Optimal estimation from correlated, as opposed to uncorrelated, samples requires different strategies. Given the ubiquity of temporal correlations in the visual environment, if humans are to make decisions efficiently, they should exploit information about the correlational structure of sensory samples. We investigated whether participants were sensitive to the correlation structure of sequential visual samples and whether they could flexibly adapt to this structure in order to approach optimality in the estimation of summary statistics. In each trial, participants saw a sequence of ten dots presented at different locations on the screen which were either highly correlated (r = 0.7) or uncorrelated (in two separate blocks of 260 trials), and were asked to provide an estimate of the mean location of the dots. In the high correlation block, participants showed a trend towards overweighting the first and last samples of the sequence, in accordance with the optimal strategy given correlated data. In contrast, when exposed to uncorrelated inputs, the weights that participants assigned to each sample did not differ significantly from the optimal uniform allocation. Thus, it appears that humans are sensitive to the correlational structure of the data and can flexibly adapt to it so that their performance approximates optimality.