  Motion correlation interfaces are those that present targets moving in
different patterns, which the user can select by matching their motion. In this
paper, we re-formulate the task of target selection as a probabilistic
inference problem. We demonstrate that previous interaction techniques can be
modelled using a Bayesian approach and that how modelling the selection task as
transmission of information can help us make explicit the assumptions behind
similarity measures. We propose ways of incorporating uncertainty into the
decision-making process and demonstrate how the concept of entropy can
illuminate the measurement of the quality of a design. We apply these
techniques in a case study and suggest guidelines for future work.
