A Probabilistic Interpretation of Motion Correlation Selection Techniques

Abstract

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. CCS CONCEPTS• Human-centered computing → Interaction techniques; HCI design and evaluation methods; HCI theory, concepts and models.

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“…Individually tailoring thresholds to participants could further alleviate some issues, and Vergence Matching may be more sensitive to these parameters due to the exponential change in vergence angle as distance increases. These personalised optimisations and further improvements to the detection algorithm underpinning Vergence Matching (e.g., probabilistic frameworks [57]) could further reduce the selection times and error rates, making it a more viable alternative to select one among many targets.…”

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