A central task of the visual system is to take the information available in the retinal image and compress it to form a more efficient representation of the world. Such compression requires sensitivity to the statistical distribution that stimuli are drawn from, in order to detect redundancy and eliminate it (Shannon, 1948). But how sensitive are people to complicated and abstract statistical regularities? And how robustly do we use these regularities to form compressed representations?

In the first part of the talk I will discuss work on statistical learning mechanisms (e.g., Saffran et al. 1996) that demonstrates how people track the distribution of images over time in the real world. I'll present several experiments that use sequences of natural images to demonstrate that such statistical learning mechanisms operate at multiple levels of abstraction, including the level of semantic categories. I'll discuss how learning at this abstract level allows us to minimize redundancy by not relearning the same regularities over and over again.

In the second part of the talk I will suggest another potential benefit to such statistical learning mechanisms the ability to remember more items in visual short-term memory (VSTM). To demonstrate how observers can take advantage of learned regularities for the purpose of compression, I'll present several experiments where we show that observers can take advantage of relationships between colors in VSTM displays to eliminate redundant information and form more efficient representations. I'll then present a model of this data using Huffman coding, a compression algorithm, to demonstrate that quantifying VSTM in terms of the bits of information remembered is more accurate than measuring the number of objects remembered (the most common metric).