title: Statistical learning of higher-order temporal structure from visual shape sequences.
abstract: In 3 experiments, the authors investigated the ability of observers to extract the probabilities of successive shape co-occurrences during passive viewing. Participants became sensitive to several temporal-order statistics, both rapidly and with no overt task or explicit instructions. Sequences of shapes presented during familiarization were distinguished from novel sequences of familiar shapes, as well as from shape sequences that were seen during familiarization but less frequently than other shape sequences, demonstrating at least the extraction of joint probabilities of 2 consecutive shapes. When joint probabilities did not differ, another higher-order statistic (conditional probability) was automatically computed, thereby allowing participants to predict the temporal order of shapes. Results of a single-shape test documented that lower-order statistics were retained during the extraction of higher-order statistics. These results suggest that observers automatically extract multiple statistics of temporal events that are suitable for efficient associative learning of new temporal features.
authors.affiliation: Department of Brain and Cognitive Sciences and Center for Visual Science, University of Rochester, New York 14627-0268, USA.
authors.forename: József
authors.initials: J
authors.lastname: Fiser
authors.forename: Richard N
authors.initials: RN
authors.lastname: Aslin
mesh_terms: D000704:Analysis of Variance; D001288:Attention; D004193:Discrimination Learning; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D011939:Mental Recall; D009949:Orientation; D010364:Pattern Recognition, Visual / Q000502:physiology*; D013997:Time Factors