  Network science has emerged as a powerful tool through which we can study the
higher-order architectural properties of the world around us. How human
learners exploit this information remains an essential question. Here, we focus
on the temporal constraints that govern such a process. Participants viewed a
continuous sequence of images generated by three distinct walks on a modular
network. Walks varied along two critical dimensions: their predictability and
the density with which they sampled from communities of images. Learners
exposed to walks that richly sampled from each community exhibited a sharp
increase in processing time upon entry into a new community. This effect was
eliminated in a highly regular walk that sampled exhaustively from images in
short, successive cycles (i.e., that increasingly minimized uncertainty about
the nature of upcoming stimuli). These results demonstrate that temporal
organization plays an essential role in how robustly knowledge of network
architecture is acquired.
