# A Probabilistic Interpretation of Motion Correlation Selection Techniques

arXiv: https://arxiv.org/abs/2102.03955 · HTML: https://mlsanity.com/paper/2102.03955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2021.

## Built on by (most cited first, 2 of 2)

- [Active Inference and Human--Computer Interaction](https://mlsanity.com/paper/2412.14741/) (2024)
- [From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization](https://mlsanity.com/paper/2603.22766/) (2026)

## Abstract (arXiv)

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.