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Encoding Multielement Scenes: Statistical Learning of Visual Feature Hierarchies
 

Summary: Encoding Multielement Scenes: Statistical Learning of Visual
Feature Hierarchies
Jo´zsef Fiser and Richard N. Aslin
University of Rochester
The authors investigated how human adults encode and remember parts of multielement scenes com-
posed of recursively embedded visual shape combinations. The authors found that shape combinations
that are parts of larger configurations are less well remembered than shape combinations of the same kind
that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness
constraint enables the development of complex new features for acquiring internal representations
efficiently without being computationally intractable. The resulting representations also encode parts and
wholes by chunking the visual input into components according to the statistical coherence of their
constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers
a unified framework for investigating the formation of different internal representations in pattern and
scene perception.
Keywords: perceptual learning, implicit memory, scene perception, visual features, chunking
The purpose of the current work is to investigate how human
adults develop new visual representations to encode and recognize
both familiar and novel objects and scenes in situations in which
the visual features in the input are hierarchically structured. We
began by utilizing a statistical learning framework, in which two-

  

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