Compression in visual short-term memory: using statistical regularities to form more efficient memory representations
The information we can hold in working memory is quite limited, but this capacity has typically been studied 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. Here we show that observers can take advantage of these redundancies, enabling them to remember more items in working memory. In two experiments, we introduced covariance between colors in a display so that over trials some color pairs were more likely than other color pairs. Observers remembered more items from these displays than when 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. We therefore suggest that the underlying capacity of their working memory is unchanged, but the information they have to remember can be encoded in a more compressed fashion.