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Statistical Learning of Higher-Order Temporal Structure From Visual Shape Sequences
 

Summary: Statistical Learning of Higher-Order Temporal Structure
From Visual Shape Sequences
Jo´zsef Fiser and Richard N. Aslin
University of Rochester
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, demon-
strating 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.
Our visual experience consists almost entirely of spatiotemporal
events created by observer movement through the visual array
(through eye, head, and body movements) and/or by independent
movements of objects with respect to the static environment.
Therefore, the computational task facing the visual system during

  

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

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences