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Compression in Visual Working Memory: Using Statistical Regularities to Form More Efficient Memory Representations
 

Summary: Compression in Visual Working Memory: Using Statistical Regularities
to Form More Efficient Memory Representations
Timothy F. Brady and Talia Konkle
Massachusetts Institute of Technology
George A. Alvarez
Harvard University
The information that individuals can hold in working memory is quite limited, but researchers have
typically studied this capacity using simple objects or letter strings with no associations between them.
However, in the real world there are strong associations and regularities in the input. In an information
theoretic sense, regularities introduce redundancies that make the input more compressible. The current
study shows that observers can take advantage of these redundancies, enabling them to remember more
items in working memory. In 2 experiments, covariance was introduced between colors in a display so
that over trials some color pairs were more likely to appear than other color pairs. Observers remembered
more items from these displays than from displays where the colors were paired randomly. The improved
memory performance cannot be explained by simply guessing the high-probability color pair, suggesting
that observers formed more efficient representations to remember more items. Further, as observers
learned the regularities, their working memory performance improved in a way that is quantitatively
predicted by a Bayesian learning model and optimal encoding scheme. These results suggest that the
underlying capacity of the individuals' working memory is unchanged, but the information they have to
remember can be encoded in a more compressed fashion.

  

Source: Alvarez, George A. - Department of Psychology, Harvard University
Oliva, Aude - Department of Brain and Cognitive Science, Massachusetts Institute of Technology (MIT)

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences