Brandeis University University of Rochester Brandeis University, USA Human infants are known to implicitly learn statistical regularities of their sensory environment in various perceptual domains. Visual statistical leaning studies with adults have illustrated that this learning is highly sophisticated and well approximated by optimal probabilistic chunking of the unfamiliar hierarchical input into statistically stable segments that can be interpreted as meaningful perceptual units. However, the emergence and use of such perceptual chunks at an early age and their relation to stimulus complexity have not been investigated. In three experiments, we found that 9-month-old infants can extract and encode statistical relationships within complex, hierarchically structured visual scenes, but they are not able to identify and handle these chunks as individual structures in the same manner as adults. These results suggest that as stimulus complexity increases, infantsʼ ability to extract chunks becomes limited, even though they are perfectly able to encode the structure of the scene. Apparently, the ability to use embedded features in more general and complex contexts emerges developmentally after encoding itself is operational.