross-situational KW - statistical-learning AU - Fiser, Jozsef AU - Aslin, Richard PY - 2001/// UR - http://www.blackwell-synergy.com/doi/abs/10.1111/1467-9280.00392 ER -