  Entropy-based measures are an important tool for studying human gaze behavior
under various conditions. In particular, gaze transition entropy (GTE) is a
popular method to quantify the predictability of fixation transitions. However,
GTE does not account for temporal dependencies beyond two consecutive fixations
and may thus underestimate a scanpath's actual predictability. Instead, we
propose to quantify scanpath predictability by estimating the active
information storage (AIS), which can account for dependencies spanning multiple
fixations. AIS is calculated as the mutual information between a processes'
multivariate past state and its next value. It is thus able to measure how much
information a sequence of past fixations provides about the next fixation,
hence covering a longer temporal horizon. Applying the proposed approach, we
were able to distinguish between induced observer states based on estimated
AIS, providing first evidence that AIS may be used in the inference of user
states to improve human-machine interaction.
