Our knowledge of objects reflects the statistics of the visual environment. From our experiences in the world, we store information about categories of objects and the features that define them. One important statistical property of objects is the co-occurrence of their constituent features. For example, the round shape of an apple co-occurs frequently with the color red, but not the color blue. Here we examine the neural mechanisms that encode such feature co-occurrence statistics at the interface of perception and memory. In an fMRI experiment, subjects viewed images of colored objects while performing an unrelated scrambled-object detection task. The stimuli included exemplars from three different categories: apples, leaves, and roses. To create stimuli that sampled a range of co-occurrence statistics, each exemplar image had its color systematically manipulated to be red, pink, yellow, green, or blue (Fig.1A). We quantified co-occurrence frequencies of color-object combinations (e.g., “yellow apple”) in a large lexical corpus. A separate norming study demonstrated that this metric was strongly correlated with subjective ratings of color-object typicality. Importantly, the co-occurrence of object and color information is independent of the frequencies of each feature alone. We tested the hypothesis that feature co-occurrence information is encoded in semantic memory regions and automatically retrieved during object perception. Using representational similarity analysis, we identified regions where response patterns were similar for category exemplars with similar co-occurrence statistics (Fig.2B). We expected that the angular gyrus would encode combinatorial information given its proposed role in semantic integration. Indeed, we found that this region and the anterior fusiform cortex encode high-level feature co-occurrence statistics, while early visual cortex, lateral-occipital complex, and inferior-temporal cortex did not. These results suggest that regions at the interface of vision and semantic memory encode combinatorial information that underlies real-world knowledge of objects and is independent of coding for individual features. Meeting abstract presented at VSS 2015