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Eye movement patterns contain important information about the underlying perceptual and cognitive mechanisms. Traditional area of interest (AOI) measures such as fixation count, fixation duration, scan-path length, and spatial density ignore either the spatial or the temporal aspects of fixation sequences. The transition-probability matrix contains both spatial and sequential information, but it only quantifies pairs of transitions and ignores properties of temporally-extended sequences. Here we describe a new method that captures the statistical regularities in longer sequences using successor representations (SRs, Dayan, 1993, Neural Computation). Whereas, each cell of a traditional transition matrix represents the frequency of making a single saccade from one AOI to another, the SR uses temporal difference learning to incrementally strengthen the weights of multiple cells based on both recent and predicted …